KR20090073529A - Position recognition and user identification system using signal strength map in home healthcare - Google Patents

Abstract

A position recognition and user identification system for home healthcare using a signal strength map is provided to track the location of an old resident through a wireless sensor network. A bio-signal detection unit(10) detects the bio-signal of a user. If a user sensor unit(100) approaches within the range of a predetermined area, the bio-signal detection unit recognizes the identification of the user sensor unit. The bio-signal detection unit transmits the bio-signal to a monitoring unit(400) through a sync unit(300). The monitoring unit receives the ID of the user sensor unit, the ID of a router unit(200), and an RSSI(Received Signal Strength Indicator) between the router unit and user sensor unit from the sync unit. By computing the received IDs and RSSI after data parsing, the monitoring unit determines the location of the user.

Description

Position recognition and user identification system using signal strength map in home healthcare}

The present invention relates to a home medical location recognition and user identification system using a wireless sensor network-based signal strength map, and more specifically, to recognize a resident's location and automatically measure a biosignal, and by using a received signal strength (RSSI). The present invention relates to an in-home medical location and user identification system that is programmable in the sensor module and has higher accuracy.

With the recent increase in the elderly population, the number of elderly people who live alone at home during the day without family protection is increasing, and there are increasing cases of accidents and sudden death at home. Therefore, a monitoring system for tracking the location of elderly residents indoors is required as a method of preventing this, and in particular, the monitoring system is desired to monitor the location of residents using a wireless sensor network so as not to inconvenience the user. .

Wireless Sensor Networks (WSNs) refers to the collective use of information gathered from a network of sensors installed in an unspecified space around a wireless sensor field concept. This allows users to freely use information and communication services beyond the limitations of existing wired network services, and has been applied in many fields by utilizing ubiquitous networks and various sensor devices. In particular, based on this, situational awareness and positional awareness are being actively researched, and in the medical field, it is necessary to monitor the current state of patients and automatically update electronic medical records when seeking the location of medical equipment and equipment or patients. Applicable

In the prior art, in the field of ubiquitous computing, research on location recognition in a limited space has been mainly conducted.

Looking at the example of the position recognition system in the conventional ubiquitous computing field, AT & T's Active Badge using infrared is a method of detecting the position of the user in the building by finding the position of the active badge, ultrasonic and RF Microsoft's Radar using MIT's Cricket and Received Signal Strngth Indicator (RSSI) for wireless local area network (WLAN) uses IEEE.802.11 wireless networking technology. It is the location recognition and tracking system of the building area. There is also Ubisenserk's Ubisenserk, which uses Ultra wide-band (UWB) signals.

In the conventional system using RSSI, the conventional time of arrival (TOA) algorithm in the field of location recognition and tracking requires separate hardware for measuring the time between the receiver and the transmitter. The angle of arrival (AOA) algorithm also requires an additional antenna or array type antenna for measuring the angle of the received signal.

Accordingly, the present invention provides a location recognition system using a signal strength map of a location based service (LBS) method based on a wireless sensor network.

An object of the present invention is to provide a home medical location recognition and user identification system using a signal strength map of a location based service (LBS) method based on a wireless sensor network.

Another problem to be solved by the present invention is a user identification system that can recognize the location of the occupants and automatically measure the bio-signal using RSSI as a programmable and sensory home medical location recognition and higher accuracy in the sensor module and It is to provide a user identification system.

Another problem to be solved by the present invention is to provide a home medical location and user identification system having an efficient network in real time by applying a multi-hop routing method using a LBS star-mesh network.

Another problem to be solved by the present invention is to monitor the resident location by providing the user's location with the bio-signal information such as pulse, blood sugar, body temperature, blood oxygen concentration (SpO2), electrocardiogram, etc. Through this, it is possible to prevent sudden death and accidents, and to provide resident-based home-based location recognition and user identification system that can provide quick emergency services.

According to an embodiment of the present invention for achieving the above object, the home medical care location recognition and user identification system using the signal strength map is a user main module to which a unique ID (ID of the user sensor unit) is assigned. The user sensor unit is used to carry or mounted to the user; A router unit mounted in a building, having a unique ID (router ID) and receiving the user sensor unit ID from a user sensor unit; A sink unit for receiving an ID of the user sensor unit, an ID of the router unit, and an RSSI between the user sensor unit and the router unit from the router unit; And a monitoring unit for receiving the ID of the user sensor unit, the unique ID of the router unit, the RSSI between the user sensor unit and the router unit from the sink unit, and parsing the data to determine the location of the user. It features.

According to another embodiment of the present invention for achieving the above object, the home medical location recognition and user identification system using the signal strength map, the signal strength map including at least a user sensor unit, router unit, monitoring unit In the at-home medical location recognition and user identification system used, the user sensor unit comprises a plurality, each user sensor unit constitutes a peer to peer (P2P) network, and each user sensor unit has a unique ID (ID of the user sensor unit). Granted; The router unit is composed of a plurality of mesh networks for accessing from four user sensor units in one router unit, each router unit having a unique ID (router ID); The sink unit includes one, and all router units request a connection to the sink unit centered on one sink unit, and the sink unit is formed of a star network that controls the connection of all router units that allow the connection; The monitoring unit may receive an ID of a user sensor unit, a unique ID of a router unit, an RSSI between the user sensor unit and the router unit from the sink unit, parse the data, and then calculate and process the data to determine the location of the user. It is characterized by.

The home medical location recognition and user identification system using the signal intensity map is mounted on an object in a room, and when a person equipped with a user sensor unit approaches within a certain area, the unique ID of the user sensor unit is recognized and biosignal measurement is performed accordingly. It further comprises a bio-signal detection unit.

The biosignal detection unit may transmit the detected biosignal to the monitoring unit through the sink unit.

The biosignal detection unit may transmit the detected biosignal to a monitoring unit through a user sensor unit, a router unit, and a sink unit.

The biosignal detection unit may transmit the detected biosignal to a monitoring unit through a router unit and a sink unit.

The biosignal detection unit detects at least one of a pulse, a blood sugar, a body temperature, a blood oxygen concentration (SpO2), an electrocardiogram, a weight, and a blood pressure.

The biosignal detection unit is mounted on at least one of a toilet, a bath, a bed, a desk, a computer, and a chair.

