KR102343167B1 - Location detection in the network - Google Patents

Abstract

A system and method for detecting the presence of a body in a network without fiducial elements using signal absorption caused by the presence of biological masses in a communication network and signal forward and backscatter of radio frequency (RF) propagation.

Description

Location detection within the network

CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation of U.S. Utility Patent Application No. 15/713,309, filed on September 22, 2017, and a continuation of U.S. Utility Patent Application No. 15/713,219, filed on September 22, 2017; It is a continuation application of U.S. Utility Patent Application No. 15/674,487, filed on August 10, 2017; and U.S. Utility Application No. 15/674,328, filed on August 10, 2017. Application 15/713,219 is a continuation-in-part of U.S. Utility Application No. 15/674,328, filed on August 10, 2017, which is a continuation-in-part of U.S. Utility Application No. 15/600,380, filed May 19, 2017, which It is a continuation of U.S. Utility Patent Application No. 15/227,717, filed on August 3, 2016, which is a continuation of Provisional Patent Application No. 62/262,954, filed November 9, 2015, and U.S., filed September 16, 2015 Claims the benefit of Provisional Patent Application No. 62/219,457. Application 15/713,309 is a continuation of U.S. Utility Patent Application No. 15/674,487, filed on August 10, 2017, which is a continuation of U.S. Utility Patent Application No. 15/674,328, filed on August 10, 2017, which It is a continuation-in-part of U.S. Utility Application No. 15/600,380, filed May 19, 2017, which is a continuation-in-part of U.S. Utility Application No. 15/227,717, filed August 3, 2016, which was filed on November 9, 2015 Claims the benefit of U.S. Provisional Patent Application No. 62/252,954, filed on September 16, 2015 and U.S. Provisional Patent Application No. 62/219,457, filed on September 16, 2015. Application 15/227,717 was filed March 29, 2016 and filed for U.S. Utility It is a continuation of U.S. Utility Patent Application No. 15/084,002, published on October 18, 2016 as Patent No. 9,474,042, which in turn are also filed on November 9, 2015 in U.S. Provisional Patent Application Nos. 62/252,954 and 2015 Claims the benefit of U.S. Provisional Patent Application No. 62/219,457, filed September 16, . The entire disclosure of these documents is incorporated herein by reference.
field of invention
The present disclosure relates to the field of object detection, and more particularly, to a system and method for detecting the presence of a biological mass in a wireless communication network.

Object tracking can be performed using a number of techniques. For example, a mobile transceiver may be attached to an object. Examples of such systems include global positioning systems, such as GPS, which use orbiting satellites to communicate with terrestrial transceivers. However, these systems are generally ineffective indoors, where satellite signals are blocked and accuracy is poor. Therefore, other technologies such as roaming or Bluetooth(TM) beacons that calculate the location of an unknown transceiver are often used indoors. The roaming transceiver serves as a fiducial element.
These systems have several drawbacks, among which the tracked object must include a transceiver. In certain applications, the object to be tracked does not have such a fiducial element or will actively disable any such element, such as a home intruder.
There are other techniques that can detect and track objects without the use of fiducial elements. For example, radar is a useful object detection system that uses RF waves to determine the range, angle or velocity of objects, including aircraft, ships, spacecraft, guided missiles, automobiles, meteorological formations, and terrain. Radars generally operate by transmitting electromagnetic waves using waves in the radio frequency (“RF”) of the electromagnetic spectrum that are reflected from any object in their path. A receiver, typically part of the same system as the transmitter, receives and processes these reflected waves to determine the properties of the object. Other systems similar to radar using other parts of the electromagnetic spectrum may also be used in a similar manner, such as ultraviolet, visible or near-infrared light from lasers.
Radar technology does not require a fiducial element, but has other drawbacks. For example, radar signals are susceptible to signal noise, or random fluctuations in the signal caused by internal electrical components, as well as noise and interference from external sources such as natural background radiation. Radar is also vulnerable to external sources of interference, such as inclusions blocking the beam path, and can be deceived by objects of a certain size, shape and orientation.

The following is a summary of the invention in order to provide a basic understanding of some aspects of the invention. This Summary is not intended to identify key or critical elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some concepts of the invention in a simplified form as a prelude to the more detailed description that is presented later.
Because of these and other problems in the art, in this application, among other things, a method of detecting the presence of a human body is described, the method comprising: providing a first transceiver disposed at a first location within a detection area; providing a second transceiver disposed at a second location within the detection area; a computer server communicatively coupled to the first transceiver; the first transceiver receives a first set of wireless signals from the second transceiver via the wireless communication network; the computer server receives a first set of signal data from the first transceiver, the first set of signal data comprising data relating to a characteristic of the first set of wireless signals, the characteristic data comprising: the communication network generated as part of the normal operation of the first transceiver on the computer server generates a baseline signal profile for communication from the second transceiver to the first transceiver, wherein the baseline signal profile is based at least in part on a characteristic of a wireless signal in the first set of signal data; ; characterizing wireless transmission from the second transceiver to the first transceiver when no human body is present in the detection area; the first transceiver receives a second set of wireless signals from the second transceiver via the wireless communication network; the computer server receives a second set of signal data from the first transceiver, the second set of signal data comprising data relating to a characteristic of the second set of wireless signals, the characteristic data comprising: generated as part of the normal operation of the first transceiver on and the computer server determines whether a human body is present within the detection area, wherein the determination is based, at least in part, on comparing a radio signal characteristic in the received second set of radio signal data to the baseline signal profile.
In an embodiment of the method, the first set of signal characteristics comprises a wireless network signal protocol characteristic determined by the first transceiver.
In another embodiment of the method, the wireless network signal protocol characteristic is selected from the group consisting of received signal strength, latency, and bit error rate.
In another embodiment of the method, the method comprises; providing a third transceiver disposed at a third location within the detection area; the first transceiver receives a third set of wireless signals from the third transceiver via the wireless communication network; the computer server receives a third set of signal data from the first transceiver, the third set of signal data comprising data relating to a characteristic of the third set of wireless signals, the characteristic data comprising: the communication network generated as part of the normal operation of the first transceiver on The computer server generates a second baseline signal profile for communication from the third transceiver to the first transceiver, wherein the second baseline signal profile is at least dependent on a characteristic of a wireless signal in the third set of signal data. partially based; and characterizing the wireless transmission from the third transceiver to the first transceiver when no human body is present in the detection area. the first transceiver receives a fourth set of wireless signals from the third transceiver via the wireless communication network; the computer server receives a fourth set of signal data from the first transceiver, wherein the fourth set of signal data includes data relating to a characteristic of the fourth set of wireless signals, the characteristic data comprising: the communication network generated as part of the normal operation of the first transceiver on and the computer server is based, at least in part, on comparing a radio signal characteristic in the received fourth set of radio signal data to the second baseline signal profile in the determining step of determining whether a human body is present within the detection area. do.
In another embodiment of the method, the determining step applies a statistical method to the second set of radio signal data to determine the presence of the human body.
In another embodiment of the method, the method includes the computer server continuously determining the presence or absence of a human body within a detection area, wherein the determination is determined based on the baseline signal profile and continuously received at the computer server from the first transceiver. based at least in part on a comparison with signal data comprising data relating to characteristics of the first set of wireless signals; and the computer continuously updating the baseline signal profile based on the continuously received signal data when the continuously received signal data indicates that there is no human body in the detection area.
In another embodiment of the method, the method further comprises: the computer server determining whether the number of anatomy is within a detection area, wherein the determining is based on comparison of the baseline signal profile with the received second set of signal characteristics. based at least in part.
In another embodiment of the method, the method further comprises: the computer server determining a location of the one or more anatomy within the detection area, wherein the determining comprises at least a comparison of a baseline signal to a characteristic of the second set of received signals. partly based on
In another embodiment of the method, the method further comprises a computer server operatively coupled to the second system, wherein the computer operates the second system only after the computer server detects the presence of the human body in the detection area. .
In another embodiment of the method, the detection network and the second system are configured to communicate using the same communication protocol.
In another embodiment of the method, the second system is an electrical system.
In another embodiment of the method, the second system is a lighting system.
In another embodiment of the method, the second system is a heating, exhaust and cooling (HVAC) system.
In another embodiment of the method, the second system is a security system.
In another embodiment of the method, the second system is an industrial automation system.
In another embodiment of the method, the wireless communication protocol is selected from the group consisting of Bluetooth(TM), Bluetooth(TM) Low Energy, ANT, ANT+, WiFi, ZigBee, Thread, and Z-Wave.
In another embodiment of the method, the wireless communication network has a carrier frequency in a range encompassing 850 MHz and 17.5 GHz.
In another embodiment of the method, the determining whether a human body is present within a detection area comprises determining a first sample location of the anatomy having a fiducial element in the detection area, wherein the first sample location is the fiducial element determining the first sample location, which is determined based on detection of an element; determining a second sample location of the anatomy in the detection area, wherein the second sample location is at least partially in a comparison of the received second set of signal data and the baseline signal profile without using the fiducial element determining the second sample position, which is determined based on ; comparing the first sample location and the second sample location; and adjusting a determining step based on a non-fiducial element position to improve a position calculating capability of the system, wherein the adjusting is based on the comparing step. adjusted based on learning.
In another embodiment of the method, the determining whether a human body is present in the detection area comprises determining that a human body is present in the area based on a user input or action, wherein the sample signal characteristic is at least partially with a baseline signal characteristic of the empty space. , at least partially modifying the baseline signal characteristic for the empty space; modifying at least partially a signal characteristic associated with the occupancy; and adjusting a method of comparing sample signal characteristics to baseline and other comparison signal characteristics to improve accuracy of the system over time.
In embodiments of the present system, a user input or action providing presence data is provided directly to the system in any shape including, but not limited to, a physical switch, smart phone input, or an audible cue.
In embodiments of the present system, a user input or action providing presence data may, for example, intentionally change the signal profile to correspond to a decision made by the system, such as providing such a change during a dimming phase in a lighting system. provided indirectly to the system in some form, such as
In another embodiment of the method, the method further comprises: the computer server storing a plurality of historical data records indicating whether the human body has been present in the detection area for a certain period of time, each historical data record being detected in the detected area an indication of the number of the human body and the date and time when the number of the human body was detected in the detection area; and the computer server makes the historical data record available to one or more external computer systems via the interface.
Among other things, this application also describes a method of detecting the presence of a human body, the method comprising: providing a first transceiver disposed at a first location within a detection area; providing a second transceiver disposed at a second location within the detection area; a computer server communicatively coupled to the first transceiver; providing a first external system operatively coupled with the computer server; providing a second external system operatively coupled with the computer server; wherein the computer server generates a baseline signal data set comprising characteristic data relating to signal characteristics of a first set of radio signals received by the first transceiver from the second transceiver when no human body is present within the detection area. receive from a first transceiver, wherein the characteristic data is generated as part of normal operation of the first transceiver on the communication network; the computer server generates a baseline signal profile for communication from the second transceiver to the first transceiver when a human body is not present in the detection area, wherein the baseline signal profile is configured such that a human body is not present in the detection area based at least in part on characteristic data indicative of a characteristic of a wireless transmission from the second transceiver to the first transceiver when not in use; The computer server receives from the second transceiver a first set of baseline signal data comprising characteristic data relating to signal characteristics of a second set of wireless signals received by the first transceiver when a human body is present within the detection area. receive from the first transceiver, wherein the characteristic data is generated as part of normal operation of the first transceiver on the communication network; The computer server generates a first sample baseline signal profile for communication from the second transceiver to the first transceiver when a human body is present in the detection area, wherein the first sample baseline signal profile is in the detection area based at least in part on characteristic data in the first set of sample baseline signal data representative of a characteristic of a wireless transmission from the second transceiver to the first transceiver when a human body is present; The computer server receives a second set of baseline signal data comprising characteristic data relating to signal characteristics of a third set of radio signals received by the first transceiver from the second transceiver when a human body is present within the detection area. receive from the first transceiver, wherein the characteristic data is generated as part of normal operation of the first transceiver on the communication network; The computer server generates a second sample baseline signal profile for communication from the second transceiver to the first transceiver when a human body is present in the detection area, wherein the second sample baseline signal profile is in the detection area based at least in part on characteristic data in the second set of sample baseline signal data indicative of a characteristic of a wireless transmission from the second transceiver to the first transceiver when a human body is present; The computer server is configured to receive a third set of baseline signal data comprising characteristic data relating to signal characteristics of a fourth set of radio signals received by the first transceiver from the second transceiver when a human body is present within the detection area. receive from the first transceiver, wherein the characteristic data is generated as part of normal operation of the first transceiver on the communication network; based on the computer server determining that the characteristic data of the third set of sample baseline signal data corresponds to the first sample baseline signal profile, the computer server determines to operate the first external system; determining, by the computer server not to operate the second external system, based on the computer server determining that the characteristic data of the third set of sample baseline signal data corresponds to the second sample baseline signal profile; include
In an embodiment of the method, the determining to operate the first external system and the determining not to operate the second external system comprises: determining a first sample location of the human body having a fiducial element in the detection area; , determining the first sample location, wherein the first sample location is determined based on detection of the fiducial element; determining a second sample location of the anatomy in the detection area, wherein the second sample location is at least partially in a comparison of the received second set of signal data and the baseline signal profile without using the fiducial element determining the second sample position, which is determined based on ; comparing the first sample location and the second sample location; and adjusting the determining step based on the non-fiducial element position to improve the position calculating capability of the system, wherein the adjusting is based on the comparing step. It is tuned based on machine learning.
