Patent Application
20180325426
This relates generally to health monitoring devices, including but not limited to wearable electronic wellness tracking devices that monitor and report activities of daily living.
Activities of daily living (ADL) are routine activities that people tend to do every day, and a person's ability to perform them is important in determining the person's ability to live independently. The level of assistance a person may need with regard to day-to-day living can be determined by monitoring the ADLs the person performs over a given period of time.
Healthcare professionals have an interest in monitoring ADLs in order to evaluate their patients, but conventional ADL monitoring systems can be burdensome and expensive, requiring multiple monitoring devices. Further, conventional ADL monitoring systems can be inaccurate due to wide ranges of motions associated with each ADL, which vary from person to person. Additionally, conventional ADL monitoring systems can lack the flexibility and mobility required for tracking a person in multiple locations around a house, due to rigid vision systems that are limited in terms of lighting and field of view. Camera-based ADL monitoring systems are also associated with privacy issues, as people dislike the idea of being on camera while performing certain personal activities like bathing and toileting.
Accordingly, there is a need for ADL monitoring and reporting systems with improved monitoring methods and more portable and self-contained devices. Such methods and devices optionally complement or replace conventional methods and devices for monitoring and reporting ADLs. Such methods provide more accurate ADL classifications by using pre-trained neural networks to interpret raw data, and such devices eliminate the need to install multiple sensing components by being self-contained in a wearable form factor, thereby creating more accurate results with less burdensome hardware. The aforementioned deficiencies and other problems associated with ADL monitoring systems are reduced or eliminated by the disclosed ADL monitoring and reporting systems.
In accordance with some embodiments, a user-wearable electronic device includes a housing configured to be worn on a user's torso, the housing including an interface configured to be in direct contact with the user's skin; a plurality of sensors disposed in the housing, including a first sensor to produce raw ADL data, and a biometric sensor coupled to the interface and configured to sense one or more biometric characteristics of the user and generate corresponding biometric data. The device further includes one or more processors, disposed in the housing and coupled to the one or more sensors, and configured to generate, for each time period in a sequence of successive time periods, ADL identification information for the time period by processing the raw ADL data produced by the first sensor using one or more neural networks pre-trained to recognize a predefined set of ADLs. In some embodiments, at least one of the pre-trained neural networks includes a plurality of neural network layers, at least one layer of the plurality of neural network layers comprising a recurrent neural network, wherein an output of the one or more neural networks for each time period corresponds to the generated ADL identification information for the time period. In some embodiments, each pre-trained neural network includes a plurality of neural network layers, at least one layer of the plurality of neural network layers comprising a recurrent neural network.
The device also includes a transmitter, disposed in the housing and coupled to at least one processor of the one or more processors, to transmit one or more reports corresponding to the user, wherein a respective report for the user includes ADL information corresponding to the generated ADL identification information for one or more time periods in the sequence of time periods.
In some other embodiments, obtaining raw ADL data corresponding to a user and processing the raw ADL data to produce ADL identification information for one or more time periods in a sequence of successive time period is distributed over two or more devices, at least one of which processes the raw ADL data, or related ADL information, using one or more neural networks pre-trained to recognize a predefined set of ADLs. For example, in some embodiments, a user-wearable electronic device includes a housing configured to be worn by or embedded in a device worn by a user; one or more sensors disposed in the housing, including a first sensor to sense motion of the user and produce raw ADL data corresponding to the user. The device also includes a transmitter, optionally disposed in the housing, to transmit the ADL data or ADL information generated from the ADL data, to a monitoring system or to an intermediate device at which the ADL data or ADL information is further processed to generate, for each time period in a sequence of successive time periods, ADL identification information for the time period by processing the raw ADL data produced by the first sensor or ADL information generated from the ADL data, using one or more neural networks pre-trained to recognize a predefined set of ADLs. In some of these embodiments, at least one of the pre-trained neural networks includes a plurality of neural network layers, at least one layer of the plurality of neural network layers comprising a recurrent neural network, wherein an output of the one or more neural networks for each time period corresponds to the generated ADL identification information for the time period. In some of these embodiments, each of the pre-trained neural networks includes a plurality of neural network layers, at least one layer of the plurality of neural network layers comprising a recurrent neural network.