The home medical type location recognition and user identification system using the signal intensity map has a link quality indicator between a module carried and a module attached for distinguishing users when there is only one user of each measuring device in the biosignal detection site. If (LQI) is 0xF0 or more, the assigned user sensor ID is transmitted to the biosignal detection unit.

The user sensor unit or the router unit or the sink unit is characterized in that the nano system.

The user sensor unit or the router unit or the sink unit is characterized in that it comprises a Nano-24 main module, respectively.

The sink portion is characterized in that it further comprises a Nano-24 interface module.

The router is characterized in that mounted on the ceiling in the building.

The monitoring unit is characterized in that consisting of a computer.

The home medical location recognition and user identification system using the signal intensity map measures in advance the RSSI between the user sensor unit carried by the user in the specific area and the router unit attached to the ceiling in advance so that all the RSSIs measured at each point are measured. It is characterized by having a signal strength map (signal strength map) by the DB.

In order to distinguish the presence / absence of a user in each room using the signal strength map, a threshold value is provided so that the ID of the user is received only when the router part of the ceiling enters an area within a predetermined RSSI value.

The at-home medical location recognition and user identification system using the signal strength map uses a multi-hop routing method.

According to another embodiment of the present invention for achieving the above object, a home medical location recognition and user identification method using a signal strength map, the router unit receives the ID of the user sensor unit from the user sensor unit; step; A second step of the router unit having received the ID of the user sensor unit in the first step, transmitting an ID of the user sensor unit, an ID of the router unit, and an RSSI between the user sensor unit and the router unit to the sink unit; A third step of transmitting an ID of the user sensor unit, an ID of the router unit, and an RSSI between the user sensor unit and the router unit received from the second step from the sink unit to the monitoring unit; And a fourth step of determining a location of the user by performing a data processing after parsing the data in the third step.

The home medical location recognition and user identification method using the signal intensity map recognizes a unique ID of the user sensor unit when a person equipped with the user sensor unit approaches a certain area within the biosignal detection unit mounted on an object in a room. Characterized in that the biological signal of the.

According to another embodiment of the present invention for achieving the above object, the home medical location recognition and user identification method using the signal strength map, Signal strength map comprising at least a user sensor unit, router unit, monitoring unit In the at-home medical location recognition and user identification method using, The user sensor unit First step of detecting the MAC address of the sink by scanning the channel; A second step of initializing the user sensor unit of the IEEE 802.15.4 standard; A third step of initializing the nano-Q + driving the user sensor unit, which is a nano system; A fourth step of outputting an ID of the user sensor part; wherein the router part scans a channel to detect a MAC address of the sink; A second router step of initializing a router unit of an IEEE 802.15.4 standard; A third step of a router unit for storing the RSSI of the RF signal transmitted from the user sensor unit; A fourth step of a router unit connecting to receive data of a user sensor unit when the RSSI is equal to or larger than a preset threshold RSSI; A fifth step of the router unit for initializing the nano-Q + driving the router unit, which is a nano system; A sixth step of the router unit receiving an ID of the user sensor unit transmitted from the user sensor unit to the router unit; And a seventh step of the router unit transmitting the data and the router ID received from the router unit to the sink unit.

The sink unit scanning the channel to detect the MAC address of the sink; A sink unit second step of initializing a sink unit of an IEEE 802.15.4 standard; A third step of storing the RSSI of the RF signal transmitted from the router; A sink unit connecting to receive the data from the selected router unit by applying the threshold RSSI; A fifth step of sinking to initialize Nano-Q + for driving the sink, which is a nanosystem; A sixth step of receiving, by the sink unit, an ID of the user sensor unit transmitted from the router unit, an ID of the router unit, and an RSSI between the user sensor unit and the router unit; A seventh step of operating the sink to process the data received by the sink; A sink unit waiting for data reception to the sink to be completed; an eighth step;

A ninth step of identifying a user ID of the user sensor unit; a tenth step of transmitting a user ID of the identified user sensor unit to a biosignal detection unit located in a toilet or a bed or a bathroom; a position of a user according to an ID of the user sensor unit And a sink unit for recognizing; an eleventh step of transmitting the biometric data detected by the biosignal detection unit based on the unique ID of the user sensor unit through serial communication.

A first step in which the monitoring unit receives data to the monitoring unit by the occurrence of an event of serial data reception to the monitoring unit; A second step of monitoring unit generating all events by generating an event of a timer at an interval of 100 ms; After monitoring the data parsing (Parsing) in the monitoring unit to distinguish the presence or absence of each area and the monitoring unit to display the result of the third step; characterized in that consisting of.

The present invention provides an in-home medical location recognition and user identification system using a signal strength map of a location based service (LBS) method based on a wireless sensor network. Prevent accidents and sudden death at home, and also the present invention by using a wireless sensor network indoors to track the location of the resident of the elderly in the user's daily life without inconvenience and location tracking is possible in real time Do.

In addition, the present invention has the advantage that the sensor module can be programmed and the accuracy is high compared to the conventional method of using estimation and probability by using RSSI as a user identification system that can recognize the location of the occupants and automatically measure the biological signal. .

In addition, the home medical location and user identification system of the present invention has the most efficient network in real time by applying the multi-hop routing method using the LBS-type star-mesh network.

The RSSI algorithm method of the present invention compares the power between the receiver and the transmitter to estimate the distance, and applies a threshold RSSI that meets the purpose of ultralight, low power, and portability of the sensor network. It is designed to measure the data of RSSI before using it in a specific environment. Based on this, the signal intensity map is used as a database to estimate the distance from the measured values.

The threshold RSSI of 0xF0 applied to user identification is appropriate because the user must be limited to one person when measuring bio signals in bathtubs, beds, and toilets. In addition, when estimating the position of the user, the threshold RSSI was used according to the specific position through experiments by dividing each zone of the estimation space into 1 × 1m.

The reason for applying RSSI in the present invention can be summarized in two ways. First, the general RF signal path depends on the environment and obstacles that interfere with the signal. For example, due to diffraction and reflection of RF signals by household appliances and furniture in the home, RSSI measured over distance is nonlinear.

Secondly, according to the conventional method, the memory capacity of the sensor module is very small, and thus a complicated calculation process and arithmetic algorithm cannot be implemented. However, the method using the signal strength map as in the present invention is very efficient in terms of implementation memory and calculation process.