In another embodiment of the method, the determining to operate the first external system and the determining not to operate the second external system comprises using inference to determine a first sample location of the anatomy in a detection area, wherein the first determining the first sample location, wherein the sample location is determined based on detecting a human body interacting with the system in some known manner; determining a second sample location of the anatomy in the detection area, the second sample location being at least for comparison of the received second set of signal data and the baseline signal profile without using the estimated location determining the second sample location, determined based in part; comparing the first sample location and the second sample location; and adjusting a determining step based on the inferred position to improve a position calculating capability of the system, wherein the adjusting is based on the comparing step.
In another embodiment of the method, the characteristic data pertaining to the wireless signal includes data pertaining to a signal characteristic selected from the group consisting of received signal strength, latency, and bit error rate.
In another embodiment of the method, the computer server generates a first sample baseline signal profile by applying a statistical method to the first set of sample baseline signal data, and the computer server generates the first sample baseline signal profile on the second set of sample baseline signal data. A second sample baseline signal profile is generated by applying a statistical method.
In another embodiment of the method, the computer server further sets a baseline signal comprising characteristic data relating to a signal characteristic of a wireless signal of a second ™V received by the first transceiver from the second transceiver. receiving data from the first transceiver, the characteristic data being generated by the first transceiver as part of normal operation of the first transceiver on a communication network, the computer server continuously receiving a set of baseline signal data when indicates that there is no human body in the detection area, continuously updating the baseline signal profile based on the additional set of baseline signal data received.
In another embodiment of the method, the computer server provides characteristic data regarding signal characteristics of a second set of wireless signals received by the first transceiver from the second transceiver when one or more human bodies are present within the detection area. receive from the first transceiver a set of signal data comprising The computer server further comprises determining a quantity of humans present in the detection area based at least in part on the comparison of the signal data set and the baseline signal profile.
In another embodiment of the method, the method further comprises the computer server determining a location of each of the one or more human bodies present in the detection area, wherein the determining comprises at least a comparison of the baseline signal profile with the set of signal data. partly based on
In another embodiment of the method, when the human body is present in the detection area, the computer server determines that the human body is present in the detection area, and the characteristic data of the third set of sample baseline signal data corresponds to the second sample baseline signal profile Even if it does, the first external system is operated.
In another embodiment of the method, when the human body is present in the detection area, the computer server determines that the human body is present in the detection area, and the characteristic data of the third set of sample baseline signal data corresponds to the second sample baseline signal profile The second external system is operated only when
In another embodiment of the method, the wireless communication network has a carrier frequency in a range encompassing 850 MHz and 17.5 GHz.
In another embodiment of the method, the computer server stores a plurality of historical data records indicating whether a human body has been present in a detection area for a certain period of time, and each of the historical data records is a record of the human body detected in the detection area. each of the number and the number of the human body includes an indication of the date and time detected in the detection area; and the computer server making the historical data record available to one or more external computer systems via an interface.

1 is a schematic diagram of an embodiment of a system according to the present disclosure;
2 is a flowchart of an embodiment of a method according to the present disclosure;
3A shows a schematic diagram of a system for change detection in a detection network over time according to the present disclosure;
3B shows a schematic diagram of a system for detecting a change in position of a human body in a detection network over time according to the present disclosure;

The following detailed description and disclosure are illustrated by way of example and not limitation. This description will enable any person skilled in the art to make and use the disclosed systems and methods, and will describe several embodiments, adaptations, variations, alternatives, and uses of the disclosed systems and methods. Since various changes may be made in the above configuration without departing from the scope of the present disclosure, all matters included in the description or shown in the accompanying drawings should be construed in an illustrative rather than a restrictive sense.
Generally speaking, described in this application are systems and methods for detecting the presence of a body in a network without fiducial elements. In general, the systems and methods described in this application use signal absorption, and signal forward and reflected backscatter, of RF communications caused by the presence of biological masses in communication networks, typically mesh networks. .
Throughout this disclosure, the term “computer” describes hardware that implements the functions provided by digital computing technologies in general, particularly those associated with microprocessors. The term "computer" is not limited to any particular type of computing device, but is intended to include all computer devices including, but not limited to: processing devices, microprocessors, personal computers, desktop computers, laptop computers, workstations. Stations, terminals, servers, clients, portable computers, handheld computers, smartphones, tablet computers, mobile devices, server farms, hardware appliances, minicomputers, mainframe computers, video game consoles, handheld video game products , and wearable computing devices including but not limited to eyewear, wrist-worn, pendants, and clip-on devices.
As used herein, "computer" is an abstraction of the functionality provided by a single computer device with the typical hardware and accessories of a computer performing a specific role. By way of example and not limitation, the term "computer" in the context of a laptop computer will be understood by those skilled in the art to include functionality provided by a pointer-based input device such as a mouse or trackpad, whereas enterprise-class servers and As used herein, the term "computer" will be understood by those skilled in the art to include functions provided by redundant systems such as RAID drives and dual power supplies.
It is also well known to those skilled in the art that the functionality of a single computer may be distributed across a number of separate machines. This distribution may function where specific machines perform specific tasks; Alternatively, a balance is achieved if each machine can perform most or all of the functions of any other machine and tasks are assigned based on the resources available at any given point in time. Accordingly, as used herein, the term “computer” refers to a single, single-use, single-use computer including, but not limited to, a networked server farm, “cloud” computing system, software-as-a-service, or other distributed or collaborative computer network. It may refer to a standalone self-contained device or a plurality of machines operating together or independently.
Those of skill in the art will also recognize that some devices that are not typically considered a "computer" exhibit characteristics of a "computer" in certain circumstances. Where such a device performs the functions of a "computer" described herein, the term "computer" includes such device to the extent that it is intended. Devices of this type include, but are not limited to, network hardware, print servers, file servers, NAS and SANs, load balancers, and any other hardware capable of interacting with the systems and methods described herein in the context of a conventional “computer”. It is not limited."
Throughout this disclosure, the term “software” may be executed by a computer processor, or by a computer processor, including but not limited to a virtual processor, or by use of a runtime environment, virtual machine and/or interpreter. code objects, program logic, instruction structures, data structures and definitions, source code, executable and/or binary files, machine code, object code, compiled libraries, implementations, algorithms, libraries or any that can be converted into any form Refers to an instruction or set of instructions. A person skilled in the art recognizes that software is not limited to microchip, but may be wired or embedded in hardware, and is still considered "software" within the meaning of this disclosure. For the purposes of this disclosure, software includes, without limitation: RAM, ROM, flash memory BIOS, CMOS, motherboard and daughter board circuits, hardware controllers, USB controllers or hosts, peripherals and controllers, video cards, audio controllers. , network cards, Bluetooth and other wireless communication devices, virtual memory, storage devices and associated controllers, firmware, and instructions that may be stored in device drivers. The systems and methods described in this application are typically contemplated using a computer and computer software stored on a computer or machine readable storage medium or memory.
Throughout this disclosure, terms used in this application to describe or refer to media bearing software, including but not limited to terms such as "medium,""storagemedium," and "memory," include Media may be included or excluded.
Throughout this disclosure, the term “network” generally refers to a voice, data or other telecommunication network in which computers communicate with each other. The term "server" generally refers to a computer that provides a service over a network, and "client" refers generally to a computer that accesses or uses a service provided by a server over a network. Those skilled in the art will understand that the terms "server" and "client" may refer to hardware, software, or a combination of hardware and software, depending on the context. Those skilled in the art will further understand that the terms "server" and "client" may refer to an endpoint of a network communication or network connection, including but not necessarily limited to a network socket connection. Those skilled in the art will further appreciate that a “server” may include a plurality of software and/or hardware servers that deliver a service or set of services. One of ordinary skill in the art would appreciate that the term "host", in its noun form, may refer to a network communication or endpoint of a network (eg, "remote host"), or, in verb form, a server that provides a service over the network ("website hosting"). ) or an access point of a service over a network.
Throughout this disclosure, the term “real time” refers to software that is operated within an operational deadline for a given event to be initiated or completed or for a given module, software, or system to respond, generally responding or Performance time is within normal user perception, and in fact, given the technical context, it is generally referred to as occurring concurrently with the reference event. One of ordinary skill in the art would appreciate that "real-time" does not literally mean that the system processes and/or responds immediately to input, but allows the system to process quickly enough that the processing or response time is within the normal human perception of real-time delivery in the operating environment of the program. and/or respond. A person skilled in the art understands that when the operating environment is a graphical user interface, "real time" generally means a response time in real time of one second or less, with milliseconds or microseconds being preferred. However, those skilled in the art understand that in other operating environments, systems operating in “real time” may exhibit delays greater than one second, particularly when network activity is involved.
Throughout this disclosure, the term “transmitter” refers to an equipment or set of equipment having hardware, circuitry, and/or software for generating and transmitting electromagnetic waves that carry messages, signals, data, or other information. The transmitter may also include a component that receives electrical signals including such messages, signals, data or other information and converts them into electromagnetic waves. The term "receiver" means an equipment or set of equipment having hardware, circuitry and/or software that receives such transmitted electromagnetic waves and converts them into generally electrical signals from which messages, signals, data or other information can be extracted . The term “transceiver” generally refers to a device or system that includes both a transmitter and a receiver, such as a two-way radio, or wireless networking router or access point. For the purposes of this disclosure, all three terms are to be understood as interchangeable unless otherwise indicated; For example, the term “transmitter” should be understood to mean the presence of a receiver, and the term “receiver” should be understood to mean the presence of a transmitter.
Throughout this disclosure, the term “detection network” refers to a wireless network used in the systems and methods of this disclosure to detect the presence of intervening biological masses within a communication area of the network. The detection network may use common networking protocols and standards, and is not necessarily a specific purpose network. That is, although nodes in a network may be deployed for the specific purpose of establishing a radio detection network in accordance with the present invention, this need not and generally will not be the case. Normal wireless networks established for other purposes may be used to implement the systems and methods described herein. In a preferred embodiment, the detection network uses a plurality of Bluetooth(TM) low energy nodes, although the present disclosure is not limited to these nodes. Each node acts as a computer with appropriate transmitters and receivers for communicating over the network. Each time each computer sends a message, it provides a unique identifier within the network that the receiving computer can identify from where the message originated. Such message origination information will generally be important to the functioning of the invention described in the Detailed Description. The receiving computer then analyzes the incoming signal properties, including signal strength, bit error rate, and message delay. The detection network may be a mesh network, which means a network topology in which each node relays data from the network.
Throughout this disclosure, the term “node” refers to the start point or end point of a device that has a network communication, typically a wireless transceiver, and is part of a detection network. A node is typically a standalone, self-contained networking device such as a wireless router, wireless access point, short-range beacon, or the like. A node may be a general purpose device or a special purpose device configured for use in a detection network as described herein. By way of example, and not limitation, a node is a device having the wireless transmission capability of an off-the-shelf wireless networking device by adding specialized hardware, circuitry, components, or programming for implementing the systems and methods described herein. can; That is, it detects significant changes to signal properties including, but not limited to, signal strength, bit error rate, and message delay. Within the detection network, each node can act as a transmitter of signals to the network as well as a receiver for other nodes to push information. In a preferred embodiment, the node uses Bluetooth Low Energy (BLE) as the wireless networking system.
Throughout this disclosure, the term “continuous” refers to those events occurring on an ongoing basis over time, whether mathematically continuous or discontinuous. The generally accepted mathematical definition of "continuous function" describes a function that has no holes or jumps, usually described by a two-sided limit. The technique described in this application is based on disturbances to a telecommunication system, where the transceiver transmits at discrete intervals and the received raw data is taken discretely, ie at discrete time intervals. The resulting data itself can be discrete in the sense that it captures the characteristics of the system over a specific observation window (ie, a time interval). In a physical or mathematical sense, this mechanism is essentially a set of discrete data points implying a discrete function. However, in the context of technology, one skilled in the art will understand that a system exhibiting this type of behavior is "continuous" if these measurements are taken on a going basis over time.
The measurable energy density signature of the RF signal is affected by the environmental absorber and reflector. Many biological masses, such as the human body, are mostly water and serve as important energy absorbers. Other properties of the human body, such as clothing, jewellery, internal organs, etc., all further influence measurable RF energy density. This is especially true when RF communication devices transmit over relatively short distances (eg less than 50 meters) such as Bluetooth, WiFi, 802.15.4 (Zigbee, Thread) and Z-Wave transceivers. A human body passing through the physical space of the network can cause signal absorption and disruption. Due to the relative uniformity of size, density and mass composition, the human body can cause characteristic signal absorption, scattering, and measurable reflections. Changes in signal behavior and/or properties are generally referred to herein as “Artifacts”. This phenomenon is particularly useful in the ISM (Industrial, Scientific, and Medical) band of the RF spectrum, but can generally be observed in a band beyond the range.
In RF communication systems comprising spatially separated transmitters and receivers, a signal received by a receiver from a given transmitter consists of energy from the original transmitted message arriving at the receiver. In general, objects in the transmission path affect the characteristics of the final signal that arrives at the receiver.
Communication systems are generally designed to deal with these issues and also to faithfully reproduce the messages from the transmitter. Since the human body is generally present, as long as RF communication is affected, as a large amount of water, one of the observable differences between the human body presence and absence in the detection network is signal absorption by the human body. In general, the closer you are to the transmitter or receiver, the more important the absorption may be.
In general, the human body is expected to generate artifacts in the detection network in a more or less predictable manner that can be detected or identified programmatically by a detection algorithm. Additionally, artifacts may be cross-correlated across the network to determine the estimated location of the object causing the artifact. The accuracy of this estimate may vary depending on the algorithm chosen/configured and the equipment used in the individual system.