For a better understanding of the various described embodiments, reference should be made to the Description of Embodiments below, in conjunction with the following drawings in which like reference numerals refer to corresponding parts throughout the figures.
Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the various described embodiments. However, it will be apparent to one of ordinary skill in the art that the various described embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.
It will also be understood that, although the terms first, second, etc. are, in some instances, used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first contact could be termed a second contact, and, similarly, a second contact could be termed a first contact, without departing from the scope of the various described embodiments. The first contact and the second contact are both contacts, but they are not the same contact, unless the context clearly indicates otherwise.
The terminology used in the description of the various described embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various described embodiments and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
As used herein, the term “if” is, optionally, construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” is, optionally, construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context.
Attention is now directed toward embodiments of activities of daily living (ADL) monitoring and reporting systems in accordance with some embodiments.
In some embodiments, person 102, ADL monitoring device 104, and intermediary device 106 are located in a home 110. It is understood that home 110 is any living arrangement in which a healthcare professional or anyone in a caregiver role may wish to monitor ADLs of one or more persons, including, for example, a house, an apartment, an assisted care facility, or a hospital. In some embodiments, home 110 is one of a plurality of homes, such as n homes where n is an integer greater than 1, or greater than 2. Each home 110 typically houses one or more people and one or more ADL monitoring devices, all of which report ADL data (e.g., ADL identification information) to monitoring system 120, either directly or through one or more intermediary devices 106. In other embodiments, home 110 is the only home from which ADL data is reported to monitoring system 120.
In some embodiments, mobile device 122 is communicatively coupled to monitoring system 120, and provides access to ADL reports for healthcare professionals (or those otherwise fulfilling a caregiver role) wishing to monitor ADL data from one or more persons 102. In embodiments in which the ADLs of multiple people are being monitored, mobile device 122 optionally provides access to a desired subset of the people whose ADL's are being monitored. For example, a caregiver is optionally given access, via mobile device 122, to the ADL information for a particular subset of persons whose ADL information is being reported to monitoring system 120. Access rights are optionally assigned according to security levels, relevance levels, legal constraints, and/or on a need-to-know basis. For example, a first person's caregiver may be given access to ADL for the first person, while a second person's caregiver may be given access to ADL reports for the second person. As another example, for embodiments in which monitoring system 120 receives ADL reports from a plurality of different homes or rooms in a nursing home, caregivers may only have access to ADL reports for users assigned to each respective caregiver. In yet another example, different caregivers are given access to ADL information at different levels of granularity. For example, relatives of a monitored person may be given access only to summary reports for the monitored person, for example daily summary reports, without access to more detailed ADL information, while healthcare professionals assigned to monitor the person have access all ADL information or more detailed ADL information. Optionally or alternatively, for embodiments in which there is no mobile device 122, monitoring system 120 provides access to ADL reports.
In some embodiments, housing 202 is configured to be worn on or near a user's torso. In some embodiments, housing 202 is configured to be in direct contact with the user's skin, or affixed to an object, such as an adhesive layer, that is in direct contact with the user's skin. In some embodiments, housing 202 is placed on or near any portion of the user's torso that moves with the user, such as the chest, stomach, back, shoulder, or side of the body. In some embodiments, housing 202 has a compact form factor that allows device 104 to be worn on the user's body without causing a nuisance to the user. For example, housing 202 may have a length no greater than 10 centimeters (cm), a width no greater than 6.5 cm, and a thickness no greater than 0.5 cm. A typical size is 5 to 8.5 cm in length, 2.5 to 5 cm in width, and 0.1 to 0.25 cm in thickness. Other dimensions are possible as well, with a person of ordinary skill in the art recognizing that the bigger the housing, the more of a nuisance its presence may be on the user's body. However, since a bigger housing can fit more components and/or a larger battery 216, and different dimensions can be optimized to fit various sizes of internal components, the exact dimensions of housing 202 are not meant to be limiting to any of the disclosed embodiments. In some embodiments, the form factor of housing 202 is configured to allow device 104 to fit within underclothing, such as a band of an article of underclothing, which helps to ensure the device is maintained in contact with the user's skin. Optionally, housing 202 is configured to allow user-wearable electronic device 104 to be integrated into an article of clothing that conforms to the skin. Additionally, housing 202 includes a waterproof or water-resistant seal so that user-wearable electronic device 104 is washable with a machine washer and can withstand activities involving water (e.g., bathing) and environments having high humidity. In order to further minimize any discomfort that may be caused by the presence of housing 202 on the user's body, in some embodiments, housing 202 and all components within the housing may be configured to have a total weight no greater than 60 grams. In other embodiments, the total weight may be no greater than 25 grams, 20 grams, 15 grams, or 10 grams.