The existing Radio Telephony Mobile System (RTMS) has a real-time advantage in location tracking when compared to LBS, but it is much more inefficient compared to the amount of data increase on the network as the number of users increases. Therefore, the present invention introduces a method of identifying the position of the moving object based on whether the moving object to be tracked exists in a predetermined space, and the more precisely the nodes are arranged, the higher the accuracy of the estimation is. That is, the present invention has an efficient network in terms of real time by applying a multi-hop routing method using a mixed LBS star-mesh network.

In addition, since there is a problem of selection when two or more users exist at the router boundary, in the present invention, a space is applied to each zone in a grid. Confirmed. In addition, the radio wave intensity and radio wave arrival time or arrival angle are not always constant and change with time, and when the device in the stationary state starts to move, the error becomes larger, so that the present invention applies a threshold RSSI suitable for a given environment. The board was adapted to the LBS method.

The resident-based at-home location recognition and user identification system implemented in the present invention provides biosignal information such as pulse, blood sugar, body temperature, blood oxygen concentration (SpO2), electrocardiogram, etc. of the members of the family through user identification and the user's location By monitoring the location of residents can prevent sudden death, accidents, etc., it can be expected to provide fast emergency services.

Hereinafter, a configuration and operation of a home medical location recognition and user identification system using a wireless sensor network-based signal strength map according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram illustrating a schematic configuration of a home medical location recognition and user identification system according to an exemplary embodiment of the present invention. The biosignal detection unit 10, the user sensor unit 100, and a router are illustrated in FIG. The unit 200, a sink (Pan-Coordinator) unit 300, and a monitoring unit 400 are provided.

The biosignal detection unit 10 is mounted on a toilet, a bathtub, a bed, a desk, a computer, a chair, and the like, and is a measuring device that detects the biosignal of the user. When approaching, the unique ID of the user sensor unit 100 is recognized and the biosignal is measured accordingly and transmitted to the monitoring unit 400 through the sink unit 300. Alternatively, the biosignal detected by the biosignal detection unit 10 may be directly transmitted to the monitoring unit 400 by wire or wirelessly, and in some cases, the user sensor unit 100, the router unit 200, and the sink unit 300. May be transmitted to the monitoring unit 400. Alternatively, the biosignal detected by the biosignal detection unit 10 may be transmitted to the monitoring unit 400 via the router 200 and the sink 300. The biosignal detection unit 10 detects biosignal information such as pulse rate, blood sugar, body temperature, blood oxygen concentration (SpO2), electrocardiogram, weight, and blood pressure. In other words, the biosignal detection unit 10 may be mounted on a toilet, a bath, a bed, a desk, a computer, a chair, or the like, and automatically measure the biosignals comfortably when the user uses them. For example, assuming that the user of each instrument of the biosignal detection unit 10 is one, the ID assigned when the link quality indicator (LQI) between the portable module and the attached module is greater than 0xF0 for user identification. Can be implemented to be transmitted.

The user sensor unit 100 is a sensor node to which a unique ID is assigned, that is, a user main module to which a unique ID is assigned. The user sensor unit 100 may be formed in plurality in some cases, and each user sensor unit 100 forms a peer to peer (P2P) network.

The user sensor unit 100 may use, for example, a Nano-24 main module developed by the Korea Institute of Information and Communication Sciences (ETRI). Nano-24 is a kit for sensor network development based on ubiquitous environment. CPU is Atmega128L of RISC structure, supports In-System reprogammable (ISR) based flash memory, internal SRAM, EEPROM, internal and external SDRAM, and main module. And interface module. Nano-24 main module has MCU (Atmega128L) and RF transmitting / receiving module (CC2420) using 2.4GHz frequency band to enable basic RF communication, drive OS, and Zigbee protocol and PCB antenna are small. The Nano-24 interface module is used by stacking with the Nano-24 main module or other sensor module. The program download, external power supply, and built-in MAX232 enable serial communication. Software for driving Nano system developed by Korea Information and Telecommunications Research Institute (ETRI) is Nano-Q +, and it is a sensor network operating system including Simple Scheduler, TimedScheduler, Premption Scheduler, RF Driver, Serial Driver, and ADC Driver. Also, the kernel for implementing Nano system can use Nano-qplusn 1-5.le version.

The router 200 has a unique ID as a router mounted in a building, and recognizes the user sensor unit 100 to sink the unique ID of the user sensor unit 100 and the RSSI of the user sensor unit 100. 300). That is, the information received from the user sensor unit 100 by the router unit 200 is an ID of the user sensor unit 100, and the information to be transmitted from the router unit 200 to the sink unit 300 is the user sensor unit 100. ), The ID of the router 200, and the RSSI between the user sensor 100 and the router 200. Router unit 200 may be formed in plurality. The router 200 may use the Nano-24 main module, for example, the router 200 may be mounted on the ceiling of each room. One router unit 200 forms a mesh network of a net form accessible from four user sensor units 100.

The sink unit 300 receives an ID of the user sensor unit 100, an ID of the router unit 200, an RSSI between the user sensor unit 100 and the router unit 200 from the router unit 200, and the user sensor. The ID of the unit 100, the ID of the router 200, and the RSSI between the user sensor unit 100 and the router 200 are transmitted to the monitoring unit 400. The sink 300 may include a Nano-24 main module and a Nano-24 interface module. The information transmitted by each router unit 200 is received at the sink unit 300 in a 1: 1 manner, and between each router unit 200 and the sink unit 300 is a star network, that is, one sink unit 300. At the center, all nodes (router unit 200) request connection to the sink unit 300, and control the connection of all nodes that the sink unit 300 allows connection. Accordingly, the star-mesh network is composed of one sink 300, a plurality of routers 200, and a plurality of user sensor units 100 connected to the router 200.

The monitoring unit 400 receives a unique ID of the user sensor unit 100, a unique ID of the router unit 200, and an RSSI between the user sensor unit 100 and the router unit 200 from the sink unit 300. Then, the user's location is recognized by arithmetic processing from the received signal. The monitoring unit 400 may be made using a computer.