For each given algorithm selected/configured, the system may build a detection, such as a combination of a baseline signal profile with no human body present in the detection area and a baseline signal data data with a human body present in the detection area. The new incoming sample baseline signal data is compared to known sample baseline signal data and baseline signal profile to determine the presence or absence of a human body in space.
Short-range low-power communication networks typically operate using signals in the 2.4 GHz frequency band, which is noteworthy within the observed energy frequencies absorbed by the human body. As indicated, a human body physically interposed in the detection network absorbs and/or reflects at least some of the signals transmitted between and among the nodes. However, other effects such as forward and backscattering may also occur. Utilize data collection in detection networks without human presence to establish a baseline, and future elements of data for statistically significant differences typically exhibited by the physical presence of one or more anatomy, whether or not the one or more anatomy is moving. By examining the , the detection network makes a decision regarding the presence or absence of a human body within the network.
Depending on the communication network itself, the hardware used and the human body, these changes may be registered within the network in different ways and produce different results; However, such changes are detectable. This differs from radar technology in that the detection of an object does not rely on or need to rely solely on signal reflection, but relies on signal absorption, which is the opposite principle, which is often detected through measurable changes in signal properties between the transmitter and receiver at different physical locations. .
By analyzing changes in signal characteristics between nodes in the network, the location of a disruptor and, for example, a human body can be calculated for the network. Since the mere presence of the body is sufficient, the system does not necessarily include fiducial elements, nor does it need to rely on motion or movement. Because no fiducial elements are required, the systems and methods described herein can provide anonymous location data reporting services, and no additional components or devices need to be associated with the tracked body regarding traffic, route of travel and occupancy. data can be collected. Generally speaking, the systems and methods described herein operate in real time.
1 is a schematic diagram of a system and method in accordance with the present disclosure; In the illustrated embodiment 101 of FIG. 1 , a detection network 103 comprising a plurality of nodes 107 is disposed within a physical space 102 , such as a room, corridor, hallway or doorway. In the illustrated embodiment of FIG. 1 , an indoor space 102 is used, however, the systems and methods described herein are operable in an external environment as well. In the illustrated embodiment, node 107A is communicatively coupled (111) to a telecommunication network 115, such as an intranet, the Internet, or the Internet. The server computer 109 may also be communicatively coupled to the telecommunication network 115 and to a node 107A coupled thereby ( 113 ). The illustrated server 109 includes programming instructions for implementing the system described herein and performing the method steps described herein. However, in one embodiment, the functions performed by the server may be performed by one or more nodes 107 with appropriate software/programming instructions or modified as appropriate.
1 , each node 107 is communicatively coupled with at least one other node 107 in the detection network 103 , and two or more or all other nodes in the detection network 103 . 107 may be communicatively coupled. For example, in a typical wireless network deployment strategy, a plurality of wireless access points are deployed throughout the physical space 102 to generally ensure high quality signals are available everywhere. These nodes 107 collectively form the detection network 103, and can transmit data to each other or only to a router or set of routers. In the illustrated embodiment of Figure 1, node 107A is a wireless router and other nodes 107B, 107C and 107D are wireless access points. However, this is one possible configuration. Also, it is not necessary for any given node 107 to be a particular type of wireless device. Any number of nodes 107 may include routers, access points, beacons, or other types of wireless transceivers. Also, although there may be any number of nodes 107 in one embodiment, a minimum of two is preferred. More nodes 107 within space 102 increase the amount of data collected (as described elsewhere in this application), and improve the likelihood that a human body is generally interposed between at least two nodes 107 . to improve location resolution.
In the course of normal operation, the node 107 frequently transmits and receives wireless transmissions. For example, when the wireless router 107A receives a data packet, the wireless router 107A typically broadcasts a wireless transmission including the packet. This means that any receiver within the broadcast radius of router 107A can receive the signal, whether intended or not. Likewise, when an access point receives local data, this data is likewise broadcast and can be detected by other access points and routers. Although user data is not actively transmitted in the network, other data is frequently transmitted. These other transmissions may include status data, service scans, and data exchanges for functions of lower-level layers of the network stack.
Thus, each node 107 in a typical detection network 103 receives a transmission on a coherent basis, and in a busy network, this may in fact be a continuous basis. Accordingly, the detection network 103 may be used to calculate the presence and/or location of a biological mass 104 or 105 physically interspersed within the transmission range of the network 103 . Because the presence of a human body affects the characteristics of signals transmitted between or among nodes 107 in the network 103 , such presence can be detected by monitoring changes in those characteristics. This detection may also be performed while the data of the transmitted and received data packets are still transmitted and received; That is, detection occurs in a normal data exchange between two or more nodes or among two or more nodes, which continues irrespective of detection. Specifically, a wireless network operates to transmit data between nodes while simultaneously detecting and locating an object using characteristics of the manner in which data packets incorporating that data are affected by the presence of the object in the transmission path. can do.
1 , at least one node 107 itself is subject to statistically significant changes in signal characteristics even while waiting for, receiving and/or transmitting communication between itself and another node 107 . A communication signature between 107 and at least one other node 107 is monitored. Certain geometries of the physical space 102, including the presence and location of fixtures in the physical environment, generally do not affect the system, but monitoring is sensitive to statistically significant changes in signal characteristics that are indicative or demonstrating characteristics of the human body. because it is about That is, the change in signal characteristics is due to a change in an absorber or reflector such as a human body in a communication space or a physical environment covered by the detection network 103 . Detection of the presence of a human body within the detection network 103 may be performed using a method of statistical analysis on the signal, such as using a sensing algorithm, as described elsewhere in this application. Again, it is not necessary for the anatomy to be associated with a fiducial element or motion. Instead, the detection network 103 has introduced a new object (typically a human body object) into the communication space and the existence of that object has resulted in a change in the nature of network communication between the nodes 107 , typically data packets. It is detected that the characteristic of network communication has changed.
In order to detect changes, a baseline of signal characteristics is generally developed against which the most recently transmitted signal is compared. These characteristics are derived from typical wireless communication network diagnostic information. This baseline of the signal characteristics between the nodes 107 is generally established prior to using the detection network 103 as a detector. This can be done by operating the detection network 103 in a typical or normal environment, i.e. the detection network 103 communicates data packets and does not have significant biological masses interposed in the physical broadcast space of the detection network 103. . For a period of time during such operation, signal characteristics between and/or among nodes 107 are monitored, collected and stored in a database. In one embodiment, the server 109 receives and stores such data, but in one embodiment, one or more nodes 107 may include a hardware system configured to receive and/or store such data.
For example, if node 107 includes special purpose hardware and programming for use in accordance with the present disclosure, such node 107 may store its own signal characteristic data. Such signal characteristic data may be data related to a received energy characteristic of a signal received by a particular node 107 from one or more other nodes 107 . The baseline data establishes a signature characteristic profile for each node 107 , which is essentially typical of the signal received by the node 107 under normal operating conditions without significant biological masses intervening in the detection network 103 . and/or a set of data defining general characteristics. A node 107 may have one or more such profiles for different nodes 107 receiving data.
In one embodiment, after the baseline signature is detected and collected, the detection network 103 will continue to operate in a generally identical or similar manner, but is now capable of detecting the presence of a biological mass. This is done by detecting and collecting additional signal characteristics, typically in real time, when the detection network 103 operates in a normal mode of sending and receiving data packets. These newly generated real-time signal characteristic profiles are also generally characteristic of the signal between two specific nodes 107 in the detection network 103, so that the corresponding baseline signal characteristic profiles for the same two specific nodes 107 and can be compared. A statistically significant difference in certain properties between the two profiles can be interpreted as being caused by the presence of a significant biological mass such as a human body.
The comparison operation may be performed by suitable hardware at a given node 107 , or a real-time signal characteristic profile may be sent to the server 109 for processing and comparison. In a further embodiment, both are performed so that copies of the real-time data can also be stored and accessed via the server, effectively providing a history of the signal characteristic profile.
This means that, as described herein, at least some signal properties between at least two nodes are altered when a data packet with which a biological mass intervening within a network is generally intercepted and/or generally interacts with is transmitted, as described herein. because it makes The degree and nature of the change will generally be related to the nature (eg, size, shape and composition) of the particular intervening biological mass and its location 103 in the network. For example, when a housefly flies through the detection network 103 , the amount of signal change may be so small that it is indistinguishable from natural fluctuations in signal characteristics. However, the larger the mass, such as a human body, the more substantial and statistically significant change in signal characteristics may occur.
This change need not necessarily be evident in every signal characteristic profile for the detection network 103 . For example, if a mass is interposed at an edge of the detection network 103, the node 107 closest to that edge is likely to experience a statistically significant change in signal properties, whereas the node 107 on the opposite side of the detection network 103 is likely to experience a statistically significant change in signal properties. Nodes will have little or no statistically significant change (without each other's signals passing through or around the biological mass). Thus, if the physical location of the node 107 is also known, the system not only determines whether a biological mass is present in the detection network 103, but also which node 107 is experiencing the change and calculating the magnitude of that change. By determining it, we can compute an estimate of where it is located.
This can be seen in the illustrated embodiment of FIG. 1 . In FIG. 1 , assuming that the existence of only one human body, A 104 or B 105 , exists for the sake of simplicity, A 104 is generally located at node 107C rather than between nodes 107A and 107C. ) and node 107A will have a greater effect on the signal characteristics. Moreover, A 104 also generally has a small bidirectional effect on the signal characteristics between nodes 107B and 107D. Conversely, B 105 will have a bidirectional effect on the signal characteristics between nodes 107A and 107C as well as between nodes 107B and 107D.
All nodes can communicate with each other, but in communications where A 104 and/or B 105 are not generally aligned with the communication path between the nodes, the influence of A 104 and B 105 is usually more can be ignored For example, none of the human body 104 or 105 may significantly affect the transmission between the node 107A and the node 107B because the human body 104 or 105 is not in the transmission path between those nodes. However, A 104 may affect the transmission between nodes 107C and 107D.
It should be noted that the presence or absence of a biological mass within the communication area of the detection network 103 will not necessarily result in any change in data communication. The detection network 103 is expected to use its standard existing protocols, means and methods (including all forms of retransmission and error checking) to ensure that data in transmitted data packets is correctly received, processed and operated. In fact, the detection process of the detection network 103 is performed in addition to the standard data communication of the detection network.
It should be appreciated from this that the data in the data packets communicated by the node 107 in the detection network 103 will generally not be used directly to detect the biological mass within the communication area of the detection network 103 . Instead, the data will simply be data communicated via the detection network 103 for whatever reason, often not relevant to the detection of a biological mass. Moreover, although this disclosure generally contemplates packetized communication in the form of data packets, in alternative embodiments, data may be continuously communicated in non-packetized form.
In one embodiment, to enable detection network 103 to detect the presence or absence of a particular biological mass, the system includes a training aspect or step. This aspect may include, after a baseline is established, one or more anatomy being intentionally interposed in the network at one or more locations of the network, and one or more additional baseline data sets being collected and stored. This second baseline may be used for comparison purposes to improve the accuracy of detecting the size, shape and/or other characteristics of the biological masses interposed in the network and/or to improve the accuracy of positioning. Such training may use supervised or unsupervised learning and/or may utilize techniques known to those skilled in the art of machine learning.
In one embodiment, the detection network 103 may use a specialized protocol comprising a controlled messaging structure and/or format that may be controlled from one node 107 to another node 107, such that any node 107 ), it becomes simpler and easier to determine whether a message originated from Such controls further facilitate specific improvements in processing, and identify and use specific signal qualities and/or characteristics specific to detection aspects of networks 103 that may be different from general networking aspects sharing the same network 103 . enable the receiver. There is no need to scan a signal or sweep an area in space when controlling messages sent and received on the opposite side of a positioned mass, such functions being much more than required for a typical broadcast or directional transmission between nodes (107). This is because they tend to require more expensive equipment. Messages are typically structured in such a way that they best produce data usable to a detection algorithm that is configured to work best with the communications network in use. In general, such an arrangement still avoids the need for waveform level analysis of signals transmitted by the network.
In the illustrated embodiment, each node 107 may generally determine the originating node 107 of a packet received by that node 107 . This message origination information is typically encoded within the message itself, as is known to those skilled in the art of communication networks. By way of example and not limitation, this can be done by examining data embedded in protocols established in the networking stack, or by examining data sent by the originating node 107 for the specific purpose of implementing the systems and methods described herein. can be performed. Typically, each node 107 has the appropriate hardware and processing capabilities to parse the received message. Although many different topologies and messaging protocols allow for the functionality described herein, mesh networking topologies and communication methods in general will produce useful results.
FIG. 2 illustrates an embodiment 201 of a method according to the present disclosure and should be understood in the context of the system of FIG. 1 . In the illustrated embodiment, the method begins (203) with the establishment (203) of a detection network (103) comprising a plurality of communication nodes (107) according to the present disclosure. As is known to those skilled in the art of establishing communication systems, many different approaches to the setup of such networks 103 exist, and many other network 103 topologies may be feasible within this framework.
Next, a digital map of memory representing the physical node 107 geometry of the detection network 103 may be generated (205). The detection algorithm described in this application generally uses information about where the node 107 is located in the physical environment 102 . Data regarding these physical locations of nodes 107 may be manually fed into an accurate diagram of the physical network environment 102 and/or the software may generate a relational location map of one or more nodes 107 within the detection network 103 . used to automatically create, facilitating easier placement of nodes 107 into such environment maps or diagrams.
