CN115249534A

CN115249534A - Method and system for learning habits of old people based on machine

Info

Publication number: CN115249534A
Application number: CN202110470213.0A
Authority: CN
Inventors: 林俊明
Original assignee: Chongqing Shenai Elderly Care Service Co ltd
Current assignee: Chongqing Shenai Elderly Care Service Co ltd
Priority date: 2021-04-28
Filing date: 2021-04-28
Publication date: 2022-10-28

Abstract

The invention relates to a method and a system for learning habits of old people based on a machine. The method comprises the following steps: a receiving step of receiving, by a computing device, a plurality of indications of activities of an elderly person within a space; a matching step of matching, by the computing device, activities of two or more of the received plurality of activities to substantially similar activities; a tagging step of tagging, by the computing device, the matched two or more substantially similar activities as routines of the elderly person; a storing step of storing, by the computing device, the marked routine; and a learning step of learning, by the computing device, a plurality of subsequent routines based at least in part on the stored marked routines. Therefore, man-machine interaction between the old and various devices including intelligent devices can be realized, and life of the old is facilitated.

Description

Method and system for learning habits of old people based on machine

Technical Field

The invention relates to the field of intelligent old people care, in particular to a method and a system for learning habits of old people based on a machine.

Background

Unless otherwise indicated by the disclosure, the approaches described in this section are not prior art to the claims in this application and nothing contained in this section is admitted as prior art.

Technologies have provided convenience to people's daily lives from smart devices (e.g., smart phones, smart televisions, smart appliances, etc.) to medical devices (e.g., wearable medical devices, continuous blood glucose monitoring devices, wearable automatic defibrillators, etc.). It will be appreciated that the adoption of technology may be relatively easy and quickly accepted for young and middle-aged people. However, the elderly may have difficulty adopting and using the techniques. For example, it is difficult, if not impossible, for elderly people to use smartphones. The inability to use a smartphone may provide many benefits to the elderly, who may not be able to obtain the benefits many believe to be provided by using a smartphone. Even the use of a television remote control may be daunting for the elderly. Because modern televisions may be smart televisions, television remote controls may include a variety of sophisticated technologies. The remote control may also be intelligent in order to control the smart tv. Thus, technology may actually be a barrier to the elderly interacting with everyday life.

As modern healthcare extends the life of people, interaction with technology may become more important to the elderly, especially if the elderly live and/or wish to live on their own and live independently. While some technologies have been developed to help people living independently, it may be difficult for the elderly to interact with these technologies. Thus, the elderly may become more dependent on caregivers. Additionally, the inability of the elderly to interact with the technology may cause a sense of loneliness.

All subject matter discussed in this section of this disclosure is not necessarily prior art and cannot be inferred as prior art merely by the presentation in this section. In addition, any reference to any prior art in this specification is not, and should not be taken as, an acknowledgment or any form of suggestion that prior art forms part of the common general knowledge in any art in any country. Along these lines, an appreciation of any problems in the prior art, or anything related to such subject matter, will be discussed in this section, and should not be considered prior art unless explicitly stated as prior art. Rather, any discussion of the subject matter in this section should be considered part of the process that the inventors have undertaken with respect to a particular problem, which itself may be inventive. Accordingly, the foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further explanation, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

Disclosure of Invention

In view of the above-mentioned shortcomings of the prior art, the present application aims to provide a method and a system for learning the habits of the elderly based on a machine, which aims to solve the problem that the elderly may have difficulty in adopting and using technologies to interact with various intelligent devices or medical devices.

The present disclosure describes various illustrative methods and systems for facilitating machine learning of the habits of an elderly person. An example method may include receiving a plurality of indications of activity of an elderly person in a space. The example method may include matching two or more of the received activities to substantially similar activities. The example method may include a routine that tags two or more substantially similar activities that match as elderly people and stores the tagged routines. The example method may include learning a plurality of subsequent routines based at least in part on the stored tagged routines.

An example system may include a defined space that may be used to accommodate an elderly person. An example system may include a plurality of internet of things (IoT) network devices that are communicable dispersed throughout a defined space. An example system may include a computing device communicably coupled to a plurality of internet of things network devices. An example system may include a storage medium communicatively coupled to a computing device and an Elder Care Assistance (ECA) module configured. The ECA module may be configured to receive a plurality of indications of activity of an elderly person within a defined space. The ECA module may be to match two or more of the received activities to substantially similar activities. The ECA module may mark two or more substantially similar activities that match as routines of an elderly person and store the marked routines. The ECA module may be to learn a plurality of subsequent routines based at least in part on the stored tagged routines.