In some embodiments, ADL sensors 204 include an accelerometer, an orientation sensor, a motion sensor, a gyroscopic sensor, or a combination thereof. In some embodiments, ADL sensors 204 include only one of the aforementioned sensors. ADL sensors 204 generate acceleration data, orientation data, motion data, gyroscopic data, or a combination thereof in response to movements associated with ADLs. In some embodiments, a predefined set of basic ADLs monitored by user-wearable electronic device 104 includes dressing, eating, transferring/ambulation, continence/toileting, bathing/hygiene, sitting, and sleeping/napping. Optionally, a predefined set of instrumental ADLs monitored by user-wearable electronic device 104 includes housekeeping, meal preparation, transportation, talking/socialization/communication, managing personal finances/accounting, and managing medications, or any subset of those activities. In various embodiments, user-wearable electronic device 104 is configured to monitor a subset of p basic ADLs, where p is 3, 4 or 5, or more generally p is an integer greater than 2, greater than 3, or greater than 4.
In some embodiments, biometric sensors 206 include a temperature sensor for sensing temperature of the user, an electrocardiography (EKG) sensor for detecting electrical activity associated with the user's heart, a heart rate sensor for measuring the user's heart rate, a blood pressure sensor for measuring at least one parameter of the user's blood pressure, a bioimpedance sensor for measuring at least one parameter of the user's body composition, a total water content sensor, a photoplethysmograph (PPG) sensor for measuring the user's heart rate, or any subset or combination thereof. In some embodiments, biometric sensor 206 includes only one of the aforementioned sensors (e.g., a heart rate sensor), or only two of the aforementioned sensors (e.g., a temperature sensor and heart rate sensor). In some embodiments, biometric sensor 206 is coupled to interface 208 of housing 202 to allow biometric sensor 206 close access to the user's skin. In some embodiments, interface 208 is an opening in housing 202, which allows for direct physical contact of biometric sensor 206 with the user's skin. In other embodiments, interface 208 is a region of housing 202 in which a cross section of housing material is smaller (i.e., thinner) when compared to a region outside of interface 208, which is useful for establishing electrical contact with the user's skin. By enabling direct or close access to the user's skin, interface 208 allows biometric sensor 206 to accurately sense biometric characteristics of the user.
In some embodiments, bottom surface 234 of device 234 includes a plurality of contacts 232 for making electrical and mechanical contact with a user's skin, and providing electrical signals to one or more biometric sensors 206 inside housing 202. In some embodiments, bottom surface 234 of device 234 includes interface 208 through which one or more biometric sensors 206 make contact with or are coupled to the skin of the user.
As explained above with reference to
Memory 306 includes high-speed random access memory, such as DRAM, SRAM, DDR RAM or other random access solid state memory devices, and may include non-volatile memory, such as flash memory devices, or other non-volatile solid state storage devices. Memory 306, or alternately the non-volatile memory device(s) within memory 306, comprises a non-transitory computer readable storage medium. In some embodiments, memory 306, or the computer readable storage medium of memory 306 stores the following programs, modules, and data structures, or a subset or superset thereof:
Each of the above identified elements may be stored in one or more of the previously mentioned memory devices that together form memory 306. Each of the above mentioned modules or programs, including the aforementioned modules and operating system, corresponds to a set of instructions and data for performing a function described above. The above identified modules or programs (i.e., sets of instructions) need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various embodiments. In some embodiments, memory 306 may store a subset of the modules and data structures identified above. Furthermore, memory 306 may store additional modules and data structures not described above. In some embodiments, the programs, modules, and data structures stored in memory 306, or the computer readable storage medium of memory 306, provide instructions for implementing respective operations of the methods described herein.