Alternatively, nodes 107 may be placed on an empty map or an empty map or diagram using relational (as opposed to absolute) distances for detection. In such a dimensionless system, messages may still be generated ( 101 ) from algorithms related to the detection of a human body in the system, and messages relating to the movement and/or presence of the human body within the network 103 being sent to the user Additional manual processing such as input may be involved.
In embodiments with automatic node 107 location detection, node 107 location may be, for example, but not necessarily limited to: the physical location of a particular hardware component, such as node 107 , and each relative to one or more other nodes 107 . setting up and configuring a detection network 103 including the location of the node 107 of signal strength indicator; and algorithmically and/or programmatically detected by one or more nodes 107 and/or computer server 109 based on factors such as transmission delay. In the illustrated embodiment, this step 205 comprises overlaying on a map created on a digital map of the physical space 102 or the environment (referred to herein as an “environment map”) and , the detection network 103 occupies the floor plan of the building. This step 205 may further and optionally include a scaling element for aligning the scale of the generated map to the environment map, and a user manipulation and/or modifiable input element for adjusting the generated map for fine tuning. to more closely match the actual node 107 placement geometry, as will be understood by one of ordinary skill in the art. In an alternative embodiment, each node 107 may be manually placed in an appropriate location on the environment map without using a relative location algorithm.
Either way, this step 205 establishes the physical location of the node 107 in the detection network 103 , which enables determination of the location of the intervening biological mass due to the presence of the human body within the detection network 103 . will make By placing the node 107 on a map (either manually or via automatic means), the node 107 can be configured to connect to the human body in the network 103 based on how the baseline signal affects communication between the various nodes 107 . existence can be traced. The system 101 then uses the collected information about the signal arriving at the receiver, given the set of transmitted information known to the data processing algorithm. The data processing algorithm ultimately determines whether a human body exists within the network 103 and/or where the human body is located within the network 103 .
The messages are then constructed and exchanged according to the protocol in a format determined to be suitable for detecting the presence of a biological mass within the network 103 (207) according to the protocol. This may be done using general-purpose networking protocols known in the art, such as those of the OSI network model, or special-purpose protocols that replace or supplement these general-purpose protocols.
In general, this step preferably further comprises controlling and/or modifying (207) the messages passed within the detection network (103) for the specific purpose of detecting the presence of the human body and facilitating simplified statistical analysis. do. By controlling the exchange of messages (207), the system 101 coordinates for common content sent over the detection network 103, while including, but not limited to, transmission intervals; transmit power; message length and/or content; and enable adjustment of parameters including the intended message recipient(s). Again, the system does not have to rely on waveform level analysis, so it can operate within the scope of wireless communication standards.
Controlling these parameters 207 enables the development of statistics and/or analytics, which may be based, at least in part, on predefined or expected message content or characteristics. Such content and/or characteristics may include, but are not limited to, transmit time stamps and/or transmit power levels. By controlling and modifying these aspects (207), such as, but not necessarily limited to, the detection network 103 in accordance with the present disclosure, such as, but not limited to, an automatic gain control (AGC) circuit that may be integrated into specific receiver hardware at node 107 . can overcome hardware limitations including hardware features that cause undesirable results when used in
In the embodiment 201 shown next, the space 102 clears 209 a significant biological mass, particularly the human body 205 . A statistical baseline of signal strength is then developed locally by each node 107 (211). Again, by placing the node 107 on the map via manual and/or automatic means in step 205 , the node 107 can be configured based on how the baseline signal affects the communication between the nodes 107 . The network 103 may track the presence of the human body.
Next, the biological mass enters the detection network 103 213 , causing signal absorption and other distortions, which appear in changes in signal properties between nodes 107 . The change is detected (215) and analyzed (217) to determine whether the change is indicative of the presence of a human body or other type of biological mass for which the detection network 103 is configured to detect. Such detection is more limited to the area between nodes, such as at least within the inner area between three nodes on the network, but is possible with greater accuracy depending on the algorithm and hardware in use at the time.
Typically, this is done using a detection algorithm executed by one or more nodes 107 or server computers 109 . Nodes 107 and/or servers 109 use software to determine the location of detected biological masses in detection network 103 using one or more detection algorithms. These algorithms generally compare a baseline profile to newly detected signals, and also include, for example, without limitation: the physical location of a particular hardware component, such as node 107 , and each node 107 relative to one or more other nodes 107 . ) setup and configuration of the detection network 103 including the location of; signal strength indicator; and transmission delay, and various data and other aspects.
Generally speaking, as described elsewhere in this application, these algorithms compare a newly collected signal characteristic profile 215 to a baseline signal characteristic profile 211 to identify changes and determine if changes are made based on the nature of the changes. including determining whether it is indicative of the presence of a human body. This determination may be performed, at least in part, using training data developed through machine learning as described elsewhere in this application.
In one embodiment, the detection algorithm may further include the use of observed signal property change(s) between one or more pairs of nodes 107 in the detection network 103 , correlated in time and relative effect. These factors enable the identification of the physical location in the detection network 103 at which such signal change occurred, thereby enabling estimation of the physical location of the human body causing such signal property change(s), which in turn enables the biological mass to It can be used to estimate the physical location in the environment of the intervening detection network 103 . These physical locations may be provided as simple x, y, z coordinates depending on the coordinate system or may be displayed visually on a map, for example.
When multiple human bodies are present in the detection network 103 , it is more difficult to isolate the influences of the various individuals from each other, and accuracy will generally be improved by adding more nodes 107 . In one embodiment, techniques such as advanced filtering and predictive path algorithms may be used to individually determine an individual's location within the network 103 . Although the movement of the human body in the network 103 is not required for the systems and methods to operate correctly, the movement or lack of movement can be used to improve detection accuracy by predicting the path of a single individual. This may help the individual to statistically identify instances that are statistically “disappeared” from the detection network 103 , but the system has enough data to assume that the individual is still present in the network 103 .
For example, if an individual's movement path is predicted and terminated next to another detected individual, the system 101 may determine that the two individuals are too close to a signal characteristic profile change to identify them individually, but because of the individual's movement, the path has not determined that the individual is outside the detection range of the network 103, and the algorithm determines that the individual is present in close proximity to the other detected anatomy and is not moving. Thus, when one of the two proximate stationary anatomy moves, the algorithm can individually identify each again and resume path prediction based on the observed signal characteristic profile change.
In this manner, systems and methods according to the present disclosure may track whether one or more individual anatomy within the network 103 is moving, and whether such anatomy is associated with a fiducial element. Specific individual identification may further be performed using other path prediction and sensing algorithms, such as those used in human body tracking technology in the robotics industry, to estimate who a particular individual is. It should be noted that an individual may impart specific and unique influences on various signal properties that enable the identification of a particular individual and also allow one particular individual to be distinguished from another individual. These influences can be used to further determine the location of a particular individual within the detection network.
The detection algorithm(s) are generally configured to take advantage of the characteristics of the communication signal taking into account factors such as, but not necessarily limited to, the frequency of the signal(s) and the transmitted power level of the signal(s). In one embodiment, the algorithm determines the effect of the presence of the human body on a signal characteristic in the RF environment within the communication network, and then uses a data-driven method to identify when that effect is later observed. detect the presence
For example, in one embodiment, the signal characteristic that varies with the presence of the human body is the registered signal strength among the nodes 107 . This is particularly the case within a BLE network, and statistics related to signal strength over time may indicate the presence of a human body within the network. These artifacts may be used by the detection algorithm(s) to provide information about the physical location of the object causing the artifact. That is, by combining various statistics about the artifacts captured via the network 103 , the system determines where the artifacts are in the physical space 102 , and thus, where the human body is within the network 103 .
In the simplest use case, the algorithm can simply identify changes in signal characteristics that are similar to changes known to be caused by the presence of the human body (eg, from training), and whenever such changes are detected relative to baseline A detection event 219 may be triggered. This may look like an adjustment of the mean, standard deviation, skewness or variance of the signal strength depending on the system 101 used. When the detected signal characteristic profile returns to a profile similar to the baseline, it may be assumed that the physical environment 102 has returned to an empty state with respect to the presence or absence of the human body.
To elaborate on the simplest use case, in this case the baseline profile consists of some or all of the baseline profiles that exist when the space does not contain any anatomy and may depend on physical adjustments to that space. . A simpler algorithm that can account for changes associated with newly detected anatomical changes relative to a recent baseline can be used to handle such situations; However, when the baseline has changed, it is desirable for the system to accurately determine whether one or more current signal profiles match an empty baseline profile, or a profile representing some degree of occupancy. This determination may be made in response to movement, but preferably not in response to movement, but rather based on whether a characteristic of such a signal may be correlated with one or more empty baselines or one or more presence signal profiles.
In comparison to other technologies used for such determinations that motion is required to perform a function (typically a Passive Infrared (PIR) sensor), the systems and methods described herein allow for the presence of a static human body within space 102 , moving It is possible to detect whether or not the human body is not in the space 102 more precisely. For applications such as security and occupancy sensing, it can be more difficult to fool this system. Some examples of tricks that can trick PIR and other similar motion-based technologies are that a human body moves very slowly while holding a seat while entering a space, or remains immobile in an area usually after entering. Another advantage is that the system does not require any additional hardware other than the hardware used for normal network communication. This is because additional software and processing functions can be provided by modifying external components or existing hardware, such as implementing appropriate software such as SOC (System On a Chip) attached to a ready-made communication module. If additional processing power is required, additional processing node(s) can be added to analyze the signals propagated between the nodes 107 , or the workload is transmitted to a dedicated server machine 109 and sent to it can be processed by
Determining the presence and/or location of the human body may relate to the specification of a particular signal type to be analyzed, controlling the signals transmitted between the nodes 107 on the network 103 to best achieve such detection. Exemplary examples related to signal absorption, reflection, backscattering, etc. due to additional anatomy between nodes 107 by sending controlled communication pulses through network 103 where the original signal is known and the transmitted power can be modulated data can be developed. Since, in general, the baseline system can be constructed without the presence of a human body and it is assumed that this baseline will look statistically different from the presence of the human body, the signal characteristic change can be further assumed to be due to the presence of the human body in the network. By configuring the system to allow input of a timer when space 102 is empty and generally improve baseline definition, the system can be periodically recalibrated to achieve improved accuracy. Generally speaking, the tracking algorithm uses the best available triangulation calculations combined with statistical methods known to those skilled in the art of location technology, combined with a detection algorithm for detecting the human body within the network 103 .
The present invention does not require a fiducial element associated with the detected anatomy, nor does the body need to have any device capable of communicating with the network; However, such techniques make use of such elements when deployed within a system. Adding these elements can reduce the computational burden on the system and increase its accuracy. The systems and methods described in this application do not exclude such additional functionality and may be improved thereby. Augmenting detection with an inference engine makes the system more robust as the sensing hardware can recover from false alarm conditions or other edge cases. Such an inference engine may further feed information to the machine learning system, which may further modify one or more baseline signal profiles or one or more presence signal profiles to improve the performance of the system.
In one embodiment, the detection network 103 implementing the systems and methods described herein may further include an element 219 to take an action based on the detected presence and/or location of the human body. This may for example first determine the presence and/or location of a human body on the network via a network, then determine an action to be performed based on the presence and/or location of the human body on the network, and configure the network to take that action. This can be done by sending a control signal over a network using a computer to send a message via Since the communication network and the network performing the detection may be the same network, the invention described herein extends the traditional functionality of the communication network to include human body detection and/or position sensing without requiring additional sensing hardware.
The computer element of the network must perform additional calculations and can generate communication signals. This can reduce the computational burden on the computer; However, the network may still function as a command and control network independent of the network as a detection network.
The system as a whole can range from occupant sensing, which can be used for lighting control and/or security as a whole, to counting the number of people in a space needed for heat and/or traffic maps to systems that track individual human bodies as they move through the space. It can be used for applications. The technology may be integrated into the network node itself, or it may be a combination of nodes that transmit information to a processing element (either directly on the network or in the cloud) to perform a calculation to determine the desired information. The final integrated suite can be tailored to the application and used in a variety of ways.
Although no additional sensors are required (but in one embodiment a sensor may be present), detection is effectively accomplished by calculating statistics from a traditional RF communication stack. Such a system avoids collecting personal data from people walking in space, requiring any separate device acting as a fiducial element because the system knows that approximately the size of a human body of water, organs, clothing, etc. has passed. don't As such, the technology deviates significantly from traditional methods of tracking a human body as it moves through space.
A logical extension of the systems and methods described herein includes dynamically processing functional network messages within statistical analysis to avoid or reduce additional messaging overhead to the system. Further, in one embodiment, the systems and methods described herein are extended to include dynamic adjustments to network and/or message structure, configuration and/or operating parameters based at least in part on functional messages transmitted within the network. is considered to be
Moreover, since tracking is generally based on signals affected by a human mass, the system does not rely on a human body moving around for detection. Because it does not rely on motion, it overcomes many of the shortcomings associated with traditional presence sensing technologies such as passive infrared and ultrasonic sensing technologies.
The use of communication network signals between nodes to detect the presence of a human body in a network in which the human body does not have a fiducial element is a radical departure from the current non-fiducial element detection method and is a completely new method for performing presence sensing. communication network is used for The combination of detection technology and the use of network nodes as a transmitter-receiver combination for performing human presence detection presented in this application provides a new type of human presence detection system that does not require additional equipment beyond that required to form the communication network itself. make up
The systems and methods described in this application may be implemented in a communication network for the purpose of normal communication without affecting the operation of the network itself. The network continues to operate as a communication network with its main function, but in this case some communication is used to calculate the location of the human body present in the network. Because the systems and methods described in this application utilize the basic operation of the network, a human body within the network further carrying a transceiver device known to the network can be detected and positioned with increased accuracy. Such transceiver devices, which may include mobile computing devices with wireless transceivers, such as, for example, cell phones, cell phones, smart phones, tablet computers, wearable computer technology, etc., may be connected to a network, and may employ traditional triangulation methods known to those skilled in the art. can be located by the network using When a human carries such a transceiver, machine learning algorithms can be applied, which in turn can further improve performance.