In the present invention, the location recognition method using the signal strength map (signal strength map) in advance by measuring the RSSI between the user sensor unit 100 and the router unit 200 attached to the ceiling in advance in the specific area by each user The RSSI measured at the point is DBized to construct a signal strength map. In order to distinguish the presence / absence of a user in each room by using the configured signal strength map, a threshold value is specified so that the ID of the user is received only when the router 200 of the ceiling enters an area within a specific RSSI value.

When the unique ID of the user sensor unit 100, the unique ID of the router unit 200, and the data format of the RSSI between the user sensor unit 100 and the router unit 200 are transferred to the monitoring unit 400, the monitoring unit 400 is provided. ) Parsing data in () to distinguish each region.

FIG. 2 is an explanatory diagram illustrating the biosignal detection unit of FIG. 1 and includes a biosignal sensing unit 11, a biosignal preprocessor 30, and a user transmitter 60.

The biosignal detection unit 10 of the present invention may detect biosignal information such as pulse, blood sugar, body temperature, blood oxygen concentration (SpO2), electrocardiogram, blood pressure, body temperature, and weight of a member of a family through user identification.

The biosignal sensing unit 11 detects biosignals from the human body. The biosignal sensing unit 11 includes an electrocardiogram detection sensor unit 12, an oxygen saturation detection sensor unit 14, a blood pressure detection sensor unit 16, a body temperature detection sensor unit 18, a blood sugar detection sensor unit 20, and a body weight. Detection sensor 22.

The ECG sensor 12 is composed of two ECG electrodes and one reference electrode to detect an ECG signal. The ECG sensor 12 may be mounted on a toilet, a desk, or the like.

The oxygen saturation detection sensor unit 14 includes light emitting means for emitting red light and near-infrared light and light detecting means for detecting the light transmitted or reflected by the light to detect the oxygen saturation. A light emitting diode may be used as the light emitting means, and a photosensor may be used as the light detecting means. Oxygen saturation detection sensor unit 14 may be mounted on a mobile phone, bed, desk.

The blood pressure detecting sensor unit 16 detects a blood pressure information signal from an optical blood flow detecting sensor including light emitting means and light detecting means. The blood pressure detecting sensor unit 16 may be mounted on a mobile phone, a bed, a desk, or the like.

The body temperature detection sensor unit 18 detects the body temperature from the temperature detection sensor. The body temperature detection sensor 18 may be made of a thermistor or the like. The body temperature sensor 18 may be mounted on a toilet, desk, mobile phone, or bed.

The blood sugar detecting sensor unit 20 is a sensor for detecting blood sugar and may use a skin contact type blood sugar detecting sensor or the like.

The weight detection sensor unit 22 is a sensor for detecting a weight, and may use a load cell or the like. Weight detection sensor unit 22 may be mounted on the toilet, bed, and the like.

The biosignal preprocessor 30 performs preprocessing to amplify the biosignals received from the biosignal sensing unit 11, remove noise, and convert the biosignals into digital signals. The biosignal preprocessor 30 includes an electrocardiogram signal preprocessor 32, an oxygen saturation signal preprocessor 34, a blood pressure signal preprocessor 36, a body temperature signal preprocessor 38, a blood sugar signal preprocessor 40, and a weight And a signal preprocessor 42.

The ECG signal preprocessor 32 receives the ECG signal from the ECG sensor 12, amplifies the signal, removes noise, and converts the ECG signal into a digital signal.

The oxygen saturation signal preprocessor 34 receives the oxygen saturation signal from the oxygen saturation detection sensor unit 14, amplifies, removes the noise, and converts the signal into a digital signal.

The blood pressure signal preprocessing unit 36 receives the blood pressure information signal from the blood pressure detecting sensor unit 16, amplifies it, removes noise, and converts it into a digital signal.

The body temperature signal preprocessing unit 38 receives the body temperature signal from the body temperature detection sensor unit 18, amplifies, removes noise, and converts it into a digital signal.

The user transmitter 160 converts the biosignals received from the biosignal preprocessing unit 30 into a signal that can be processed and transmitted to be transmitted directly to the monitoring unit 400 or the monitoring unit 400 through the user sensor unit 100. To send).

4 is an explanatory diagram for explaining the RF signal path of the LBS according to an embodiment of the present invention.

In the present invention, the sensor network is divided into a user sensor unit 100, a router unit 200, and a sink unit 300.

The user sensor unit 100 transmits only data and cannot receive and associate with other sensor units and routers, and corresponds to reduced function devices (RFDs). The router 200 may transmit, receive, or participate in another router unit or a sensor unit, may control a partial network, and serves as a coordinator for transmitting information collected from devices under control to a sink.

The sink unit 300 exists in a single network and can transmit, receive, and participate, but unlike the router unit 200, the sink unit 300 collects information collected in the entire network and controls the devices participating in the entire network. full function devices).

The network of the present invention may be referred to as a multi-hop routing scheme, in detail, a star-mesh network in which the star network of FIG. 3 (a) and the mesh network of FIG. 3 (b) are mixed. .

The mesh structure applied between the user sensor unit 100 and the router unit 200 can be restored when the link between nodes is broken, and each router unit 200 can exchange information with each other and grasp the state of the network. You can configure the best route in. In addition, since the indoor space is narrow and narrow, the router parts 200 exist in a close position where they can be connected to each other. The star structure between the router unit 200 and the sink unit 300 can designate the upper router, that is, the sink unit 300, which is simple in shape, and applies the threshold RSSI to the bottleneck such as data transmission delay and the entire network. This has the advantage of distributing traffic. The RF communication range of the sensor device measured here can be about 0 ~ 50m, and the multi-hop routing method that enables relay between data is applied to collect data over a wide range due to the distance limitation within 5m to maintain linearity. It was.

Each user sensor unit 100 transmits data while routing to the sink unit 300 which is a final destination while collecting sensor values. That is, the user carries the user sensor unit 100 and broadcasts it to the router unit 200 fixed to the room. Accordingly, all routers 200 can receive RF signals from four user sensor units 100, and each user sensor unit 100 transmits an assigned unique ID to the router unit 200. Therefore, 20 paths that can be transmitted from the user sensor unit 100 to the router unit 200.