Although
Memory 336 includes high-speed random access memory, such as DRAM, SRAM, DDR RAM or other random access solid state memory devices, and may include non-volatile memory, such as flash memory devices, or other non-volatile solid state storage devices. Memory 336, or alternately the non-volatile memory device(s) within memory 336, comprises a non-transitory computer readable storage medium. In some embodiments, memory 336, or the computer readable storage medium of memory 336 stores the following programs, modules, and data structures, or a subset or superset thereof:
Each of the above identified elements may be stored in one or more of the previously mentioned memory devices that together form memory 336. Each of the above mentioned modules or programs, including the aforementioned modules and operating system, corresponds to a set of instructions and data for performing a function described above. The above identified modules or programs (i.e., sets of instructions) need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various embodiments. In some embodiments, memory 336 may store a subset of the modules and data structures identified above. Furthermore, memory 336 may store additional modules and data structures not described above. In some embodiments, the programs, modules, and data structures stored in memory 336, or the computer readable storage medium of memory 336, provide instructions for implementing respective operations of the methods described herein.
Although
In embodiments represented by
Each of the above identified elements may be stored in one or more of the previously mentioned memory devices that together form memory 306 (of ADL sensing device 104-2) and/or memory 336 (of intermediary device 106-2). Each of the above mentioned modules or programs, including the aforementioned modules and operating system, corresponds to a set of instructions and data for performing a function described above. The above identified modules or programs (i.e., sets of instructions) need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various embodiments. In some embodiments, memory 306 and/or memory 336 may store a subset of the modules and data structures identified above. Furthermore, memory 306 and/or memory 336 may store additional modules and data structures not described above. In some embodiments, the programs, modules, and data structures stored in memory 306 and/or memory 336, or the computer readable storage medium of memory 306 and/or memory 336, provide instructions for implementing respective operations of the methods described herein.
As recognized by those of ordinary skill in the art, embodiments corresponding to
In some embodiments, ADL sensing device 104 carries out all of the processing and transmits reports directly to a monitoring system, or other system from which ADL information regarding the user is retrieved by a caregiver, rendering an intermediary device unnecessary. In some embodiments, ADL sensing device 104 carries out all of the neural network processing and computations in real time, but only periodically sends ADL reports (e.g., once an hour, once every four hours, or once every eight hours), or only sends ADL reports when the sensing device 104 is plugged in to a power charger, thereby conserving battery power. In some embodiments, ADL sensing device 104 carries out all of the neural network processing and computations in real time, sends ADL reports periodically, but sends emergency reports in real time. In some embodiments, ADL sensing device 104 carries out all of the neural network processing and computations in real time, and sends the ADL reports in real time (e.g., as soon as a report is ready, such as every minute, every five minutes, every twenty minutes, or every hour). In some embodiments, ADL sensing device 104 carries out neural network processing and computations in real time, and sends ADL reports in real time if the sensing device 104 is in communicative range of an intermediary system 106, a monitoring system 120, or other system from which ADL information regarding the user is retrieved. Otherwise, if the sensing device 104 is outside of a communication range (e.g., the user leaves the house and the user's ADL sensing device 104 can no longer wirelessly communicate with the intermediary device 106 or the monitoring system 120), the ADL sensing device 104 continues to sense ADL information and carry out neural network processing, storing ADL reports in local memory (e.g., memory 306) until the sensing device 104 is once again in communication range of an intermediary device 106, monitoring system 120, or other system from which ADL information regarding the user can be retrieved (e.g., the user returns to the house and the user's ADL sensing device 104 sends ADL reports stored in memory 306 to the intermediary device 106 or the monitoring system 120).
Although
Memory 406 includes high-speed random access memory, such as DRAM, SRAM, DDR RAM or other random access solid state memory devices, and may include non-volatile memory, such as flash memory devices, or other non-volatile solid state storage devices. Memory 406, or alternately the non-volatile memory device(s) within memory 406, comprises a non-transitory computer readable storage medium. In some embodiments, memory 406, or the computer readable storage medium of memory 306 stores the following programs, modules, and data structures, or a subset or superset thereof:
In some embodiments, memory 406, or the computer readable storage medium of memory 406 also stores one or more neural networks (e.g., similar to neural networks 316,
Each of the above identified elements may be stored in one or more of the previously mentioned memory devices that together form memory 406. Each of the above mentioned modules or programs, including the aforementioned modules and operating system, corresponds to a set of instructions and data for performing a function described above. The above identified modules or programs (i.e., sets of instructions) need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various embodiments. In some embodiments, memory 406 may store a subset of the modules and data structures identified above. Furthermore, memory 406 may store additional modules and data structures not described above. In some embodiments, the programs, modules, and data structures stored in memory 406, or the computer readable storage medium of memory 406, provide instructions for implementing respective operations of the methods described herein.