The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further explanation, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

Drawings

Fig. 1 illustrates a system for facilitating intelligent geriatric care in accordance with various embodiments;

FIG. 2 illustrates an operational flow based on machine learning of an elderly person's habits in accordance with at least some embodiments described in the present disclosure;

FIG. 3 illustrates an example computer program product provided in accordance with at least some embodiments described in this disclosure; and

fig. 4 is a block diagram illustrating an example computing device 400 provided in accordance with at least some embodiments described in the present disclosure.

Detailed Description

To facilitate an understanding of the present application, the present application will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present application are given in the accompanying drawings. This application may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

Based on this, the present application intends to provide a solution to the above technical problem, the details of which will be set forth in the following embodiments.

The following description sets forth various examples and specific details in order to provide a thorough understanding of claimed subject matter. It will be understood by those skilled in the art that the claimed subject matter may be practiced without some or more of the specific details disclosed in this disclosure. Additionally, in some instances, well known methods, procedures, systems, components, and/or circuits have not been described in detail as not to unnecessarily obscure claimed subject matter.

In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, like numerals generally identify like components, unless context dictates otherwise. In the detailed description, the illustrative embodiments described in the figures and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the presently disclosed subject matter. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the figures, can be arranged, substituted, combined, and designed in a wide variety of different configurations, all of which are explicitly contemplated and make part of this disclosure.

The present disclosure relates to, among other things, methods, apparatus, systems, and computer-readable media related to intelligent endowment care.

With the aging world population, healthcare systems in many countries can face significant challenges to meet the needs for aging population. Elderly and long-term care may be considered a difficult task, especially if the elderly wish to live independently. Various technologies may be able to provide assistance in the elderly's daily lives, but learning to interact with various technologies may be difficult or impossible for the elderly. According to various embodiments, various methods of facilitating various intelligent technology interactions with the elderly will be described. Some example methods may include machine learning of the behavior of the elderly to facilitate utilization of various technologies. According to various embodiments, various methods of facilitating interaction with a variety of intelligent technologies of the elderly will be described. Some example methods may include machine learning of elderly behavior to facilitate utilization of various technologies. In order to provide a thorough understanding of the disclosed subject matter, a non-limiting example scenario may be described as utilizing the various embodiments disclosed in this disclosure. In a non-limiting example scenario, an elderly person may live independently in a home with various technologies to facilitate machine learning of the elderly person's behavior.

It is worth noting that in some embodiments, the methods, apparatus, systems, and computer readable media of the present disclosure may also be applied to other persons requiring cared attention, such as handicapped persons, patients, or children.

In some embodiments, an elderly person (e.g., a person who may be 65 years old or older) may live independently in a house (e.g., a residence, apartment, etc.). The elderly may (since then) wear electronic wearable devices (e.g., fitbit, smart watches, fitness bracelets, fitness trackers, etc.). The premises may include various internet of things (IoT) -enabled devices such as, but not limited to, lights, televisions, thermostats, digital monitoring systems, etc., which may be dispersed throughout the premises. Devices with IoT and electronic wearable devices (hereinafter wearable devices) may be communicatively coupled to computing devices for Artificial Intelligence (AI) including Machine Learning (ML). Even though various devices may be capable of intelligent interaction with the elderly, the initial device interaction may be manual. For example, the elderly may manually turn on the lights through a wall switch. However, according to various embodiments, as the computing device learns the routine of the person, various devices may interact with the person in an intelligent manner under the control of the computing device.

Continuing with a non-limiting example, an elderly person may have routines/habits of the current day. For example, the elderly may wake up between 7. Upon waking up, the elderly may turn on their television to a particular television station. While watching television, the elderly may use a blood pressure measurement device (such as, but not limited to, a non-invasive blood pressure (NIBP) device) to measure blood pressure. Subsequently, the elderly person continues to follow substantially the same or substantially similar routines, and the computing device may begin learning routines/habits of the elderly person. Thus, the computing device may facilitate routines for the elderly.

For example, a computing device may utilize a network of IoT devices to determine actions and behaviors of an elderly person at various times of the day. The computing device may match two or more activities to substantially similar activities (e.g., wake up every 7 to 8 morning. The computing device may mark activities and behaviors that match at different times (routines thereafter) as routines. The computing device may be used to continuously store tagged routine information or data. As the database storing routines becomes progressively larger, the computing device may learn a plurality of subsequent routines based at least in part on the stored tagged routine data.

In one example, after learning the routines of the elderly, the computing device may learn to predict routines of the elderly to further facilitate routine activities of the elderly. Using the example of the routine described above, the computing device may detect a routine (e.g., via a motion detector configured to connect with an internet of things network device) that a person wakes up in the morning between 7 a.m. and 8 a.m.. In response to this routine of the acquisition of the time period, the computing device may turn on a light in the bedroom.