The calculation of the position of a known transceiver device may be compared to the position of a person determined by the non-transceiver aspect described herein. When the communication network reports both the location of the fiducial element and the human body within the network, these two locations can be compared. In general, since the detected position of the fiducial element has a higher accuracy than the estimated position of the human body based only on network communication, the position calculation for the position of the human body in the network is calculated for the position of the system with respect to the next human body entering the network. It can be tuned using machine learning algorithms to improve performance.
Using a machine learning algorithm, the system can improve the accuracy of the position prediction algorithm based on the known position from the transceiver. This allows you to confirm prior decisions and refine future decisions. For example, if previous decisions are found to be consistent by approximately the same amount, that amount can be applied to future decisions as adjustments. In this way, the system can be continuously improved and trained to better locate the human body within the network. Likewise, machine learning can continuously improve detection and false alarm rates. As an example and not by way of limitation, data regarding historical traffic patterns at a facility may be used to establish defaults, estimates, or expectations regarding a range of times or days when a particular facility is generally occupied or generally vacant. The system can use this data to improve performance.
Additionally, the system may include a network element; That is, making inferences based on physical interactions with devices or components attached to or communicating with networks that are operable or operating based on the presence of the human body, such as network-operable electrical switches, doors, motion sensors, infrared sensors, etc. is configured to derive. These physical interactions can be considered fiducial elements at the point of interaction for the purposes of the system. As an example, when a light switch that is part of a network is operated, the system may infer that a human body was present at or near a physical location of the switch when the switch is operated. As such, the system translates that information into known data points to which machine learning can be applied to better predict human presence in the future (i.e., knowledge inferred that its characteristics reflect the human body at a particular location near a switch). signal characteristics of various network devices at the point) can be used as inspection). Additionally, these events can be used as presence triggers for other purposes, such as security alerts. As an example, assuming the system is in secure mode and someone has found a way to hide their presence but is still interacting with the switch, the system can determine if someone is present and send an alert based on the interaction with the switch. Generally speaking, interactions with systems are defined physically and logically, and logical interactions will include typical usage patterns based on time, external inputs, etc. These systems serve as backups for RF presence sensing and provide the system with additional machine learning capabilities.
Additionally, the system may estimate whether a mobile transceiver in the network is actually being carried by a human body, eg, where the human body has left the device at a location within the network. Since the system can detect the human body as biomass through the change of signal characteristics, it can detect whether the transceiver is present in the network while the human body biomass is absent. This prevents erroneous training of the system and prevents the baseline and presence signal profiles from being corrupted by data uncorrelated with these profiles.
In one embodiment, as an additional input to the inference engine, if some indication of the system changes the state, and the human body within the detection area behaves in a way that corrects the system state, the system better reflects the user's preferences It can be inferred that the baseline and prerequisite profiles for For example, if a light in a space should be turned off along with a human body in the space, the human body could physically move, reflect the presence of waving an arm, etc., or simply take an action such as looking for a wall switch or walking towards a wall switch. . This movement can be detected in a reasonable amount of time, and the system can determine that the space is erroneously determined to be empty and adjust the baseline and presence profile accordingly. This activity may be referred to as inferring the existence of one or more human bodies in space.
As a side effect of collecting different signal characteristics and running them through different algorithms, a system can run multiple detection calculations simultaneously to achieve different performance criteria on the same system. By way of example, the same communication network may be used for lighting and security related detections; However, the collected statistics may be processed differently for the two applications, but may be processed simultaneously. In this way, lighting applications can still provide shorter detection times to detect but can potentially offer higher false alarm rates, while security applications can trade slightly longer time to detect while reducing false positive rates. have. The signal characteristics processed by the system may vary depending on the application, but they are all captured from the communications network and can be processed in several ways simultaneously. This processing method may be encapsulated into several sets of different sample baseline signal data to determine detection relative to a baseline signal profile.
In one embodiment of the system according to the invention, the system comprises a communication system capable of determining the presence of one or more human bodies from information relating to radio signals between two or more computers on a network, each computer comprising: a transceiver for communication ; and a computing element for performing the calculation, each computer sending a signal to one or more other computers on the network, the signal comprising a unique identifier of the computer sending the signal; Each computer processes the received signal for the purpose of determining the presence of one or more anatomy; and one or more bodies need not have any devices capable of communicating with the network.
In one embodiment of such a system, the algorithm determines the presence of one or more anatomy using a statistical method. In a further embodiment of such a system, the statistical method determines the number of anatomy present. In still further embodiments of such a system, the system may determine the physical location of one or more bodies on the network. In another embodiment of such a system, the system may track the physical location of one or more bodies over time. In yet a further embodiment of such a system, the system uses information regarding the presence of one or more anatomy to control a device on the network. In one embodiment, the network is a mesh network.
In one embodiment, the computer determines their relative physical locations and further determines the relative physical locations of one or more bodies on the network. In a further embodiment, the statistical method is applied to the measurement of signal strength to determine the presence of a human body. In a further embodiment, the transmitted signal is controlled to more readily detect the presence of a human body. In another embodiment, the power level of the transmitted signal is controlled to facilitate the presence of the human body. In a further embodiment, the system functions as an occupancy sensing system. In a further embodiment, the occupancy sensing system controls the lighting system. In a further embodiment, the network for controlling the lighting system and the network used for occupancy sensing use the same communication technology and hardware. In a further embodiment, the communication technology employed by the computer is selected from the list of Bluetooth Low Energy, Wi-Fi, Zigbee, Thread and Z-Wave.
In a further embodiment, the system functions as a sensing system for a security application. In a further embodiment, the security sensing system controls the security system. In a further embodiment, the network for controlling the security system and the network used for security sensing use the same communication technology and hardware. In a further embodiment, the system functions as a human body detector for a robotic system. In a further embodiment, the robotic system has a computer that dynamically positions various elements of the robotic system relative to each other. In a further embodiment, the network for controlling the robotic system and the network for functioning as a human body detector for the system use the same communication technology and hardware.
In a further embodiment, the system functions as a sensing system for HVAC applications. In a further embodiment, the HVAC sensing system controls the HVAC system. In a further embodiment, the network for controlling the HVAC system and the network used for HVAC sensing use the same communication technology and hardware.
In another embodiment, the system uses machine learning to improve detection capabilities and allows a person having his/her fiducial element to: (1) use known location techniques to determine the location of the fiducial element; (2) using the system described above to locate a person; (3) comparing the position calculated by the method of (1) of this paragraph with the method of (2) of this paragraph; (4) train the system through adjusting the positioning method using machine learning algorithms to improve the system's position calculation ability;
In another embodiment, the system may infer the presence of a human body in the network based on the human bodies interacting with one or more computers on the network in some way. In further embodiments, the system may use the estimated presence of the human body as an input for machine learning to improve detection capabilities.
In one embodiment of the system according to the invention, the system comprises a communication system capable of determining the presence of at least one body stationary and moving from information relating to signals between at least two computers on a network, each computer comprising: for transceivers; and a computing element for performing the calculation, each computer sending a signal to one or more other computers on the network, the signal comprising a unique identifier of the computer sending the signal; Each computer processes the received signal for the purpose of determining the presence of one or more anatomy; One or more bodies need not have any devices capable of communicating with the network.
In one embodiment, the algorithm determines the presence of one or more anatomy using a statistical method. In another embodiment, the statistical method determines the number of anatomy present. In other embodiments, the system may determine the physical location of one or more bodies on the network. In other embodiments, the system may track the physical location of one or more bodies over time. In another embodiment, the system uses information about the presence of one or more bodies to control devices on the network. In other embodiments, information regarding the presence of one or more anatomy is available to one or more systems that are not directly involved in the determination of presence. In another embodiment, the system has the ability to perform self-optimization to achieve a given performance according to one or more preset criteria.
In a further embodiment, a communication protocol or network is generally defined by a standards committee including, but not limited to, protocols such as Bluetooth Low Energy, Wi-Fi, Zigbee, Thread, and Z-Wave. In another embodiment, the statistical method is applied to the measurement of the received signal strength to determine the presence of the human body. In other embodiments, transmitting and receiving devices on the network may be selected and actuated by the system to facilitate human body detection. In another embodiment, the power level of the transmitted signal may be controlled to facilitate the presence of the human body. In another embodiment, the system functions as an occupancy sensing system for a lighting system. In another embodiment, the occupancy sensing system controls the lighting system. In another embodiment, the network for controlling the lighting system and the network used for occupancy sensing use the same communication technology and hardware.
In another embodiment, the system functions as a sensing system for a security application. In another embodiment, the security detection system controls the security system. In another embodiment, the network for controlling the security system and the network used for security sensing use the same communication technology and hardware. In another embodiment, the system functions as an occupancy sensor for a heating, exhaust and cooling (HVAC) system. In another embodiment, the occupancy sensing system controls the HVAC system. In another embodiment, the network for controlling the HVAC system and the network used for occupancy sensing use the same communication technology and hardware.
In another embodiment, the system uses machine learning to improve detection capabilities and allows a person having his/her fiducial element to: (1) use known location techniques to determine the location of the fiducial element; (2) using a system to locate a person; (3) comparing the position calculated by (1) of this paragraph with (2) of this paragraph; (4) train the system through adjusting the positioning method using machine learning algorithms to improve the system's position calculation ability;
In another embodiment, the system may infer the presence of a human body in the network based on the human bodies interacting with one of the computers on the network in some way. The interaction may be a direct physical interaction or an indirect interaction in response to a state change in the system (eg, waving an arm in response to a light being turned off). In another embodiment, the system may use the estimated presence of the human body as an input for machine learning to improve detection capabilities.
Also described herein is a communication system capable of determining the presence of one or more bodies stationary and moving in a detection network from information about signals between two or more computers on the network, each computer comprising: a transceiver for communication; and a computing element for performing the calculation, each computer sending a signal to one or more other computers on the network, the signal comprising a unique identifier of the computer sending the signal; Each computer processes the received signal to determine the presence of one or more human bodies in two or more ways to achieve different performance criteria required to function simultaneously for two or more purposes; One or more bodies need not have any devices capable of communicating with the network.
In one embodiment, the algorithm uses two or more statistical methods to determine the presence of one or more anatomy according to two or more sets of performance criteria. In other embodiments, the system has the ability to perform self-optimization to achieve more than one performance set according to two or more preset criteria. In a further embodiment, a communication protocol or network is generally defined by a standards committee including, but not limited to, protocols such as Bluetooth Low Energy, Wi-Fi, Zigbee, Thread, and Z-Wave. In another embodiment, two or more statistical methods are applied to the measurement of received signal strength to determine the presence of the human body according to two or more sets of performance criteria. In another embodiment, the system uses machine learning to improve the detection capabilities of two or more methods for determining presence and allows a person having their fiducial element to: (1) use a known location technique to determine the location of the fiducial element. to use; (2) using a system to locate a person; (3) comparing the position calculated by (1) of this paragraph with (2) of this paragraph; (4) train the system through adjusting the positioning method using machine learning algorithms to improve the system's position calculation ability;
In one embodiment, the systems and methods described herein include change detection. By way of example and not limitation, change detection may use or utilize a rolling baseline approach. In such an embodiment, a first baseline is established and compared to a second baseline, and any difference between the first baseline and the second baseline caused by the presence of a human body in the detection network will be recognized by the system. can It receives a set of radio signal characteristic data from one or more nodes in the detection network, and based on this data, an RF environment caused by the presence of a human body in a different location when a first baseline is established compared to a second baseline. This can be done by programming software to detect changes in . These methods can be used when the system is first set up in place to establish a minimum performance level without requiring empty space at startup. Such systems with change detection can improve overtime to the state between change and presence that the limited aspect of presence detection can exist in such systems.
A representative example of change detection is shown in FIG. 3A . In the illustrated embodiment of FIG. 3A , the RF environment of FIG. 1 is depicted with a human body 301 present in the environment 103 at a discrete location 303 . As described elsewhere in this disclosure, the nature of the wireless signal transmission between nodes 107A-107D is affected by the presence of the human body 301 . In this particular example, the transmission between node A 107A and node D 107D is affected by the presence of the human body 301 . Accordingly, when the baseline is established as shown in the method of FIG. 2 211 , the baseline represents the radio signal characteristics and the human body 301 is present at the discrete location 303 . When the human body 301 moves to the new location 305, the characteristics of the radio signal between the nodes 107A-107D will change, as shown in FIG. 3A.
In this particular exemplary embodiment, there will be little interference between nodes A 107A and D 107D when the human body is in position 305 . However, there will be greater interference between nodes B 107B and C 107C because location 305 is located between these two nodes. Accordingly, when the difference is detected (205), as shown in FIG. 2, a change in the position of the human body can be detected.