Although
Memory 506 includes high-speed random access memory, such as DRAM, SRAM, DDR RAM or other random access solid state memory devices, and may include non-volatile memory, such as flash memory devices, or other non-volatile solid state storage devices. Memory 506, or alternately the non-volatile memory device(s) within memory 506, comprises a non-transitory computer readable storage medium. In some embodiments, memory 506, or the computer readable storage medium of memory 506 stores the following programs, modules, and data structures, or a subset or superset thereof:
Each of the above identified elements may be stored in one or more of the previously mentioned memory devices that together form memory 506. Each of the above mentioned modules or programs, including the aforementioned operating system, corresponds to a set of instructions and data for performing a function described above. The above identified modules or programs (i.e., sets of instructions) need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various embodiments. In some embodiments, memory 506 may store a subset of the modules and data structures identified above. Furthermore, memory 506 may store additional modules and data structures not described above. In some embodiments, the programs, modules, and data structures stored in memory 506, or the computer readable storage medium of memory 506, provide instructions for implementing respective operations of the methods described herein.
Although
Examples of activity counts 630 include a walking activity count 632, which is or includes, for example, a count of steps by the user, or a count of minutes in which the user was walking, during one or more predefined periods of times, such as fifteen minutes, one hour, eight hours, and/or twenty-four hours; an eating activity count 634, which is or includes a count of minutes in which the user was eating, during one or more predefined period of times, such as fifteen minutes, one hour, eight hours, and/or twenty-four hours; a resting activity count 636, which is or includes, for example, a count of minutes in which the user was resting (remaining stationary, or napping, or sleeping), during one or more predefined period of times, such as fifteen minutes, one hour, eight hours, and/or twenty-four hours; and/or a bathing activity count 638, which is or includes, for example, a count of minutes in which the user was bathing or engaged in hygiene activities, during one or more predefined period of times, such as fifteen minutes, one hour, eight hours, and/or twenty-four hours. Another example of an activity count that is optionally determined by device 104 for each of one or more respective time periods, and included in activity counts 630, is a count of the number of times the user has tripped or stumbled. In the aforementioned examples, each count of the number of instances of an ADL being performed may be considered with a corresponding count of time during which the instances were being performed in order to calculate a health-related value. For example, a user who walks for S minutes during a given period of time will have a higher level of health, welfare, fitness, and/or activity than a user who walks for T minutes during the given period of time, where T is less than S (sometimes represented as T<S).
In some embodiments, user-wearable electronic device 104 generates raw data reports 650 so as to provide monitoring system 120, or one or more other systems, with raw ADL data 658 to enable the generation of improved, or personalized, neural network configurations. In some embodiments, a respective raw data report 650 includes ADL vectors 642 (described in more detail below) for a report period, activity counts 644 (e.g., activity counts for activities such as walking, eating, hygiene and resting) for the report period, biometrics 646 (e.g., temperature and heart rate) for the report period, and raw ADL data 658 for the report period.
Exemplary ability categories in accordance with some embodiments include, but are not limited to: ambulation-impaired activities (e.g., for users who use a cane, walker, or wheelchair for ambulation), and motor function-impaired activities (e.g., for users who have difficulty moving their hands, arms, hips, and the like). ADLs in different ability categories are detected using category-specific NNCs. For example, wheelchair-based ambulation is associated with different torso movements than walking-based ambulation, and accordingly, a first NNC is used for detecting ambulation based on movements associated with operating a wheelchair, and a second (different from the first) NNC is used for detecting ambulation based on movements associated with walking. As a further example, a motor function-impaired person may bathe by sitting in a bath, while a non-impaired person may bathe by standing in a shower. Accordingly, a first NNC is used for detecting bathing based on movements associated with taking a bath, and a second (different from the first) NNC is used for detecting bathing based on movements associated with taking a shower.