During the day, the computing device may detect another routine of the elderly person, which may include the elderly person moving from one room to another. In response to the learned routines, the computing device may turn on the lights in the various rooms as the elderly moves from room to room (i.e., the expected movement of the elderly that is obtained based at least in part on the learned routines). Further, the computing device may know that the elderly are at 12 noon: 00 to 1 in the afternoon: 00, there may be a routine to enter the room where the television is located. In response to this learned routine, the computing device may pre-turn on the television, including setting the channel to a commonly viewed channel (e.g., a news channel).

Continuing with the example above, in this example, the computing device may learn routines for the elderly at various times of the day, and the computing device may learn to determine that routines for the elderly may not be within their regular routines. For example, on a day, the computing device may not detect a moving person during the normal 7 am and 8 am. The computing device may utilize the surveillance camera to determine whether the elderly person is likely located within the premises (e.g., through facial and/or body recognition). If the computing device detects an elderly person, but if the elderly person continues to be immobile, the computing device may alert the healthcare or emergency services personnel to perform a welfare check.

In some embodiments, the computing device may have learned to identify a healthcare worker. For example, a routine that the computing device has learned may include that the healthcare worker visits the elderly between 2 pm to 3 pm on each tuesday. As part of the learning process, the computing device may be used to identify medical personnel via a facial recognition device (e.g., a surveillance camera or a smart home security device such as, but not limited to, a smart doorbell initiatives).

Continuing with the example of medical or emergency service personnel performing welfare checks on the elderly, the computing device may facilitate welfare checks. For example, healthcare personnel may be dispatched from a healthcare provider. The information of the dispatched healthcare worker may be communicated to the elderly's computing device. Thus, when the healthcare worker arrives at the elderly's home, the computing device may identify the healthcare worker via some form of biometric data (e.g., by facial recognition, by a fingerprint sensor on the door, by enabling a radio frequency identification/RFID key, etc.). If the elderly require assistance, the computing device may identify and allow medical personnel to enter the home by unlocking an IoT network-enabled door lock. Thus, a computing device that has learned a person's routine may help facilitate welfare checks. This example may also be applicable to emergency personnel, and thus, the computing device may facilitate emergency assistance for elderly people.

In another example, an elderly person may have a health-related condition that may require the elderly person to take blood pressure readings once a day. A blood pressure device (e.g., a non-invasive blood pressure cuff or NIBP) may be an IoT-enabled device and may be communicatively coupled to a computing device. After a period of time, the computing device may learn to associate the person's habits with the blood pressure readings. For example, the elderly's blood pressure may reach a certain level after meals, but if the computing device detects that the elderly's blood pressure reaches a higher than normal level before meals, the computing device may utilize various IoT devices to notify medical personnel. Thus, the computing device may facilitate measures related to preventive health.

Continuing with the example of monitoring of conditions related to a person's health, the computing device may have learned that an elderly person has a routine to visit the toilet once or twice between 2. However, if the computing device detects that the elderly has gone outside of the routine (e.g., 4 to 5 nights), the computing device may communicate this information to the healthcare worker, as this may be an indication related to a health issue. Thus, a routine that causes a computing device to continuously learn about a person may facilitate continuous monitoring of the person's benefits.

In some examples, the computing device may learn of the person's routines may include entertainment (e.g., television channels at certain times), and the computing device may facilitate the elderly's viewing of the television, including learning of the channels the elderly desire to see. In this example, the computing device may be operative to receive and respond to voice commands from a person (e.g., turn off the television, change channels, reduce volume, etc.) via one or more audio interfaces of the IoT device. Thus, the computing device may facilitate entertainment for the person.

In some examples, the routine that the computing device learns of the elderly person may include communicating periodically (e.g., with a phone of a person such as, but not limited to, a son or daughter). For example, an elderly person may make a personal call with their son or daughter at 8.

As shown in a non-limiting example scenario, according to various embodiments, a system having an IoT device and a machine learning computing device may facilitate machine learning behaviors of an elderly person. Thus, the elderly may be able to live on their own without having to learn how to interact with the smart device (e.g., the computing device may learn instead of a person). In addition, the elderly may have more contact with others through smart devices, which may reduce the potential sense of loneliness. Furthermore, potential health related issues of the elderly may be monitored non-intrusively. This monitoring can provide services to a plurality of elderly persons at the same time without requiring regular visits to the elderly person, thus facilitating effective use of this single medical staff, which can continue to monitor the elderly person's health status remotely at the same time.

Turning now to fig. 1, fig. 1 illustrates a system for facilitating intelligent elderly care, in accordance with various embodiments. In fig. 1, a machine learning elderly based system 100 may include a defined space 102, where the defined space 102 may be used to accommodate an elderly person 104. The defined space 102 may include a plurality of internet of things (IoT) network devices 106 dispersed in the defined space 102. The system 100 may include a network 108 and a computing device 110. The computing device 110 may include a storage medium 112, a processor 114, and an assisted care (ECA) module 116. In fig. 1, the internet of things network device 106 may be communicatively coupled to the network 108 through a communication medium 118. Additionally, computing device 110 may be communicatively coupled to network 108 through communication media 118.