This is less difficult to implement than presence sensing because the human body is not present and the need to establish a baseline in the detection network 103 is reduced. Such a system may detect changes within the detection network 103 and primarily based on when the human body changes position, and update the motion baseline profile on a rolling basis. That is, in this embodiment the baseline 211 is updated to be the same as the baseline when the anatomy 301 is in position 305 . Thus, when the anatomy 301 moves to the third position 307 , the detected difference 215 is the second baseline taken at position 305 and the detection network 103 when the anatomy is in position 307 . It is the same between the radio signal characteristics. Likewise, the baseline 211 is updated equal to the baseline of the position 307 , which can then be used to detect further changes in the position of the anatomy 201 . This detection method uses a change in the radio signal baseline caused by a change in the position of the human body 301 in the detection network 103 . This system has many advantages over conventional motion detection techniques such as passive infrared in that the system is not obscured by objects and can detect slow or gradual changes in position, which can be overlooked by conventional systems. has a In embodiments using this method, the baseline may be continuously updated.
In another embodiment, a system or method includes a reliability determination. This aspect may determine a confidence level that the third baseline corresponds to the first or second set of baselines. Reliability determination may use any number of techniques, including techniques known in the art, such as supervisory training or the use of statistical methods to determine the degree of similarity or difference between data sets. Confidence may increase or decrease over time, and may automatically make decisions regarding baseline differences with minimal differences but still indicate the presence of a human body in detection network 103 . A confidence in the determination of the presence or absence of an anatomy in the detection network 103 may be determined based on how similar the third baseline is to the first baseline or the second baseline. For example, if a third baseline is known to indicate the presence of a mass that affects signal properties, comparing a first baseline or a second baseline to a third baseline results in the identified mass moving from the third baseline to the third baseline. The level of confidence that it is identical to the identified mass can be improved (or decreased). Depending on the reliability, the system can be configured to use different reliability thresholds in different operating contexts (eg, HVAC, security, lighting, safety, etc.). A system or method involving reliability determination may operate across multiple systems using a common communication system, and the systems may include disparate nodes communicating with each other. A given node operates on multiple detection networks 103 , allowing better system scaling when deploying systems and methods across multiple adjacent detection networks 103 .
In another embodiment, the baseline difference may be used to count or estimate the number of anatomy present in the detection network. In one embodiment, this may be done by estimating the amount of human mass in the detection network and dividing by the average mass per person. This can be done by establishing a first, empty baseline when there are no human bodies in the detection network 103 , and establishing a second, occupied baseline when a known number of human bodies exist in the detection network 103 . Next, a third baseline is taken and compared to the first vacant baseline and the second occupied baseline. Then, the system software interprets the location where the third baseline radio signal characteristic fits the spectrum of the profile between the first empty baseline and the second occupied baseline, and from that determination the total in the detection network 103 is determined. Estimate the body mass. This estimate may be based on the total mass of the anatomy in the detection network when a second occupied baseline is established.
As a non-limiting example, if the signal distortion at the third baseline is moderate compared to the first empty baseline, the system may estimate that the amount of body mass present is relatively low. However, if the amount of signal distortion is closer to that shown in the second occupied baseline, then the system determines that the amount of estimated body mass present in the detection network 103 is closer to the amount that was present when the second baseline was taken. can Similarly, if the amount of distortion is determined to be much more extreme than reflected in the second baseline, then the system determines that the amount of total body mass present when the third baseline is taken exceeds the amount present when the second baseline is taken. can decide to Estimation of anatomical mass may be broadly based on the algorithms and methods described herein, and may be generally adapted to estimate multiple anatomy in space as described above.
In another embodiment, the system uses the entrance and exit signatures in the network diagnostic information to estimate the number of human bodies present in the space based on such signatures.
In this method, an entrance profile is established by a human body entering a space, an exit profile is established by a human body leaving the same space, and other profiles captured later are compared with the entry and exit profiles to determine whether the human body has entered or exited the space. decide whether The inlet and outlet profiles are learned through normal system operation based on estimates from presence detection techniques and decisions based on changes in state. By way of example and not limitation, if the system detects a change and the presence goes from undetected to detected, such an event may be classified as an ingress. Similarly, by way of example and not limitation, if the system detects a change and determines that the space has gone from occupied to unoccupied, such an event may be classified as an exit. The difference between the entrance count and the exit count can be used to estimate the number of human bodies present in the space.
In another embodiment, the system uses the ingress and egress signatures in the network diagnostic information in combination with a person count estimate derived by comparing the sample profile with the presence profile of various person counts.
Each of these methods may be used in conjunction with one or more counting methods to improve accuracy.
In one embodiment, the number or estimate of anatomy present in the detection network 103 may be used to operate other systems, such as, but not necessarily limited to, HVAC systems.
In one embodiment, a position or position of the human body in the detection network is estimated. It estimates the range between various devices to determine the position of the human body, examines a subset detection area consisting of a larger number of nodes, uses various position baselines, and additionally calculates the velocity and velocity of the human body in the detection network. This can be done by further extending the ability of the location system to analyze location over time to estimate orientation. In one embodiment of this method, the system may use various pairs of nodes, and based on baseline information, estimate the position of the human body between the pairs using the superimposed estimates within the node pair, and then calculate the actual position of the human body. To determine, one can determine the most probable location for the human body in the detection network based on the overlap estimate.
In another embodiment, a system with a higher number of nodes may use a system with a larger subset of detection areas, typically each having three or more nodes, to determine the presence or absence of a human body in each space, The common occupied space can be estimated based on the overlapped occupied area, which can be assumed to be the most specified position of the human body in the detection network. By way of example and not limitation, a set of four nodes may be subdivided into four sets of three nodes, where a location may be determined based on the location at which a subset of the presence of three nodes is detected. This sub-region creation allows detection within sub-regions where these sub-shapes are defined by overlapping regions created by sets of three or more nodes. Alternatively, a plurality of baselines may be established for the human body at different locations within the detection network, and subsequent baselines may be compared with the baseline to determine the location of the human body within the detection network. As a non-limiting example, a given detection profile may be generated for various locations in a detection area corresponding to the human body at a given location in the network, the sample profile being compared with a set of detection profiles corresponding to the different locations, The system determines which detection profile is most correlated with the sample profile, and the system determines the location of the human body based on the location of the detection profile considered to be most similar to the sample profile.
Further, based on the detected change in the position of the human body in the detection network over time, the moving speed and direction of the human body in the detection network may be estimated. This can be done, for example, using interpolation and dead reckoning (or direct reconnaissance). An exemplary embodiment of such a system and method is shown in Figure 3B. In the illustrated embodiment, The human body is located in the detection network 103 at time 0 (Time 0) at location 401. At a subsequent point in time 1 (Time 1), the human body is detected at another location (403). 403) is known, the distance 1 between them can be calculated. Additionally, the time from time 0 to time 1 can be determined or known, assuming that distance equals velocity times time, location 401 A movement speed of the human body from to the position 403 may be determined. Additionally, a vector representing the movement of the human body by implementing both a direction and a magnitude (velocity) may be determined.
However, if there are only two sample points, the possibility of a high error rate increases, and more than two sample points are required. For example, in the illustrated embodiment, the third profile taken at Time2 places the anatomy at location 405 . Again, the distance2 from location 403 to location 405 may be determined, and the ratio of velocity between these locations may also be determined. In the illustrated embodiment, these positions are generally linear, suggesting that the human body is moving in a nearly straight line in a given direction defined by the vector. Accordingly, the system may further estimate a future or predicted location of the human body based on this data. That is, at time 3 (Time3), the estimated position 407 of the human body may be determined based on the previously detected position. This estimation may place the human body external to the detection network 103 , and may further be used in the detection network 103 or to estimate the arrival or departure of the human body. Additionally, this information could potentially be used to alert other entire detection networks or other segments of the detection network that a person is about to arrive. This may be done, for example, by communication using the computer server 109 over the network 115 .
Continuing the above exemplary embodiment, in the illustrated embodiment, the detected position change of the human body from time 0 to time 1 is 0.8 meters per second at a rate of 0.8 meters per second. The detected change in the distance from Time 1 to Time 2 is at an average rate of 1 meter per second for an additional elapsed second of 1 second of 1.2 meters or a total of 2.0 meters for two total elapsed seconds. Thus, at time 3 after 1 second, an additional 1 meter movement can be expected, with the estimated future position 407 moving 1 meter further along the vector than position 405 .
In one embodiment, the systems and methods further train the system over time using machine learning. By way of example, and not limitation, the system may be configured to generate information from one or more of the change detection techniques described above using known feedback and/or combinations of feedback from third-party systems, such as, but not limited to, interactions with other smart devices. Data may be accumulated (thermostats, speech recognition systems, etc.) and/or inferences may be used over time to improve behavior according to expected or expected system behavior. Such inferences may be based on normal behavior in space, direct human interaction with elements of a system, or changes in sample profile from the human body's response to system decisions. As a non-limiting example, in a system implementing change detection to operate a lighting or HVAC system, user feedback is may be provided in the system.
Similarly, in a system that implements presence detection, a user can force change detection while within the detection network, resulting in an automated system that establishes a baseline execution for occupancy based on the time the change trigger event occurs in the system. A means can be provided, enabling when away from the change triggering event to be detected as generally empty. When combined with inference occupancy based on room type, this method can facilitate system training itself, improving functionality over time, from change detection to presence detection level functionality. Effectively, by using a system based on change detection, the system can infer presence and absence, thereby establishing a baseline profile for when the anatomy is not present and a detection profile based on the presence of an anatomy. In this way, the system will be able to train itself to move from operating as a change detection system to operating as an actual presence detection system.
In embodiments implementing counting, a combination of change detection and presence detection can determine an estimated counting of anatomy within a detection network, to estimate such a counting baseline profile and refine it over time. Such a system can facilitate system training itself over time to count the number of anatomy in the detection network.
In an embodiment implementing locating a person, the combination of change detection, presence detection, and anatomy counting determines a location estimate based on overlapping area and occupancy counts and ultimately establishes a more accurate baseline estimate to determine the time This over time allows the system to improve locating the human body within the detection network. Such systems may include an inference engine, such as computer software running on the server, and/or may build estimates of expected system behavior from normal operation and adjust operating parameters according to the expected behavior. For example, if the detection area is typically empty from 10 am to 3 pm and occupied from 3 pm to 6 pm, the parameter expects an empty state from 10 am to 3 pm and 3 pm It can be adjusted to anticipate the state of existence from 6:00 p.m. to 6:00 p.m.). Such inferences can be developed over time and can maintain overall flexibility while improving performance at those times.
Nodes can be placed in various location combinations to improve system operation. The system may operate with walls, ceilings, fixed nodes, mobile nodes, and/or nodes located in mixed configurations. Since the space is three-dimensional, the detection area can be defined by nodes on different floors of the building. Nodes may be placed on the wall at locations such as switches and exits. The broadcast range generally defines the perimeter of the detection area, and the system can be configured to assume that a human body is within that perimeter and test network diagnostics.
In one embodiment, one or more nodes may be placed in the ceiling. By way of example and not limitation, this may occur when a node is integrated into a fixture and/or lighting system. In such embodiments, nodes may generally radiate downward into the detection area, and the system may be configured to examine network diagnostics based on radiation and multipath patterns other than those seen in switch and egress based systems. In such embodiments, nodes generate communications in a generally downward direction, where reflections from walls, objects, and floors generally ensure that RF energy reaches other nodes via multiple paths. Multipath also generally provides coverage of the detection area. This coverage due to multipath means that ceiling-mounted nodes behave similarly to wall-mounted nodes with respect to the human body's influence on network diagnostic information.
other fixed nodes, such as television; Also contemplated are, but are not limited to, monitors and smart home hubs. These nodes may be installed on a wall, ceiling, or in a fixed location. Other nodes such as smart phones, tablets and laptop computers can be used as mobile nodes in the detection network. However, in such an embodiment, the mobile node may first locate itself relative to the fixed node in the system. Once the location is established within the network, the accuracy of the system can be further improved.
In one embodiment, a combination of nodes may be used in the detection area. When combining a larger number of nodes, the system can determine the optimal node for operation. The optimal node may be determined by determining, among other things, the most efficient node for the selected functional level. As the number of nodes increases, the accuracy of the decision generally increases as well as at the functional level.
In one embodiment, one or more nodes may operate in a plurality of detection regions. This enables improved system scaling, especially for adjacent detection areas. Such scaling may also lead to inference within a larger node network comprising multiple detection areas, making it easier to track human detections from one detection area to the next. For example, nodes may be shared between detection areas. A given node in the first detection network may have network diagnostic information based on the communication in the first detection area, and may also be part of the second detection network and have network diagnostic information based on the communication in the second detection network. The system may make independent decisions about how to operate the third-party system in each of the two detection areas. Examining the reasoning for a detection area may improve the determination of the presence or absence of a human body within a detection area, particularly when a person leaves one detection area and enters another. The detected change in the signal characteristic may be used to determine the presence or absence of a human body in the respective area based on information shared between the first and second detection areas.
In one embodiment, the systems and methods may operate through the use of mass identification techniques. In such an embodiment, a “mass” is identified and tracked. A unique identity can be assigned to a mass by a computer system and tracked based on changes in radio signal properties. By way of example and not limitation, if a mass is first detected near the center of the room and then at a location several feet away from the center of the room, then the system did not detect any other masses entering the room, and the system 2 It can be assumed that the detected mass is the same as the first detected mass, but has been rearranged to a new location. Based on the difference in signal properties caused by the interference of masses in the network, the system can infer that, for example, different masses exhibiting similar motion will have a similar effect on the signal properties. In this way, the system can “learn” how to identify and track masses.