The mesh network is a network form accessible from four user sensor units 100 to one router unit 200. Each user sensor unit 100 forms a peer-to-peer network and the router unit 200. The information received from is an ID of the user sensor unit 100 and the information to be transmitted from the router unit 200 to the sink unit 300 includes the ID of the router unit 200 and the user sensor unit 100 and the router unit 200. ) Is the RSSI between. The information transmitted by each router unit 200 is received at the sink unit 300 in a 1: 1 manner, and the sinks of each router are connected to a star network, that is, all nodes around the sink unit 300. Request a connection to the 300, and the sink unit 300 controls the connection of all nodes that allow the connection. Accordingly, the star-mesh network is composed of one sink 300, a plurality of routers 200, and a plurality of user sensor units 100 connected to the router 200.

When the router 200 transmits the same data to the sink 300, the data rate suddenly increases in the entire network. This can be solved by keeping the network connection intact during the data transition time between modules or by setting RSSI above a certain threshold to reduce the data volume of the entire network.

According to the flow of the network of the present invention, as shown in FIG. 2, four user sensor units 100 transmit unique IDs to the router unit 200, and five router units 200 transmit IDs transmitted from the user sensor unit 100. And, after receiving the RSSI between the user sensor unit 100 and the router unit 200, and transmits again to the sink unit 300 including the ID of the router unit 200. Finally, the data received by the sink unit 300 includes an ID of the user sensor unit 100, an ID of the router unit 200, an RSSI between the router units 200, and between the router unit 200 and the sink unit 300. RSSI. Therefore, the wireless sensor network of the present invention is composed of four user sensor units 100, five router units 200, and one sink unit 300. That is, the wireless sensor network of the present invention is composed of four sensors, five routers, and one sink.

In addition, in the present invention, when a user carries a user sensor unit assigned with a unique ID, and the user approaches the toilet, bathtub, or bed within a predetermined area, the assigned unique ID is transmitted to a module attached to the measuring instrument. The user can then automatically measure the bio-signals comfortably. It assumes one user of each instrument and implements to transmit assigned ID when LQI (link quality indicator) between portable module and attached module is more than 0xF0 for user identification.

In addition, in the present invention, a signal intensity map using RSSI measured in advance at a given place is applied by using a sensor conforming to the IEEE802.15.4 standard. In addition, analyzing the path loss in the RSSI method is important to increase the accuracy of the location. In RSSI measurement, multi-path fading, which causes phase distortion of a received signal when multiple signals are received, and the field of view between the transmitter and the receiver is not secured, the receiver cannot directly receive the signal sent by the transmitter and reflects it. In order to solve this problem, the present invention employs a signal strength map based method for constructing a database using RSSI measured in advance at a given location.

In general, the monitoring system can be classified into a real time monitoring system (RTMS) and a lactation based service (LBS). Among them, the RTMS is a real-time location monitoring method. The more routers, the higher the resolution. In addition, LBS knows whether a person exists in a given area and requires fewer nodes than RTMS. In the present invention, the position monitoring method is applied to the LBS method in real time except tracking.

5 is a schematic flowchart of a home medical location and user identification system according to an embodiment of the present invention.

First, an ID allocation process of the user sensor unit 100 which is a main module carried by a user is as follows. The user sensor unit 100 is a sensor that conforms to the IEEE 802.15.4 standard.

The MAC address of the sink is detected by scanning the channel (S110).

Initialize the user sensor unit 100 of the IEEE 802.15.4 standard (S120).

Initializes Nano-Q + driving the user sensor unit 100 which is a nano system (S130).

The ID of the user sensor unit 100 is transmitted (S140).

Next, an ID allocation process of the router 200 attached to the ceiling and an RSSI measurement process between the user sensor unit 100 and the router 200 will be described.

Scan the channel to detect the MAC address of the sink (S210).

The router unit 200 of the IEEE 802.15.4 standard is initialized (S220).

The RSSI of the RF signal transmitted from the user sensor unit 100 is stored (S230).

The threshold RSSI is applied to connect to receive data of the selected user sensor unit 100 (S240). In other words, it connects to transmit data from the user sensor unit 100, which is an RSSI equal to or greater than a preset threshold RSSI, to the router unit 200.

Initializes the Nano-Q + driving the router 200 which is a nano system (S250).

The data transmitted to the router 200 from the user sensor unit 100 is received (S260). In this case, the information received from the user sensor unit 100 in the router 200 is an ID of the user sensor unit 100.

The router 200 transmits the data and the router ID received from the router 200 to the sink 300 (S270). In this case, the information transmitted from the router unit 200 to the sink unit 300 includes an ID of the user sensor unit 100, an ID of the router unit 200, and an RSSI between the user sensor unit 100 and the router unit 200. to be.

Next, data transmission from the sink unit 300 to the data collection and monitoring unit of the entire network will be described.

The MAC address of the sink is detected by scanning the channel (S310).

The sink unit 300 of the IEEE 802.15.4 standard is initialized (S315).

The RSSI of the RF signal transmitted from the router 200 is stored (S320).

A threshold RSSI is applied to connect to the selected router 200 to receive data (S325). In other words, the router unit 200 connects to transmit data from the router unit 200, which is an RSSI equal to or greater than a preset threshold RSSI, to the sink unit 300.

Initializes the Nano-Q + driving the sink unit 300 which is a nano system (S330).

In operation S335, data transmitted from the router 200 to the sink 300 is received. In this case, the information transmitted from the router unit 200 to the sink unit 300 includes an ID of the user sensor unit 100, an ID of the router unit 200, and an RSSI between the user sensor unit 100 and the router unit 200. to be.

The data received by the sink unit 300 is processed (S340). In this case, the format includes a format for transmitting the received data to the monitoring unit 400 and transmitting the data to the monitoring unit 400. Also, the method may include determining whether there is additional data of the user according to the unique ID of the user sensor unit 100, that is, data (bio information data) measured by the biosignal detection unit 10.

Wait for the reception of data to be completed (S345). This includes waiting for the data transmission from the sink unit 300 to the monitoring unit 400 to be completed. In this case, the transmitted data is an ID of the user sensor unit 100, an ID of the router unit 200, and an RSSI between the user sensor unit 100 and the router unit 200. In addition, the biometric data of the user according to the unique ID of the user sensor unit 100 may be received here.

The following describes the process by which the user of the tub toilet bed is identified.

The ID of the user sensor unit 100 is identified (S350).