The defined space 102 in this disclosure may point to the same object as the representation of the defined space.

In the system 100 shown in fig. 1, an ECA module 116 included in the computing device 110 may receive an indication (e.g., a signal or information) of activity of the elderly person 104 in the defined space 102 (e.g., activity information of the elderly person at home). The ECA module 116 may cause the computing device 110 to match the received two or more activities (or activity information) to substantially similar activities. The ECA module 116 may cause the computing device 110 to mark the matching two or more substantially similar activities as routines of the elderly 104. The marked routines may be stored in a storage medium 112 included in the computing device 110. The ECA module 116 may cause the computing device 110 to learn a plurality of subsequent routines based at least in part on the stored tagged routines. As such, the computing device 110 may learn habits of the elderly 104.

In some embodiments, the step of matching two or more activities further comprises facial recognition of the elderly or/and facial recognition of a healthcare provider. Taking as an example that an activity matching two or more activities to be substantially similar includes facial recognition of the elderly, the method or system mentioned in the present disclosure may be used simultaneously for habits of a plurality of different elderly persons, and based on face recognition and pedestrian trajectory tracking technology, relevant information of elderly persons whose faces are recognized as the same person is stored in association, the relevant information including a trajectory, activity indication, various routines, and health-related data such as blood pressure, blood sugar, height, weight, etc. of the coming year.

In fig. 1, the internet of things network device 106 may be shown as a surveillance camera. However, internet of things network devices 106 may include a wide variety of internet of things network devices within the scope of the claimed subject matter, such as, but not limited to, light switches, televisions, wearable devices, healthcare-related devices, communication-configured devices, set-top boxes, and, as such, the claimed subject matter is not limited in this respect.

In some embodiments, the plurality of internet of things network devices includes an internet of things network device to provide healthcare monitoring or an internet of things network device to provide entertainment to the elderly. It is to be understood that the internet of things network device for providing healthcare monitoring includes at least one of an impedance monitoring sensor, a motion sensor, a non-invasive blood pressure sensor, a hemodynamic sensor, a pulse oximeter sensor, a strain sensor, a temperature sensor, and a moisture/sweat sensor, without limitation.

As shown in fig. 1, the internet of things network device 106 may be communicatively coupled to the computing device 110 over the network 108 via the communication medium 118. The communication medium 118 may be a variety of communication media such as, but not limited to, wired and/or wireless communications (e.g., wi-Fi), bluetooth types based on IEEE 802 standards, near Field Communication (NFC), radio Frequency Identification (RFID), temporary wireless networking solutions (e.g., bluetooth sharing), and any combination thereof. Accordingly, claimed subject matter is not limited in this respect.

The internet of things network devices 106 may utilize a variety of communication media such as, but not limited to, mesh Local Area Network (LAN) types (e.g., zigBee, bluetooth low energy, Z-Wave,6LoWPAN, home internet of things protocol technology, etc.), and any combination thereof. Thus, the communication medium 118 and the network 108 may be used to communicate with various remote devices, such as, but not limited to, smart phone type devices, tablet type devices, server type devices, cloud networks, and the like.

In some examples, the system 100 may locate the computing device 110 within the defined space 102 (e.g., via the network 108 as a local area network or LAN). In some examples, the system 100 may locate the computing device remotely from the defined space 102 (e.g., via the network 108 as the internet and/or cloud). Accordingly, claimed subject matter is not limited in this respect.

Computing device 110 may be a variety of Artificial Intelligence (AI) capable processors available to facilitate at least some of the functions described by the present disclosure, such as, but not limited to, AI-capable processors available from intel corporation of santa clara, california (e.g., nervana type processors), intavada corporation of santa clara, california (e.g., volta (tm) type processors), apple corporation of cupertino, california (e.g., a11 Bionic type processors), gory technologies corporation of donut, china (e.g., kirin type processors), ultramicron instinct (tm) type processors, sony, inc, of sandyvale, california, and the like, and may be available from sanyn, of sey, korea (e.g., exeos type processors), and the like, and so claimed subject matter in this regard. Using an AI-capable processor may facilitate machine learning habits of the elderly 104.

Fig. 2 illustrates an operational flow based on machine learning of an elderly person's habits in accordance with at least some embodiments described in the present disclosure. In some portions of the description, an illustrative embodiment of the method is described with reference to the system 100 depicted in FIG. 1. However, the described embodiments are not limited to these depictions. More specifically, some elements depicted in fig. 1 may be omitted from some embodiments of the methods detailed in this disclosure. Moreover, other elements not depicted in fig. 1 may be used to implement the example methods detailed herein.