Each human mass in the system will cause different interference characteristics when placed anywhere for most indoor locations, but the total set of human bodies likely to exist in a room is usually finite. That is, most interior spaces are occupied most of the time by the same basic set of people most of the time and change insignificantly and infrequently. For example, the same set of people appears every day in public places such as work, school, and even restaurants. Since most indoor spaces can only be entered by a limited number of entry points, such as doorways, the system can detect people entering and exiting the entry point and determine specific interference patterns due to the presence of a specific human body when entering the space. Based on signal characteristics (interference) and how these characteristics change compared to other human bodies in space, it is possible to determine where and how each human body moves through the room.
In particular, it is contemplated that the system can automate various aspects of setup, particularly with regard to grouping nodes into detection regions and building nominal functional levels based on the machine learning methods described herein. A system that determines the nearest node and estimates the detection area through inference does not need to be set up by the user. Based on the best estimate, the user can simply place nodes throughout the building, and the nodes use unsupervised machine learning to automatically group them into detection areas, ultimately letting the building system learn how to detect occupants. . Occupancy may relate to actions taken by occupants, and may develop automated systems that can reduce or eliminate the need for human input for normal system operation.
In one embodiment, the method is a method for detecting the presence of a human body, the method comprising: providing a first transceiver disposed at a first location within a detection area; providing a second transceiver disposed at a second location within the detection area; a computer server communicatively coupled to the first transceiver; the first transceiver receives a first set of wireless signals from the second transceiver; said computer server: receiving a first set of signal data from said first transceiver, said first set of signal data comprising data relating to a characteristic of said first set of wireless signals; ; estimating that the first set of signal data is indicative of the presence of a human body in the detection area; for wireless communication from the second transceiver to the first transceiver based at least in part on a characteristic of the first set of wireless signals in the first set of signal data when a human body is estimated to be present in the detection area; generating a detection signal profile; the first transceiver receiving a second set of wireless signals from the second transceiver; The computer server may further include: receiving, from the first transceiver, a second set of signal data comprising data relating to a characteristic of the second set of wireless signals; estimating that the second set of signal data indicates the absence of any human body in the detection area; wireless communication from the second transceiver to the first transceiver based at least in part on a characteristic of the second set of wireless signals in the second set of signal data when the absence of any human body within the detection area is inferred generating a baseline signal profile for the first transceiver receiving a third set of wireless signals from the second transceiver; the computer server receives a third set of signal data from the first transceiver, the third set of signal data comprising data relating to a characteristic of the third set of wireless signals; determining whether the third set of signal data indicates the presence or absence of any human body in the detection area, wherein the detection signal profile and the baseline signal profile and the third set of signal data are determining, based at least in part on the comparison.
In a further embodiment, the method comprises inferring that the first set of signal data is indicative of the presence of a human body in the detection area, wherein the inference is received by the first transceiver from other transceivers in the detection area based at least in part on the additional set of signal data for the signals; and inferring that the second set of signal data is indicative of the absence of any human body in the detection area, wherein the inference is an addition to signals received by the first transceiver from other transceivers in the detection area. based at least in part on the set of signal data.
In a further embodiment, the method includes: the computer server stores a plurality of historical data records indicating whether a human body has been determined to be present in the detection area for a period of time, each of the historical data records being in the detection area the number of human bodies determined to be performed and an indication of the date and time each of the numbers of the human bodies was determined to be present in the detection area; and making, by the computer server, at least a portion of the plurality of historical data records available to one or more external computer systems via an interface.
In a further embodiment, the method further comprises: the computer server being operatively coupled to a second system; and only after the computer server determines that a human body is present in the detection area, the computer server operates the second system.
In a further embodiment, the method comprises causing the first transceiver and the second system to be configured to communicate using a same communication protocol.
In a further embodiment, the method further comprises: an electrical system; lighting system; heating, exhaust and cooling (HVAC) systems; security system; and industrial automation systems, electrical systems.
In a further embodiment, the method comprises wherein the wireless communication uses a protocol selected from the group consisting of Bluetooth, Bluetooth low energy, ANT, ANT+, WiFi, Zigbee, Thread, and Z-Eve.
In a further embodiment, the method includes wireless communication from the second transceiver to the first transceiver having a carrier frequency in a range encompassing 850 MHz to 17.5 GHz.
In a further embodiment, the method includes causing the computer server to determine whether the third set of signal data is indicative of the presence of a human body, including a confidence metric.
In a further embodiment, the method includes the first transceiver and the second transceiver being configured to automatically calculate a relative position within the detection area.
In another embodiment, the method includes a method for estimating the number of anatomy present in an area, the method comprising: providing a first transceiver disposed at a first location within a detection area; providing a second transceiver disposed at a second location within the detection area; a computer server communicatively coupled to the first transceiver; the first transceiver receiving a first set of wireless signals from the second transceiver when a human body is not present within the detection area; the computer server receives a first set of signal data from the first transceiver, the first set of signal data comprising data relating to a characteristic of the first set of wireless signals; the computer server generates a baseline signal profile for wireless communication from the second transceiver to the first transceiver, the baseline signal profile comprising the first set of signal data when a human body is not present in the detection area based at least in part on a characteristic of the first set of wireless signals in the first transceiver receives a second set of wireless signals from the second transceiver when the first plurality of human bodies are present in the detection area, the first plurality of human bodies having a total mass; the computer server receives a second set of signal data from the first transceiver, the second set of signal data comprising data relating to a characteristic of the second set of wireless signals; The computer server generates a second baseline signal profile for wireless communication from the second transceiver to the first transceiver, wherein the second baseline signal profile is configured when the first plurality of human bodies are present in the detection area. based at least in part on a characteristic of the second set of wireless signals in the second set of signal data; the first transceiver receives a third set of wireless signals from the second transceiver when a second plurality of human bodies are present within the detection area; the computer server receives a third set of signal data from the first transceiver, the third set of signal data comprising data relating to a characteristic of the third set of wireless signals; wherein the computer server estimates the total mass of the second plurality of anatomy, wherein the estimation determines a characteristic of the third set of radio signals in the third set of radio signals from the first baseline signal profile and the second set of radio signals. 2 based at least in part on the comparison to the baseline signal profile; The computer server estimates the total number of anatomy in the plurality of anatomy based, at least in part, on dividing the estimated total mass of the plurality of anatomy by an average mass per anatomy.
In a further embodiment, the method comprises: the computer server estimating the total mass of the second plurality of anatomy with a characteristic of the third set of radio signals in the signal data of the third radio set and the second set based at least in part on a comparison of a characteristic of the second set of wireless signals in the wireless signal data of
In a further embodiment, the method includes a radio network signal protocol characteristic determined by the first transceiver, wherein the characteristics of the first set of wireless signals, the second set of wireless signals, and the third set of wireless signals include a wireless network signal protocol characteristic determined by the first transceiver. .
In a further embodiment, the method includes wherein each of the wireless network signal protocol characteristics is selected from the group consisting of a received signal strength, a latency, and a bit error rate.
In a further embodiment, the method comprises interpolating the total mass of an anatomy in the second plurality of anatomy for the computer server to estimate.
In a further embodiment, the method wherein the interpolating uses an assumed mass of zero for the baseline signal profile and a total mass of the first plurality of anatomy for the second baseline signal profile.
In a further embodiment, the method comprises wherein the total mass is a discrete user-supplied quantity.
In a further embodiment, the method comprises wherein the average mass per human body is a discrete user supply.
In a further embodiment, the method includes: the computer server stores a plurality of historical data records indicating whether a human body has been present in a detection area for a certain period of time, each of the historical data records includes: the number of human bodies detected in the detection area; each of the number of the human body includes an indication of the date and time detected in the detection area; and the computer server making at least a portion of the plurality of historical data records available to one or more external computer systems via an interface.
In a further embodiment, the method, wherein the computer server estimating the total mass of the second plurality of anatomy is adjusted based on machine learning comprising: having a fiducial element in the detection area determining a first sample total mass of a plurality of anatomy, wherein the first sample mass is determined based on detection of the fiducial element; determining a second sample total mass of the plurality of anatomy in the detection area, the second sample mass being determined based at least in part on a comparison of the baseline signal profile with a second set of received signal data , determining the second sample total mass; comparing the first sample mass and the second sample mass; and adjusting an estimate of the total mass of the second plurality of anatomy based on the comparison.
In a further embodiment, the method includes: the computer server estimating the total mass of the second plurality of anatomy is adjusted based on machine learning comprising: determining a first sample total mass; determining a second sample total mass of a plurality of anatomy in the detection region, wherein the second sample mass is determined based at least in part on a comparison of the baseline signal profile to a second set of received signal data; determining the second sample total mass; comparing the first sample mass and the second sample mass; and adjusting the determination of the second sample mass based on the comparison.
In a further embodiment, the method includes: determining, by the computer server, a first sample location of the human body based on inference in the detection area, wherein the human body is near the network element from the detected operation of the network element in the detection area. infer that it is within the detection region of
In a further embodiment, the method comprises wherein the network component is a component of an electrical system, a lighting system, a heating, ventilation and cooling (HVAC) system, a security system, or an industrial automation system.
In a further embodiment, the method comprises a confidence metric to estimate the total number of anatomy in the plurality of anatomy.
In a further embodiment, the method includes the first transceiver and the second transceiver being configured to automatically calculate a relative position within the detection area.
In a further embodiment, the method includes the wireless communication using a protocol selected from the group consisting of Bluetooth, Bluetooth low energy, ANT, ANT+, WiFi, Zigbee, Thread, and Z-Eve.
In a further embodiment, the method includes wireless communication from the second transceiver to the first transceiver having a carrier frequency in a range encompassing 850 MHz to 17.5 GHz.
While the present invention has been disclosed with a description of specific embodiments, including those presently believed to be preferred, the detailed description is for the purpose of explanation and should not be construed as limiting the scope of the disclosure. As will be understood by one of ordinary skill in the art, embodiments other than those specifically described herein are encompassed by the present invention. Modifications and variations of the described embodiments may be made without departing from the spirit and scope of the present invention.

Claims (75)

A method for detecting the presence of a human body, the method comprising:
providing a first transceiver disposed at a first location within the detection area; and
providing a second transceiver disposed at a second location within the detection area;
including,
a computer server communicatively coupled to the first transceiver;
the first transceiver receives a first set of wireless signals from the second transceiver when a human body is present in a first position within the detection area;
the computer server receives a first set of signal data from the first transceiver, the first set of signal data comprising data relating to a characteristic of the first set of wireless signals;
the computer server generates a baseline signal profile for wireless communication from the second transceiver to the first transceiver, wherein the baseline signal profile is configured to include the first based at least in part on a characteristic of the first set of wireless signals of the set of signal data;
the human body moves from the first position to a second position in the detection area;
the first transceiver receives a second set of wireless signals from the second transceiver when the human body is in the second position;
the computer server receives a second set of signal data from the first transceiver, the second set of signal data comprising data relating to a characteristic of the second set of wireless signals, and
the computer server determines whether the position of the human body in the detection area has changed, the determining comprising at least comparing characteristics of the second set of wireless signals in the second set of signal data to the baseline signal profile based in part,
Way. The method of claim 1,
A characteristic of the first set of radio signals is:
and a wireless network signal protocol characteristic determined by the first transceiver. 3. The method of claim 2,
Each of the wireless network signal protocol characteristics,
a method selected from the group consisting of received signal strength, latency, and bit error rate. The method of claim 1,
The human body moves to a new location in the detection area,
the first transceiver receives a new set of radio signals from the second transceiver when the human body is present in the detection area at the new location;
the computer server receives a new set of signal data from the first transceiver, the new set of signal data comprising data relating to characteristics of the new set of radio signals;
the computer server replaces the baseline signal profile with a new baseline signal for wireless communication from the second transceiver to the first transceiver when the human body is in the new position, wherein the second baseline signal profile is and generating when a human body is present in the detection area at the new location based at least in part on a characteristic of the new set of wireless signals of the new set of signal data. 5. The method of claim 4,
before the computer server replaces the baseline signal profile with the new baseline signal, the computer server further comprises storing a historical data record of the baseline signal profile. 6. The method of claim 5,
The stored history data record indicates that the human body was detected in the detection area, and includes a date and time when the human body was detected in the detection area. 5. The method of claim 4,
repeating the moving, receiving and replacing one or more times. According to claim 1,
When the computer server determines whether the position of the human body has changed in the detection area,
determining a first sample location of an anatomy having a fiducial element in the detection area, wherein the first sample location is determined based on detection of the fiducial element; ,
determining a second sample location of the anatomy in the detection area, the second sample location being determined based at least in part on a comparison of the received second set of signal data and the baseline signal profile determining a second sample location;
comparing the first sample location to the second sample location, and
adjusting the determination of the second sample location based on the comparison.
A method that is tuned based on machine learning comprising: The method of claim 1,
When the computer server determines whether the position of the human body has changed in the detection area,
determining a location of a first sample in the human body based on the inference of the detection area;
determining a second sample location of the anatomy in the detection area, the second sample location being determined based at least in part on a comparison of the received second set of signal data and the baseline signal profile determining a second sample location;
comparing the first sample location to the second sample location, and
adjusting the determination of the second sample location based on the comparison.
A method adapted based on machine learning comprising: 10. The method of claim 9,
Determining the location of the first sample of the human body based on the inference in the detection area comprises:
the computer infers from the detected operation of a network element in the detection area that a human body is present in the detection area near the network element.