In some embodiments, memory 406 initially includes a generic ability category NNC, which enables device 104 to be used without a preprogrammed ability-specific NNC. In some embodiments, the generic NNC is subsequently updated or replaced with an updated neural network configuration according to a received update or based on subsequent training, resulting in processor(s) 210 reconfiguring or replacing the generic NNC with the updated configuration, thereby enabling ability-specific ADL identification information for time periods subsequent to the reconfiguring of the ADL sensing device 104 with an ability-specific NNC.
In some embodiments, raw ADL sensor data is temporarily stored in a raw data buffer 708 in user-wearable electronic device 104, which, along with the report data included in the aforementioned periodic reports is provided to a raw data report generator 710, which produces a raw data report (e.g., raw data report 650,
Through the use of one or more trained neural networks 316 in user-wearable electronic device 104, ADLs are associated with certain characteristic motions and/or orientations. As a nonlimiting example, eating is typically associated with a forward-leaning motion or similar torso motion as the user inserts a utensil with food into the user's mouth. Similarly, other ADLs are associated with other patterns of movement and/or orientation. One or more neural networks in user-wearable electronic device 104 are trained to recognize motion and/or orientation patterns consistent with eating, and each of the other ADLs that device 104 is configured to monitor.
In some embodiments, as shown in
For each time period in a sequence of successive time periods, processor 210 generates ADL identification information for the time period by processing the raw ADL data produced by ADL sensor 204 using one or more neural networks pre-trained to recognize a predefined set of ADLs. In some embodiments, the successive time periods each have a duration of no more than 30 seconds (for example, 6 seconds). In some embodiments, processor 210 processes at least 10 samples of raw ADL data for each time period of the successive time periods. Further, in some embodiments, a ratio of the time period (at which processor 210 generates ADL identification information) to the sampling period (at which processor 210 samples raw data) is no less than 100, and is typically between 100 and 5,000. In some embodiments, each pre-trained neural network includes a plurality of neural network layers, and at least one layer of the plurality of neural network layers is, or includes, a recurrent neural network. An output of the neural network for each time period corresponds to the generated ADL identification information for the respective time period.
In some embodiments, processor 210 generates the ADL identification information for a respective time period in the sequence of time periods by generating a set of scores, including one or more scores for each ADL in the predefined set of ADLs. In accordance with the generated set of scores, processor 210 determines a dominant activity for the respective time period, wherein the dominant activity is one of the ADLs in the predefined set of ADLs. In accordance with a determination that the one or more scores for the dominant activity for the respective time period meets predefined criteria, processor 210 includes in the generated ADL identification information for the respective time period information identifying the dominant activity for the respective time period. However, in accordance with a determination that the one or more scores for the dominant activity for the respective time period do not meet the predefined criteria, processor 210 includes in the generated ADL identification information for the respective time period information indicating that the user's activity during the respective time period has not been classified as any of the ADLs in the predefined set of ADLs. In some embodiments, the predefined set of ADLs includes N distinct ADLs, where N is an integer greater than 2, and the ADL identification information generated by the one or more processors for the time period includes a vector of having at least N+1 elements, only one of which is set to a non-null value. In other embodiments, the predefined set of ADLs includes N distinct ADLs, where N is an integer greater than 2, and the ADL identification information generated by the one or more processors for the time period includes a vector of having at least N elements, only one of which is set to a non-null value.