Additionally, FIG. 2 employs a block diagram to illustrate in detail the example methods described therein. These listed block diagrams may be described as various functional blocks or acts, may be process steps, functional operations, events and/or acts, etc., and may be performed by hardware, software, and/or firmware. Many alternative detailed functional blocks may be practiced in various implementations. For example, intervening actions not shown in the figure and/or additional actions not shown in the figure and/or some of the actions shown in the figure may be eliminated. In some examples, the acts shown in one figure may be operated using the techniques discussed with respect to the other figure. Additionally, in some examples, the acts illustrated in these figures may be operated using parallel processing techniques. The above and other non-described rearrangements, substitutions, changes, modifications, etc. may be made without departing from the scope of the claimed subject matter.

In some examples, the operational flow 200 may be used as part of learning the habits/habits of the elderly. Beginning at block 202 ("receive activity indication"), the ECA module 116 (shown in fig. 1) may receive a number or indication of activities of the elderly person in the space.

Continuing from block 202 to 204 ("matching activities"), the ECA module 116 may match two or more of the received plurality of activities as substantially similar activities.

Continuing from block 204 to decision block 206 ("mark as routine"), as part of machine learning, the ECA module 116 may mark two or more substantially similar activities that match as routines of an elderly person.

From blocks 206 through 208 ("store marking routine"), the ECA module may store the marking routine in the storage medium 112 (shown in FIG. 1). In some examples, storage medium 112 may be located remotely from computing device 110 (e.g., pervasive computing environment/cloud computing).

Continuing from block 208 to 210 ("learn successor routine"), under control of the ECA module 116, a plurality of successor routines may be learned based at least in part on the stored flag routines.

In general, the operational flows described with respect to fig. 2 and elsewhere herein may be implemented by a computer program product, may be executed on any suitable computing system, and so on. For example, a computer program product for coordinating multiple drones may be provided, an example computer program product being described with reference to fig. 3 and elsewhere herein.

Fig. 3 illustrates a computer program product 300 of an example of an arrangement according to at least some embodiments described in the present disclosure. The computer program product 300 may include a machine-readable non-transitory medium having stored therein instructions that cause a machine to perform learning habits of an elderly person according to the processes and methods discussed in this disclosure. The computer program product 300 may include a signal bearing medium 302, and the signal bearing medium 302 may include one or more machine readable instructions 304 that, when executed by one or more processors, may cause a computing device to operatively provide the functionality described in the present disclosure. In various examples, some or all of the devices discussed in this disclosure may use machine readable instructions.

In some examples, the machine-readable instructions 304 may include receiving a plurality of indications or signals of activity of elderly people in the space. In response to the received plurality of indications, the machine-readable instructions 304 may match two or more of the received plurality of activities to substantially similar activities, and the machine-readable instructions 304 may mark the matched two or more substantially similar activities as a routine for an elderly person. The machine-readable instructions 304 may store a marked routine, and the machine-readable instructions 304 may learn a plurality of subsequent routines based at least in part on the stored marked routine.

In certain embodiments, the signal bearing medium 302 may comprise a computer readable medium 306 such as, but not limited to, a hard disk drive, a Compact Disc (CD), a Digital Versatile Disc (DVD), a digital tape, a memory, and the like. In some embodiments, the signal bearing medium 302 may comprise a recordable medium 308 such as, but not limited to, a memory, a read/write (R/W) optical disc, a read-write digital versatile disc, and the like. In some implementations, the signal bearing medium 302 may include a communication medium 310, such as, but not limited to, a digital medium and/or an analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communications link, a wireless communication link, etc.). In some examples, the signal bearing medium 302 may comprise a machine-readable non-transitory medium.

The methods described with respect to fig. 3 and elsewhere in this disclosure may be implemented in any suitable computing system and/or interactive electronic device. An example system may be that depicted in fig. 4 or described elsewhere, which may be used to learn habits of the elderly.

Fig. 4 illustrates a block diagram of an example computing device 400 arranged in accordance with at least some embodiments described in this disclosure. In various examples, computing device 400 may be used to learn the habits of the elderly discussed in this disclosure. In one example of a basic configuration 401, computing device 400 may include one or more processors 410 and a system memory 420. A memory bus 430 may be used for communicating between the one or more processors 410 and the system memory 420.

Depending on the desired configuration, the one or more processors 410 may be of any type including, but not limited to, a microprocessor (μ P), a microcontroller (μ C), a Digital Signal Processor (DSP), or any combination thereof. Additionally, the microprocessor may include a processor with AI capabilities, such as those previously described. One or more processors 410 may include one or more levels of cache, such as a level one cache 411 and a level two cache 412, as well as processor cores 413 and registers 414. Processor Core 413 may include an Arithmetic Logic Unit (ALU), a floating point arithmetic unit (FPU), a digital signal processing Core (DSP Core), or any combination thereof. The memory controller 415 may also be used with one or more processors 410, or in some embodiments, the memory controller 415 may be a built-in part of the processors 410.