A method comprising 11. The method of claim 10,
The network element is
electrical system; lighting system; heating, ventilation and cooling (HVAC) systems; A method that is a component of a security system or industrial automation system. The method of claim 1,
The computer server stores a plurality of historical data records indicating whether a human body has changed positions in the detection area over a period of time, and each of the history data records includes the number of human bodies detected in the detection area and the number of human bodies detected in the detection area. each of the numbers comprises an indication of the date and time it was determined that the location was changed in the detection area, and
and the computer server makes available at least a portion of the plurality of historical data records to one or more external computer systems via an interface. The method of claim 1,
wherein the computer server is operatively coupled to the second system, and only after the computer server determines that a human body is present in the detection area, the computer server operates the second system. 14. The method of claim 13,
The first transceiver and the second system,
configured to communicate using the same communication protocol. 14. The method of claim 13,
The second system is
electrical system; lighting system; heating, exhaust and cooling (HVAC) systems; security system; and industrial automation systems. According to claim 1,
The wireless communication is
A method using a protocol selected from the group consisting of Bluetooth, Bluetooth Low Energy, ANT, ANT+, WiFi, ZigBee, Thread and Z-wave. The method of claim 1,
and the wireless communication from the second transceiver to the first transceiver has a carrier frequency in a range encompassing 850 MHz to 17.5 GHz. The method of claim 1,
wherein the computer determining whether the position of the anatomy has changed in the detection area comprises a confidence metric. The method of claim 1,
The first transceiver and the second transceiver,
and automatically calculate a relative position within the detection area. The method of claim 1,
The first transceiver and the second transceiver,
and automatically define a detection area comprising the first transceiver and the second transceiver. A method for detecting the presence of a human body, the method comprising:
providing a first transceiver disposed at a first location within the detection area; and
providing a second transceiver disposed at a second location within the detection area;
including,
a computer server communicatively coupled to the first transceiver;
the first transceiver receives a first set of wireless signals from the second transceiver;
The computer server,
receive a first set of signal data from the first transceiver, the first set of signal data comprising data relating to characteristics of the first set of wireless signals;
estimating that the first set of signal data is indicative of the presence of a human body in the detection area;
for wireless communication from the second transceiver to the first transceiver based at least in part on a characteristic of the first set of wireless signals in the first set of signal data when a human body is estimated to be present in the detection area; generate a detection signal profile,
The first transceiver,
receive a second set of wireless signals from the second transceiver;
The computer server,
receive a second set of signal data from the first transceiver, the second set of signal data comprising data relating to characteristics of the second set of wireless signals;
estimating that the second set of signal data indicates the absence of any human body in the detection area;
wireless communication from the second transceiver to the first transceiver based at least in part on a characteristic of the second set of wireless signals in the second set of signal data when the absence of any human body within the detection area is inferred create a baseline signal profile for
The first transceiver,
receive a third set of wireless signals from the second transceiver;
The computer server,
receive a third set of signal data from the first transceiver, the third set of signal data comprising data relating to characteristics of the third set of wireless signals;
determine whether the third set of signal data indicates the presence or absence of any human body in the detection area;
The decision is
based at least in part on a comparison of the detection signal profile and the baseline signal profile to the third set of signal data. 22. The method of claim 21,
in inferring that the first set of signal data is indicative of the presence of a human body in the detection area, wherein the inference includes a further set of signal data for signals received by the first transceiver from other transceivers in the detection area based at least in part on and
In inferring that the second set of signal data is indicative of the absence of any anatomy in the detection area, the inference is based on an additional set of signals received by the first transceiver from other transceivers in the detection area. based at least in part on signal data. 22. The method of claim 21,
The computer server stores a plurality of historical data records indicating whether a human body has been determined to exist in the detection area for a certain period of time, and each of the historical data records includes the number of human bodies determined to exist in the detection area and the human body includes an indication of the date and time each of the numbers of ? was determined to be present in the detection area; and
and the computer server makes available at least a portion of the plurality of historical data records to one or more external computer systems via an interface. 22. The method of claim 21,
the computer server is operatively coupled to a second system, and
and only after the computer server determines that a human body is present in the detection area, the computer server operating the second system. 25. The method of claim 24,
The first transceiver and the second system,
configured to communicate using the same communication protocol. 25. The method of claim 24,
The second system is
electrical system; lighting system; heating, exhaust and cooling (HVAC) systems; security system; A method selected from the group consisting of industrial automation systems and electrical systems. 22. The method of claim 21,
The wireless communication is
A method using a protocol selected from the group consisting of Bluetooth, Bluetooth Low Energy, ANT, ANT+, WiFi, Zigbee, Thread, and Z-Wave. 22. The method of claim 21,
and the wireless communication from the second transceiver to the first transceiver has a carrier frequency in a range encompassing 850 MHz to 17.5 GHz. 22. The method of claim 21,
wherein the computer server determining whether the third set of signal data is indicative of the presence of a human body comprises a confidence metric. 22. The method of claim 21,
The first transceiver and the second transceiver,
and automatically calculate a relative position within the detection area. In the method of estimating the number of human body present in the area,
providing a first transceiver disposed at a first location within the detection area; and
providing a second transceiver disposed at a second location within the detection area;
including,
a computer server communicatively coupled to the first transceiver;
the first transceiver receives a first set of radio signals from the second transceiver when a human body is not present within the detection area;
the computer server receives a first set of signal data from the first transceiver, the first set of signal data comprising data relating to a characteristic of the first set of wireless signals;
the computer server generates a first baseline signal profile for wireless communication from the second transceiver to the first transceiver, wherein the first baseline signal profile is configured to include the first baseline signal profile when no human body is present in the detection area. based at least in part on a characteristic of the first set of wireless signals in the set of signal data;
the first transceiver receives a second set of wireless signals from the second transceiver when the first plurality of human bodies are present in the detection area, the first plurality of human bodies has a total mass;
the computer server receives a second set of signal data from the first transceiver, the second set of signal data comprising data relating to a characteristic of the second set of wireless signals;
wherein the computer server generates a second baseline signal profile for wireless communication from the second transceiver to the first transceiver, wherein the second baseline signal profile is configured when the first plurality of human bodies are present in the detection area. based at least in part on a characteristic of the second set of wireless signals in the second set of signal data;
the first transceiver receives a third set of wireless signals from the second transceiver when a second plurality of human bodies are present within the detection area;
the computer server receives a third set of signal data from the first transceiver, the third set of signal data comprising data relating to a characteristic of the third set of wireless signals;
wherein the computer server estimates a total mass of the second plurality of anatomy, wherein the estimation determines a characteristic of the third set of wireless signals in the third set of signal data, the first baseline signal profile and the second based at least in part on the comparison to the baseline signal profile,
and the computer server estimating a total anatomical number in the second plurality of anatomy based at least in part on dividing the estimated total mass of the second plurality of anatomy by an average mass per anatomy. 32. The method of claim 31,
estimating the total mass of the second plurality of human bodies by the computer server,
based at least in part on a comparison of a characteristic of the third set of wireless signals in the third set of signal data and a characteristic of the second set of wireless signals in the second set of signal data. 32. The method of claim 31,
characteristics of the first set of wireless signals, the second set of wireless signals, and the third set of wireless signals,
and a wireless network signal protocol characteristic determined by the first transceiver. 34. The method of claim 33,
Each of the wireless network signal protocol characteristics is:
a method selected from the group consisting of received signal strength, latency, and bit error rate. 32. The method of claim 31,
What the computer server estimates is,
and interpolating a total mass of anatomy in the second plurality of anatomy. 36. The method of claim 35,
The interpolation is
using the hypothesized mass zero for the second baseline signal profile and the total mass of the first plurality of anatomy for the second baseline signal profile. 32. The method of claim 31,
wherein the total mass is a discrete user-supplied quantity. 32. The method of claim 31, wherein the average mass per human body is a discrete user supply. 32. The method of claim 31,
The computer server stores a plurality of historical data records indicating whether or not a human body has been present in a detection area for a certain period of time, and each of the history data records includes the number of human bodies detected in the detection area and the number of human bodies detected in the detection area, respectively. an indication of the date and time detected in the area; and
and the computer server makes available at least a portion of the plurality of historical data records to one or more external computer systems via an interface. 32. The method of claim 31,
estimating the total mass of the second plurality of human bodies by the computer server,
determining a first sample total mass of a plurality of anatomy having a fiducial element in the detection area, the first sample total mass being determined based on detection of the fiducial element;
determining a second sample total mass of the plurality of anatomy in the detection area, the second sample total mass being determined based at least in part on a comparison of the baseline signal profile with a second set of received signal data; ,
comparing the first sample total mass and the second sample total mass; and
adjusting an estimate of the total mass of the second plurality of anatomy based on the comparison;
A method adapted based on machine learning comprising: 32. The method of claim 31,
estimating the total mass of the second plurality of human bodies by the computer server,
determining a total mass of a first sample of a plurality of anatomy based on inferences in the detection region;
determining a second sample total mass of a plurality of anatomy in the detection region, the second sample total mass being determined based at least in part on a comparison of the baseline signal profile with a second set of received signal data;
comparing the first sample total mass and the second sample total mass; and
adjusting the determination of the second sample total mass based on the comparison.
A method adapted based on machine learning comprising: 22. The method of claim 21,
Determining the location of the first sample of the human body based on the inference in the detection area comprises:
and the computer server infers from a detected operation of a network element in the detection area that a human body is present in the detection area near the network element. 43. The method of claim 42,
The network element is
A method that is a component of an electrical system, a lighting system, a heating, ventilation and cooling (HVAC) system, a security system, or an industrial automation system. 32. The method of claim 31,
wherein estimating the total number of anatomy in the plurality of anatomy comprises a confidence metric. 32. The method of claim 31,
The first transceiver and the second transceiver,
and automatically calculate a relative position within the detection area. 32. The method of claim 31,
The wireless communication is
A method using a protocol selected from the group consisting of Bluetooth, Bluetooth Low Energy, ANT, ANT+, WiFi, Zigbee, Thread, and Z-Wave. 32. The method of claim 31,
and the wireless communication from the second transceiver to the first transceiver has a carrier frequency in a range encompassing 850 MHz to 17.5 GHz. A method for detecting the presence of an object affecting an RF signal, the method comprising:
providing a first transceiver disposed at a first location within the detection area; and
providing a second transceiver disposed at a second location within the detection area;
including,
a computer server communicatively coupled to the first transceiver;
the first transceiver receives a first set of radio signals from the second transceiver when an object affecting the RF signal is present within the detection area at a first location;
the computer server receives a first set of signal data from the first transceiver, the first set of signal data comprising data relating to a characteristic of the first set of wireless signals;
the computer server generates a baseline signal profile for wireless communication from the second transceiver to the first transceiver, wherein the baseline signal profile determines that the object affecting the RF signal is located in the detection area at the first location based at least in part on a characteristic of the first set of wireless signals of the first set of signal data when present in
the object moves from the first position to a second position in the detection area;
the first transceiver receives a second set of wireless signals from the second transceiver when the object affecting the RF signal is present in the second location;
the computer server receives a second set of signal data from the first transceiver, the second set of signal data comprising data relating to a characteristic of the second set of wireless signals, and
The computer server determines whether the position of the object affecting the RF signal in the detection area has changed, the determination is based on a characteristic of the second set of radio signals in the second set of signal data and the baseline based at least in part on the comparison of the signal profiles. 49. The method of claim 48,
A characteristic of the first set of radio signals is:
and a wireless network signal protocol characteristic determined by the first transceiver. 50. The method of claim 49,
Each of the wireless network signal protocol characteristics,
a method selected from the group consisting of received signal strength, latency, and bit error rate. 49. The method of claim 48,
wherein the object affecting the RF signal comprises a weapon. 49. The method of claim 48,
the object affecting the RF signal moves to a new location in the detection area,
the first transceiver receives a new set of radio signals from the second transceiver when the object affecting the RF signal is present in the detection area at the new location;
the computer server receives a new set of signal data from the first transceiver, the new set of signal data comprising data relating to characteristics of the new set of radio signals;
the computer server replaces the baseline signal profile with a new baseline signal for wireless communication from the second transceiver to the first transceiver when the object affecting the RF signal is in the new location; wherein the baseline signal profile is generated based at least in part on a characteristic of the new set of radio signals of the new set of signal data when the object affecting the RF signal is present in the detection area at the new location. , Way. 53. The method of claim 52,
before the computer server replaces the baseline signal profile with the new baseline signal, the computer server further comprises storing a historical data record of the baseline signal profile. 54. The method of claim 53,
wherein the stored historical data record indicates that the object affecting the RF signal was detected in the detection area, and includes a date and time at which the object was detected in the detection area. 53. The method of claim 52,
repeating that the object affecting the RF signal moves, the computer server receives, and the computer server is replaced at least twice. 49. The method of claim 48,
determining, by the computer server, whether the position of the object affecting the RF signal in the detection area has changed,
determining a first sample location of an object affecting an RF signal having a fiducial element in the detection area, the first sample location being determined based on detection of the fiducial element;
determining a second sample location of the object affecting an RF signal in the detection area, the second sample location being the baseline signal profile not using the received second set of signal data and the fiducial element determined based at least in part on a comparison of -,
comparing the first sample location to the second sample location, and
adjusting the determining step of the second sample position to improve a position calculating capability of the system, wherein the adjusting is based on a comparison between the first determination and the second determination;
A method adapted based on machine learning comprising: 49. The method of claim 48,
and existing historical recording data of the first set of signal data and the second set of signal data are retrieved from a server and adjusted to allow use of the historical recording in different operating environments. 49. The method of claim 48,
wherein the object affecting the RF signal comprises a metal. 49. The method of claim 48,
The object is
A method that causes a measurable change in a signal metric due to one or more phenomena from the list of reflection, refraction, diffraction, scattering, and absorption. 49. The method of claim 48,
wherein the object affecting the RF is a vehicle.

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