In some embodiments, proximity receiver 212 is disposed in or on housing 202. Proximity receiver 212 obtains location or proximity information (hereinafter, “raw proximity information”) corresponding to a range or proximity to one or more beacons 132-138 (see
In some embodiments, transceiver 214 is disposed in housing 202 and coupled to processor 210. Transceiver 214 obtains ADL identification information for a sequence of time periods from processor 210, and transmits reports for the user. In some embodiments, transceiver 214 transmits the reports at predefined times at intervals of no less than 5 minutes (for example, fifteen minutes). In other embodiments, transceiver 214 transmits the reports only when device 104 is connected to an external power source or otherwise receiving power from an external power source, for example so as to charge the internal battery 216 of the device. Further, in some embodiments, transceiver 214 transmits the reports in response to a manual transmission command (e.g., by pressing a “transmit” button on device 104, or by a caregiver requesting the reports while using monitoring system 120 or mobile device 122). In some embodiments, reports are transmitted at a predetermined transmission rate (e.g., every fifteen minutes, every hour, every four hours, every eight hours, and/or once a day), but with aggregated ADL identification information from a plurality of time periods (e.g., ADL counts for five-minute or fifteen-minute windows of time). It is understood that the aforementioned reporting times and aggregation periods are exemplary, and a person of ordinary skill in the art may configure them to be set in accordance with caregiver-determined requirements and/or user-specific needs. In some embodiments, a respective report includes ADL information (e.g., a list of ADLs detected during given time periods) corresponding to the generated ADL identification information for one or more time periods in the sequence of time periods. In some embodiments, in addition to including ADL information, a respective report also includes biometric information corresponding to the raw biometric data generated by the biometric sensor for a time period corresponding to the respective report.
Further, in some embodiments, in addition to or as an alternative to including ADL identification information, a respective report (e.g., raw data report 650,
In some embodiments, processor 210 automatically detects an emergency, based on the raw ADL data and/or biometric data, in accordance with predefined emergency detection criteria. In response to the automatic detection of an emergency, processor 210 initiates transmission of an emergency report to the target system using transceiver 214. In some embodiments, the criteria for identifying an emergency include one or more of: heart rate high or low (e.g., above a first heart rate threshold or below a second heart rate threshold); body temperature high or low (e.g., above a first temperature threshold or below a second temperature threshold); breathing rate high or low (e.g., above a first breathing rate threshold or below a second breathing rate threshold); falling or stumbling; not sleeping during a time period of at least a predefined minimum duration; not eating or drinking during a time period of at least a predefined minimum duration; coughing in excess of a predefined threshold; choking; a crossed threshold of time during which an activity has been performed (e.g., sleeping or remaining in one position for too long); a crossed threshold of time between particular activities (e.g., too much time between meals); a crossed threshold of activity counts (e.g., too many or too little ADL events compared to a predefined goal or standard); or a combination of such factors.
In some embodiments, and with reference to
In some embodiments, transceiver 214 receives an updated configuration for the one or more neural networks, and sends the updated configuration to processor 210, which reconfigures the one or more neural networks with the updated configuration. As a result, processor 210 thereafter generates ADL identification information for subsequent time periods using the one or more neural networks with the updated configuration. In some embodiments, all of the one or more neural networks are updated with new configurations at the same time. In some embodiments, or in some circumstances, just one of the neural networks is updated with a new configuration, or a subset of the neural networks are updated with new configurations when one or more updated configurations are received by device 104. In some embodiments, the updated configuration allows for more accurate user-specific ADL identification schemes, based on analysis of previously received raw ADL data. For example, the updated configuration allows for personally targeted ADL identification schemes based on the user's personal habits (e.g., washing hair in the sink versus in the shower) or the user's historical activities (e.g., dancing, petting animals, etc.). In some embodiments, the updated configuration is customized in accordance with the geographic region of the user or cultural or other classifications associated with the user, or a combination thereof. In some embodiments, transceiver 214 is a wireless transceiver, while in other embodiments, transceiver 214 is a wired transceiver.
In some embodiments, rechargeable battery 216 is disposed within the housing, and processor 210 performs a predefined set of tasks while device 104 is determined to be connected to an external power source for recharging the battery. In some embodiments, the predefined set of tasks includes transmitting (e.g., through transceiver 214) recorded information that was not transmitted while the system was not connected to the external power source. Further, in some embodiments, the predefined set of tasks includes receiving (e.g., through transceiver 214) update information for reconfiguring at least one aspect of device 104 (e.g., an updated configuration for the one or more neural networks as disclosed above).
The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various embodiments with various modifications as are suited to the particular use contemplated.
This application claims priority to U.S. Provisional Patent Application 62/590,140, filed Nov. 22, 2017, and to Provisional Patent Application 62/505,784, filed May 12, 2017, each of which is hereby incorporated by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 62590140 | Nov 2017 | US | |
| 62505784 | May 2017 | US |