Depending on the desired configuration, the system memory 420 may be of any type including, but not limited to, volatile memory (e.g., RAM), non-volatile memory (e.g., ROM flash, etc.) or any combination thereof. System memory 420 may include an operating system 421, one or more application programs 422, and program data 424. The one or more applications 422 may include an aging services (ECA) module application 423 that may be configured to perform the functions, acts, and/or operations described herein, including the functional blocks, acts, and/or operations described herein. The program data 424 may include routine activity data 425 for use with the ECA module application 423. In some example embodiments, one or more application programs 422 may be arranged to operate with program data 424 on operating system 421. The basic configuration 401 of this description is illustrated in fig. 4 by those components within the dashed line.

Computing device 400 may have additional features or functionality, and additional interfaces to facilitate communications between basic configuration 401 and any required devices and interfaces. For example, a bus/interface controller 440 may be used to facilitate communications between basic configuration 401 and one or more data storage devices 450 via a storage interface bus 441. The one or more data storage devices 450 may be removable storage 451, non-removable storage 452, or a combination of the two. Examples of removable and non-removable storage devices include magnetic disk devices such as solid state hard disks (e.g., floppy disk drives and Hard Disk Drives (HDDs)), optical disk drives such as Compact Disk (CD) drives or Digital Versatile Disk (DVD) drives, drives (SSDs), and tape drives, among others. Example computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data.

System memory 420, removable storage 451 and non-removable storage 452 are all examples of computer storage media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical storage, or cassette tapes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by computing device 400. Any such computer storage media may be part of computing device 400.

Computing device 400 may also include an interface bus 442 for enabling various interface devices (e.g., output interfaces, peripheral interfaces, and communication interfaces) to communicate with the basic configuration 401 via the bus/interface controller 440. Exemplary output interfaces 460 can include a graphics processing unit 461 and an audio processing unit 462, which can be used to communicate with various external devices such as a display or speakers via one or more A/V ports 463. Exemplary peripheral interfaces 470 may include a serial interface controller 471 or a parallel interface controller 472, which may be used to communicate with external or other peripheral devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device, etc.) through one or more I/O ports 473. Illustratively, communication interface 480 includes a network controller 481 which can be used for network communications with one or more other computing devices 490 via one or more communication ports 482. A communication connection is one example of communication media. Communication media may typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and may include any information delivery media. A "modulated data signal" may be a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the information. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio Frequency (RF), infrared (IR), and other wireless media. The term computer readable media as used herein may include both storage media and communication media.

Computing device 400 may be part of a small portable (or mobile) electronic device such as a portable phone, mobile phone, tablet device, laptop computer, personal Data Assistant (PDA), or may be a personal media player device, wireless network viewing device, personal headset device, application specific device, or a hybrid device that include any of the above functions. Computing device 400 may also be configured as a personal computer including laptop computers or non-laptop computers. Additionally, computing device 400 may be part of a wireless base station, other wireless system, or device.

Some portions of the foregoing detailed description are presented in terms of algorithms or symbolic representations of operations on data bits or binary digital signals stored within a computing system memory, such as a computer memory, that are examples of techniques used by one of ordinary skill in the data processing art to convey the substance of their work to others skilled in the art. An algorithm is here, and generally, considered to be a self-consistent sequence of operations or similar processing leading to a desired result. In this context, operations or processing involve physical manipulation of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to such signals as bits, data, values, elements, symbols, characters, terms, numbers, or the like. It should be understood, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels. Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification discussions utilizing terms such as "processing," "computing," "calculating," "determining," or the like, refer to the action or processes of a computing device, wherein manipulating or transforming data in physical or magnetic quantities may be through the memories, registers or other information storage, transmission or display devices of the computing device.

The scope of the claimed subject matter of the present disclosure is not limited to the specific embodiments described in the present disclosure. For example, in some embodiments, the operations may be performed in hardware, e.g., for operation on a device or combination of devices, while in other embodiments, the operations may be performed in software and/or firmware. Also, although claimed subject matter is not limited in scope in this respect, some embodiments may include one or more articles, such as a signal bearing medium, storage medium and/or storage media. The storage medium, e.g., CD-ROM, computer diskette, flash memory, etc., may have stored thereon instructions, which when executed by a computing device, such as a computing system, computing platform, e.g., a "processor" or "other" system, illustratively may be one of the processors executing instructions that implement the subject matter previously described and claimed. As one possibility, a computing device may include one or more processing units or processors, one or more input/output devices (e.g., a display, a keyboard, and/or a mouse), and one or more memories (e.g., static random access memory), dynamic random access memory, flash memory, and/or a hard drive.