The ID of the identified user sensor unit 100 is transmitted to the biosignal detection unit 10 located in the toilet, bed, bathroom, or the like.

Recognize the position (S355). This includes recognizing the location of the user according to the ID of the user sensor unit 100 and recognizing the object or the biosignal detection unit 10 that the user is using or in proximity to.

Serial communication (S360). Here, communication with these objects or the biosignal detection unit 10 is included. In addition, the biosignal information detected by the biosignal detection unit 10 according to the unique ID of the user sensor unit 100 may be transmitted.

The following describes the program process of the monitoring unit 400 for position recognition in the monitoring unit 400.

Data is received by the monitoring unit 400 by the occurrence of an event of serial data reception by the monitoring unit 400 (S410). The data received here includes an ID of the user sensor unit 100 received from the sink unit 300, an ID of the router unit 200, an RSSI between the user sensor unit 100 and the router unit 200. And receiving biometric data detected by the biosignal detection unit 10.

An event of a timer is generated at an interval of 100 ms (S420). This maintains the network connection for the data transition time between modules. In other words, all information is updated every 100ms.

After parsing the data by the monitoring unit 400, the location of each area is distinguished and the result is displayed (S430). That is, when the ID of the user sensor unit 100, the ID of the router unit 200, and the data format of the RSSI between the user sensor unit 100 and the router unit 200 are transmitted to the monitoring unit 400, the monitoring unit 400 is provided. After parsing the data in the), we distinguish between the locations of each region.

In summary, as shown in FIG. 5, a unique ID of the user sensor unit 100 is broadcast from the user sensor unit 100 to the router unit 200.

The router 200 receives the ID transmitted from the user sensor unit 100 and the RSSI from the sensor unit 100, and whenever a RSSI data packet is received from the outside, the reception function is called by a timer / interrupt, After the delay, the synchro sensor ID, the router ID, and the RSSI value received from the user sensor are transmitted. Data received by the sink is transmitted to the monitoring unit (PC) at 384000bps. The transmitted data was processed by applying binary tree and double linked list using Microsoft C #, and implemented a real-time monitoring program that updates all information every 100ms.

6 is an explanatory diagram for explaining a star-mesh network that is a network implemented in the present invention.

The home medical location recognition and user identification system of the present invention applies a multi-hop routing method connected by sensors, routers, and sinks. It consists of a network between the sensor and the router and a network between the router and the sink, and the applied networks between the layers are different. That is, a star-mesh network method using a mesh network and a star network is constructed.

7 is an explanatory diagram for explaining user identification in a wireless sensor network in the present invention.

In FIG. 7, it is assumed that two users are represented.

First, a unique ID is assigned to the main module carried by the user.

Second. Attach a main module with a router or sink function to the toilet, bath, or bed.

Thirdly. Assuming that the toilet, bathtub and bed are used by one person, when the user approaches 7 cm, the ID of the module carried by the user is automatically transmitted to the module attached to the toilet, bathtub and bed.

Fourth, the ID transmitted to the module becomes a start signal for inducing automatic measurement of the toilet, bath, and bed measuring instrument, and measures the biosignal.

Fifthly, health care is performed by distinguishing users and monitoring the measured bio signals accordingly.

In the location-based service using the signal strength map of the present invention, when the user goes to the toilet, bath, bed with a module that can be automatically controlled on / off of the instrument and the bio-signal information according to the home health care server It is transmitted to the user, and when the user moves around the house, it is possible to analyze and diagnose an individual's biosignal according to a specific location. Therefore, this is called LBS (Location Based Service).

8 is an example of RSSI measured between two nodes up to 1-5m and 0-330 degrees in the present invention.

8 is a result obtained with a PCB antenna in free space, assuming 0 degrees when facing the receiver and transmitter. The receiver was fixed and the transmitter was rotated at 0 ° to 360 ° by 30 ° intervals and measured 100 times up to 5m while increasing the distance in 1m intervals. In Figure 4, RSSIs measured at 120-degree intervals are similar, and the antennas used share spectral resources for each sector. In the experiment to obtain the results of FIG. 8, the router was fixed to the ceiling and the sensor was attached to the body to determine the radiation angle and measurement direction between the two nodes as shown in FIG. 9.

The sensor was fixed at 30 degrees while maintaining the same position by fixing the sink by connecting between the user sensor unit and the sink unit in 1: 1, that is, P2P. That is, as a result of experimenting the measured angle between the user sensor unit and the sink unit, it can be seen that consistent RSSI can be measured only by maintaining a specific angle between the two modules.

FIG. 9 is an explanatory diagram for describing a method of measuring an optimal RSSI at a specific distance of each region in the present invention. That is, FIG. 9 determines the radiation angle and the measurement direction between two nodes of the router and the sensor in the experiment to obtain the result of FIG. 8.

10 is an example of a plan view of a home medical location recognition and user identification system according to a temporary embodiment of the present invention.

ID authentication is a step to distinguish users for measuring bio-signals in bed, bathtub, and toilet. When the user carries the user sensor unit, which is the main module, and is active in the room, the user moves to the toilet, bed, and bathtub at the minimum distance. The ID assigned to the module attached to the bathtub and toilet is sent and measured automatically. Information such as measured blood sugar, blood pressure, pulse rate, electrocardiogram, and oxygen concentration (SpO2) can be monitored based on the user's location in the laboratory.

Second is the location recognition and monitoring system of the people living in the room. By dividing the interior of the room into 1m × 1m, RSSI between the sensor and the router was measured 50 times at each location up to at least 1m and up to 3m in each of the five routers, and the signal strength map was implemented at each location in FIG. 10.

FIG. 11 is an example of RSSI measured at each node sector in FIG. 10, and FIG. 12 is a standard deviation of the result of FIG. 11.

FIG. 11 is a router corresponding to the RSSI * measured in each node sector in consideration of the resolution set in FIG. 10. 12 is a router of each sector where the point corresponding to the standard deviation * for the result of FIG.

(A) of FIG. 11 is RSSI measured in node 5 sector, (b) of FIG. 11 is RSSI measured in node 6 sector, (c) of FIG. 11 is RSSI measured in node 7 sector, and FIG. (D) is RSSI measured in node 8 sector, and FIG. 11 (e) is RSSI measured in node 9 sector.