There is little distinction left between hardware and software implementations of aspects of systems; the use of hardware or software is often (but not always, since in some cases the choice between hardware and software may become important) a design choice representing a cost versus efficiency tradeoff. The system also includes various vehicles by which processes and/or systems and/or other technologies described in this disclosure may be effected (e.g., hardware, software, and/or firmware), and preferably the deployment of the vehicle will vary with the context in which the processes and/or systems and/or technologies are deployed. For example, if the implementer determines that speed and accuracy are paramount, the implementer may opt for a mainly hardware and/or firmware implementation; if flexibility is paramount, the implementer may opt to implement primarily in software; alternatively, the implementer may opt for some combination of hardware, software, and/or firmware to substitute.

The foregoing detailed description has set forth various embodiments of the devices and/or processes via the use of block diagrams, flowcharts, and/or examples. Insofar as such block diagrams, flowcharts, and/or examples contain one or more functions and/or operations, it will be understood by those within the art that each function and/or operation within such block diagrams, flowcharts, or examples can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or any combination thereof. In one embodiment, portions of the subject matter described in this disclosure may be implemented in the form of an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), or other integrated circuit. However, those skilled in the art will recognize that some aspects of the embodiments disclosed herein, in whole or in part, can be equivalently implemented in integrated circuits, e.g., as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), e.g., as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware, or as virtually any combination thereof, as designing the circuitry and/or writing the code for the software and/or firmware would be well within the skill of one of skill in the art in light of this disclosure. In addition, those skilled in the art will appreciate that the mechanisms of the subject matter described herein are capable of being distributed as a program product in a variety of forms, and that an illustrative embodiment of the subject matter described herein applies regardless of the particular type of signal bearing media used to actually carry out the distribution. Examples of signal bearing media include, but are not limited to, the following: recordable type media such as floppy disks, hard Disk Drives (HDD), compact Disks (CD), digital Versatile Disks (DVD), digital tape, computer memory, etc.; a transmission type medium such as a digital and/or an analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communications link, a wireless communication link, etc.).

It will be appreciated by those skilled in the art that it is common within the art to describe devices and/or processes in the manner set forth herein, and then to integrate such described devices and/or processes into a data processing system using engineering practices. That is, at least a portion of the devices and/or processes described herein may be integrated into a data processing system via a reasonable amount of experimentation. Those skilled in the art will recognize that a typical data processing system generally includes a system unit housing, a video display device, memory such as volatile and non-volatile memory, processors (e.g., microprocessors and digital signal processors), computing entities (e.g., operating systems, drivers, graphical user interfaces, and applications), one or more interaction devices (e.g., a touch pad or screen), and/or a control system including feedback loops and control motors (e.g., feedback for sensing position and/or velocity, control motors for moving and/or adjusting components and/or quantities). The exemplary data processing system described above may be implemented using any suitable commercially available components, such as those typically found in data computing/communication and/or network computing/communication systems.

The subject matter described herein sometimes illustrates different components contained therein, or connected with different other components. It is to be understood that such depicted architectures are merely exemplary, and that in fact many other architectures can be implemented which achieve the same functionality. Conceptually, any arrangement of components to achieve the same functionality as desired can be considered "associated" with the components. Hence, any two components herein combined to achieve a particular functionality can be seen as "associated with" each other such that the subject matter described herein relates to the same functionality as desired, irrespective of architectures or intermedial components. Likewise, any two components so associated can also be viewed as being "operably connected," or "operably coupled," to each other to achieve the desired functionality, and any two components capable of being so associated can also be viewed as being "operably couplable," to each other to achieve the desired functionality. Specific examples of operably coupled include, but are not limited to: physically mateable and/or physically interacting components and/or wirelessly interactable and/or wirelessly interacting components and/or logically interactable components.

With respect to the use of substantially any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate and/or applicable. Various singular/plural permutations may be expressly set forth herein for the sake of clarity.

It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as "open" terms (e.g., the term "including" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," the term "includes. It will be further understood by those within the art that if a certain number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the statements added below may contain usage of the introductory phrases "at least one" and "one or more" to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles "a" or "an" recites a particular claim, but is limited to the inclusion of only one such recitation of any claim containing such introduced claim, even if the same claim includes the introductory phrases "one or more" or "at least one" and indefinite articles such as "a" or "an" (e.g., "a" and/or "an" should typically be interpreted to mean "at least one" or "one or more"), as well as the use of definite articles such as "a" or "an" for the introduction of a claim.

In addition, even if the recitation of the claims explicitly introduces a specific number, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number. (e.g., a mere statement that only "two statements," without other modifiers, are used, typically means at least two statements, or two or more statements.) furthermore, where those conventions are used similar to "at least one of A, B, and C, etc.," it is intended that such construction be used in the sense one of skill in the art would understand the convention (e.g., "a system having at least one of A, B, and C" would include, but not be limited to, systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). Where those conventions use something similar to "at least one of A, B, or C, etc." it is intended that such construction be used in general in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B, or C" would include but not be limited to systems that individually own A, individually own B, own C, own A and B, own A and C, own B and C, and/or own A, B, and C, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, including one or both of the terms. For example, the phrase "a or B" will be understood to include the possibility of "a" or "B" or "a and B".

Reference in the specification to "one embodiment," "an embodiment," "some embodiments," or "other embodiments" may mean that a particular feature, structure, or characteristic described in connection with one or more embodiments may be included in at least some embodiments, but not necessarily in all embodiments. The appearances of the various "one embodiment," "an embodiment," or "some embodiments" in the preceding description are not necessarily all referring to the same embodiments.

While the disclosure has described and illustrated certain exemplary techniques using various methods and systems, it will be understood by those skilled in the art that various other modifications may be made, and equivalents may be substituted, without departing from claimed subject matter. In addition, many modifications may be made to adapt a particular situation to the teachings of claimed subject matter without departing from the central concept described in this disclosure. Therefore, it is intended that claimed subject matter not be limited to the particular examples disclosed, but that such claimed subject matter may also include all implementations falling within the scope of the appended claims, and equivalents thereof.

It is to be understood that the invention is not limited to the examples described above, but that modifications and variations may be effected thereto by those of ordinary skill in the art in light of the foregoing description, and that all such modifications and variations are intended to be within the scope of the invention as defined by the appended claims.

Claims

1. A method for machine-based learning of habits of an elderly person, the method comprising:

a receiving step of receiving, by a computing device, a plurality of indications of activities of an elderly person within a space;

a matching step of matching, by the computing device, activities of two or more of the received plurality of activities to substantially similar activities;

a tagging step of tagging, by the computing device, the matched two or more substantially similar activities as routines of the elderly person;

a storing step of storing, by the computing device, the marked routine; and

a learning step, by the computing device, learning, based at least in part on the stored marked routine, a plurality of subsequent routines.

2. The method of claim 1, wherein the receiving step comprises: receiving, by a plurality of Internet of things network devices, a plurality of indications of activities of the elderly.

3. The method of claim 2, wherein the plurality of internet of things network devices comprises internet of things network devices to provide entertainment to the elderly.

4. The method of claim 2, wherein the plurality of internet of things network devices comprises internet of things network devices to provide healthcare monitoring.

5. The method of claim 4, wherein the Internet of things network device to provide healthcare monitoring includes at least one of an impedance monitoring sensor, a motion sensor, a non-invasive blood pressure sensor, a hemodynamic sensor, a pulse oximeter sensor, a strain sensor, a temperature sensor, and a moisture/sweat sensor.

6. The method of claim 2, wherein the plurality of internet of things network devices comprises one or more smart wearable devices.

7. The method of claim 2, wherein the plurality of internet of things network devices comprises internet of things network devices to provide communications to elderly people.

8. The method of claim 1, wherein said matching step comprises performing facial recognition of said elderly.

9. The method of claim 1, wherein the matching step comprises performing facial recognition by a healthcare provider.

10. The method of claim 1, wherein the step of tagging comprises counting a number of times the two or more substantially similar activities are matched.

11. The method of claim 1, wherein the storing step comprises sending the marked routine to a remote storage device.

12. The method of claim 1, wherein the learning step comprises determining whether the plurality of subsequent routines are outside of the marker routine.

13. The method of claim 1, wherein the learning step comprises determining whether the plurality of subsequent routines are within the tagged routine.

14. A system for machine learning of the habits of elderly people, the system comprising:

a defined space configured to accommodate an elderly person;

a plurality of internet of things network devices communicatively dispersed in the defined space;

a computing device communicatively coupled to the plurality of internet of things network devices;

a storage medium communicatively coupled to the computing device; and

an endowment care help module to perform:

receiving a plurality of indications of activity of an elderly person within the defined space;

matching two or more of the received plurality of activities as substantially similar activities;

marking the two or more substantially similar activities that match as routine activities for the elderly;

storing the marked routine in the storage medium; and

learning a plurality of subsequent routines based at least in part on the stored routine of the indicia.

15. The system of claim 14, wherein the defined space comprises a home of the elderly.

16. The system of claim 14, wherein the plurality of internet of things network devices comprises an internet of things network device to provide entertainment to the elderly.

17. The system of claim 14, wherein the plurality of internet of things network devices comprises an internet of things network device to provide healthcare monitoring.

18. The system of claim 17, wherein the internet of things network device to provide healthcare monitoring includes at least one of an impedance monitoring sensor, a motion sensor, a non-invasive blood pressure sensor, a hemodynamic sensor, a pulse oximeter sensor, a strain sensor, a temperature sensor, and a moisture/sweat sensor.

19. The system of claim 14, wherein the plurality of internet of things network devices comprises one or more smart wearable devices.

20. The system of claim 14, wherein the plurality of internet of things network devices comprises an internet of things network device to provide communications to the elderly.