11 is a signal strength map in space confined to RSSI measured at the unique location of each sector. The measurement result shows that RSSI decreases as the distance between transmitter and receiver increases, similar to the characteristics of RF signal.However, the section where RSSI increases due to diffraction and reflection of RF signal caused by obstacles such as furniture and home appliances I could confirm it.

FIG. 12 is a standard deviation of RSSI measured at each position in FIG. 11 and shows an error distribution at a position. This may be a change in the transmission / reception angle between a transmitter and a receiver that is different from a human movement.

RSSI used in the present invention means the signal power coming from the signal source to the receiver and does not consider the gain of the antenna or the loss in the circuit. In addition, RSSI used in programming is a hexadecimal packet, LQI, which indicates the degree of connection between nodes, and has a dynamic range in the range of 0x00 to 0xFF.

As described above, the present invention uses the RSSI as a user identification system for recognizing the location of the occupants and automatically measuring the bio-signals, which is programmable in the sensor module and has high accuracy compared to the method of using estimation and probability. There is this. The most efficient network is constructed in real time by applying multi-hop routing method using LBS-type star-mesh network.

The RSSI algorithm is a method of estimating the distance by comparing the power between the receiver and the transmitter, and empirical measurement is very important, and the threshold RSSI is applied to meet the purpose of ultra-light weight, low power, and portability of the sensor network.

The present invention applies a method of estimating the distance from the measured value by making a signal intensity map as a database based on RSSI measured in advance through experiments in a specific environment. The threshold RSSI of 0xF0 applied to user identification is appropriate because the user must be limited to one person when measuring bio signals in bathtubs, beds, and toilets. When estimating the position of the user, each section of the estimated space was divided into 1 × 1m and the threshold RSSI was used according to a specific position through experiments.

The reasons for applying RSSI can be summarized in two ways. First, the path of a typical RF signal depends on the environment and obstacles that interfere with the signal. A good example of this phenomenon is the use of RF signals by household appliances and furniture. Due to diffraction and reflection, RSSI measured over distance is nonlinear. Secondly, the memory capacity of the sensor module is so small that algorithms with complex calculations and computations cannot be implemented. However, the method using the signal strength map is very efficient in terms of implementation memory and computational process.

RTMS has a real-time advantage in location tracking when compared to LBS, but it is much less efficient compared to the increasing amount of data on the network as the number of users increases. Therefore, we introduced the method of checking the position of the moving object based on whether the moving object to be tracked exists in the predetermined space. The more closely the nodes are arranged, the higher the accuracy of the estimation is. When two or more users exist at the router boundary, the selection problem occurs, so the space is gridized for each zone and the path is of interest. The radio wave strength and the arrival time or the angle of arrival are not always constant but change with time, and when the stationary device starts to move, the error becomes larger, so the LBS method applying the threshold RSSI suitable for a given environment is more suitable.

Resident-based home-based location recognition and user wall list implemented in the present invention provides the user's location and biometric information such as pulse, blood sugar, body temperature, blood oxygen concentration (SpO2), electrocardiogram, etc. By monitoring the location of residents, it is possible to prevent sudden death and accidents, and to provide emergency services quickly.

1 is a block diagram illustrating a schematic configuration of a home medical care location recognition and user identification system according to an embodiment of the present invention.

FIG. 2 is an explanatory diagram for describing a biosignal detection unit of FIG. 1.

3 is an explanatory diagram for explaining a star network and a mesh network.

4 is an explanatory diagram for explaining the RF signal path of the LBS according to an embodiment of the present invention.

5 is a schematic flowchart of a home medical location and user identification system according to an embodiment of the present invention.

6 is an explanatory diagram for explaining a star-mesh network that is a network implemented in the present invention.

7 is an explanatory diagram for explaining user identification in a wireless sensor network in the present invention.

8 is an example of RSSI measured between two nodes up to 1-5m and 0-330 degrees in the present invention.

FIG. 9 is an explanatory diagram for describing a method of measuring an optimal RSSI at a specific distance of each region in the present invention.

10 is an example of a plan view of a home medical location recognition and user identification system according to a temporary embodiment of the present invention.

<Explanation of symbols for the main parts of the drawings>

10: biosignal detection unit 11: biosignal sensing unit

12: electrocardiogram detection sensor unit 14: oxygen saturation detection sensor unit

16: blood pressure detection sensor unit 18: body temperature detection sensor unit

20: blood sugar detection sensor 22: weight detection sensor

30: biosignal preprocessor 32: ECG signal preprocessor

34: oxygen saturation signal preprocessor 36: blood pressure signal preprocessor

38: body temperature signal preprocessor 40: blood sugar signal preprocessor

42: weight signal preprocessor 60: user transmitter

100: user sensor unit 200: router unit

300: sink 400: monitoring unit

Claims (23)

A user main module to which a unique ID (ID of a user sensor unit) is given, the user sensor unit being carried by a user or mounted on the user; A router unit mounted in a building, having a unique ID (router ID) and receiving the user sensor unit ID from a user sensor unit; A sink unit for receiving an ID of the user sensor unit, an ID of the router unit, and an RSSI between the user sensor unit and the router unit from the router unit; A monitoring unit which receives an ID of a user sensor unit, a unique ID of a router unit, an RSSI between the user sensor unit and the router unit, parses the data, and then calculates the location of the user from the sink unit; At-home medical location and user identification system using a signal strength map comprising a. In the at-home medical location recognition and user identification system using a signal strength map having at least a user sensor unit, a router unit, and a monitoring unit, A plurality of user sensor units, each user sensor unit constitutes a peer-to-peer network, and each user sensor unit is assigned a unique ID (ID of the user sensor unit); The router unit is composed of a plurality of mesh networks for accessing from four user sensor units in one router unit, each router unit having a unique ID (router ID); The sink unit includes one, and all router units request a connection to the sink unit centered on one sink unit, and the sink unit is formed of a star network that controls the connection of all router units that allow the connection; The monitoring unit may include receiving an ID of a user sensor unit, a unique ID of a router unit, an RSSI between the user sensor unit and the router unit from the sink unit, parsing data, and calculating and processing the data to determine a user's location. At-home medical location and user identification system using signal strength map. The method according to claim 1 or 2, The signal intensity map further comprises a biosignal detection unit mounted on an object in a room and equipped with a user sensor unit to recognize a unique ID of the user sensor unit and measure the biosignal accordingly. Home medical location and user identification system. The method of claim 3, The at-home medical location recognition and user identification system using the signal intensity map, wherein the biosignal detection unit transmits the detected biosignal to the monitoring unit through the sink unit. The method of claim 3, The at least one biomedical signal detecting unit transmits the detected biosignal to a monitoring unit through a user sensor unit, a router unit, and a sink unit. The method of claim 3, The at least one biomedical signal detecting unit transmits the detected biosignal to a monitoring unit through a router unit and a sink unit. The method of claim 3, The biosignal detection unit uses at least one of pulse, blood sugar, body temperature, blood oxygen concentration (SpO2), electrocardiogram, weight, and blood pressure to detect a biosignal. The method of claim 3, The at-home medical location recognition and user identification system using the signal strength map, wherein the biosignal detection unit is mounted on at least one of a toilet, a bath, a bed, a desk, a computer, and a chair. The method of claim 3, If there is only one user of each instrument of the biosignal detection unit, and if the link quality indicator (LQI) between the module carried and the attached module for user identification is 0xF0 or more, the assigned user sensor unit ID is the biosignal. At-home medical location recognition and user identification system using a signal strength map, characterized in that the transmission to the detection unit. The method of claim 3, The home sensor location recognition and user identification system using the signal strength map, characterized in that the user sensor unit or the router or sink unit consisting of a nano-system. The method of claim 10, The home sensor type location recognition and user identification system using the signal strength map, characterized in that the user sensor unit or the router unit or the sink unit comprises a Nano-24 main module, respectively. The method of claim 11, The sink unit is a home medical location recognition and user identification system using a signal strength map, characterized in that further comprises a Nano-24 interface module. The method of claim 3, The router unit home medical location recognition and user identification system using a signal strength map, characterized in that mounted on the ceiling in the building. The method of claim 3, The home medical location recognition and user identification system using the signal strength map, characterized in that the monitoring unit made of a computer. The method according to claim 1 or 2, It is necessary to measure the RSSI between the user sensor unit carried by the user and the router unit attached to the ceiling in a specific area in advance, and to make a DB of the RSSI measured at each point to have a signal strength map. At-home medical location and user identification system using signal strength map. The method of claim 15, Signal strength, characterized in that it has a threshold value to receive the user's ID only when entering the area within the predetermined RSSI value of the router unit in the ceiling to distinguish the presence / absence of the user in each room using the signal strength map Home medical location and user identification system using map. The method according to claim 1 or 2, At-home medical location and user identification system using a signal strength map characterized by applying a multi-hop routing method. A first step in which a router receives an ID of the user sensor unit from a user sensor unit; A second step of the router unit having received the ID of the user sensor unit in the first step, transmitting an ID of the user sensor unit, an ID of the router unit, and an RSSI between the user sensor unit and the router unit to the sink unit; A third step of transmitting an ID of the user sensor unit, an ID of the router unit, and an RSSI between the user sensor unit and the router unit received from the second step from the sink unit to the monitoring unit; A fourth step of determining a location of a user by performing a processing process after parsing data after receiving the data in the third step; At-home medical location recognition and user identification method using a signal strength map comprising a. The method of claim 18, When a person equipped with a user sensor unit approaches a certain area within a biosignal detection unit mounted on an object in a room, the unique ID of the user sensor unit is recognized and the biosignal of the user is measured. Medical location and user identification. In the at-home medical location recognition and user identification method using a signal strength map comprising at least a user sensor unit, a router unit, and a monitoring unit, The user sensor unit A first step of detecting a MAC address of a sink by scanning a channel; A second step of initializing the user sensor unit of the IEEE 802.15.4 standard; A third step of initializing the nano-Q + driving the user sensor unit, which is a nano system; A fourth step of outputting an ID of the user sensor unit; It consists of The router unit A first step of the router unit scanning the channel to detect the MAC address of the sink; A second router step of initializing a router unit of an IEEE 802.15.4 standard; A third step of a router unit for storing the RSSI of the RF signal transmitted from the user sensor unit; A fourth step of a router unit connecting to receive data of a user sensor unit when the RSSI is equal to or larger than a preset threshold RSSI; A fifth step of the router unit for initializing the nano-Q + driving the router unit, which is a nano system; A sixth step of the router unit receiving an ID of the user sensor unit transmitted from the user sensor unit to the router unit; A seventh step of the router unit for transmitting the data and the router ID received from the router unit to the sink unit; At-home medical location recognition and user identification method using a signal strength map, characterized in that consisting of. The method of claim 20, The sink portion A sink unit detecting a MAC address of the sink by scanning a channel; A sink unit second step of initializing a sink unit of an IEEE 802.15.4 standard; A third step of storing the RSSI of the RF signal transmitted from the router; A sink unit connecting to receive the data from the selected router unit by applying the threshold RSSI; A fifth step of sinking to initialize Nano-Q + for driving the sink, which is a nanosystem; A sixth step of receiving, by the sink unit, an ID of the user sensor unit transmitted from the router unit, an ID of the router unit, and an RSSI between the user sensor unit and the router unit; A seventh step of operating the sink to process the data received by the sink; A sink unit waiting for data reception to the sink to be completed; At-home medical location recognition and user identification method using a signal strength map, characterized in that consisting of. The method of claim 21, A ninth step of a sink unit identifying an ID of the user sensor unit; A sink unit 10 transmitting an ID of the identified user sensor unit to a biosignal detection unit located in a toilet or a bed or a bathroom; An eleventh step of recognizing a location of a user according to an ID of a user sensor unit; A twelfth step of transmitting the biometric data detected by the biosignal detection unit based on the unique ID of the user sensor unit through serial communication; At-home medical location recognition and user identification method using a signal strength map, characterized in that further comprising. The method of claim 22, The monitoring unit A monitoring unit first step of receiving data to the monitoring unit by the occurrence of an event of serial data reception to the monitoring unit; A second step of monitoring unit generating all events by generating an event of a timer at an interval of 100 ms; A third step of monitoring unit for parsing data in the monitoring unit and distinguishing the location of each region and displaying the result; At-home medical location recognition and user identification method using a signal strength map, characterized in that consisting of.

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