Patent Issued for Vital signs with non-contact activity sensing network for elderly care (USPTO 11114206): Tellus You Care Inc.

2021 SEP 27 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Alexandria, Virginia, by NewsRx journalists, a patent by the inventors Coke, Tania Abedian (San Francisco, CA, US), Hsu, Kevin (San Francisco, CA, US), filed on October 2, 2018, was published online on September 7, 2021.

The assignee for this patent, patent number 11114206, is Tellus You Care Inc. (San Francisco, California, United States).

Reporters obtained the following quote from the background information supplied by the inventors: “Quick aging of world’s population presents an ever growing list and magnitude of challenges to our civilization, ranging from dramatic changes in the modern workforce size and composition, in economy and health insurance, to transformations in family lifestyles and in elderly care structure and costs.

“According to the United Nations 2017 World Population Ageing report, the global population aged 60+ years has more than doubled to 962 million in 2017 compared with 1980 and is expected to double again by 2050 to reach approximately 2.1 billion. Additionally, the number of persons aged 80 years or over will grow even faster than the above rate and is expected to triple between 2017 and 2050 from 137 million to 425 million. By 2030, older persons (aged 60+ years) will outnumber children under the age of 10 and by 2050 will exceed the number of adolescents and youth aged 10-24 years (2.1 billion vs 2 billion). Percentagewise, by 2050, an older population is expected to account for 35 percent of the population in Europe, 28 percent in North America and 25 percent in Latin America.

“Today, Japan is the oldest country in the world, with 33.4 percent of population aged 60+ years (4 percent higher than the second oldest country, Italy, with 29.4 percent of 60+ persons). Japan is expected to remain the oldest country in the world until at least 2050 when Japan will have an estimated 42.4 percent of population aged over 60 years.

“Population aging in developed countries is a result of two main factors: low fertility rate and increased life expectancy. Thus, in Japan and in some other countries where the overall population is shrinking due to low fertility rates, some of the world’s highest life expectancies for both men and women cause rapid increase of the aging population percentage.

“In addition to the aging world’s population, there is a significant and growing trend, where more and more aged people are living alone. On average, the share of aged people living independently (alone or with spouse only) has increased from 24 percent in 1990 to 37 percent in 2010. The share of independently living aged people is much higher in many developed countries: in the “more developed regions”, by the UN classification, the percent of independently living elderly people approaches 75 percent, of which, on the aggregate, 34.5 percent of females and 17 percent of males are living alone.

“With the above population aging trends in mind, long-term elderly care (LTC) becomes a problem of national significance in an increasing number of countries. It is estimated that about 70 percent of individuals over age of 65 will require some type of long-term care services during their lifetime. Governments, businesses and non-profit organizations around the world are offering numerous long-term care options. In particular, Long-term care insurance is becoming an organic part of national healthcare insurance systems (available, for example, in the US from private insurers since late 1970’s and in Japan municipally since 2000).

“Long-term care providers and services in the US include adult day services centers, home health agencies, hospices, nursing homes, and residential care communities. According to a comprehensive 2014 CDC study of long-term care providers, services and users in the US, over six million individuals aged over 65 are receiving help from approximately 63,000 of the above providers and services, including 15600 nursing homes, 30200 residential care communities and excluding 4000 hospices, which provide care to an additional 1.34 million individuals. In Japan, around 25000 specialized long-term care facilities provide long-term elderly care.

“One of the most important tasks of the long-term care system for aged individuals is permanent non-invasive monitoring of their condition to ensure their secure stay in long-term care facilities, as well as adequate performance of Activities of Daily Living (ADLs) and prevention of traumas, such as falls (which are known to occur, in particular, for 16 percent of elderly patients at skilled nursing facilities). Video cameras and other invasive tracking methods don’t satisfy privacy requirements of elder individuals and may not be used on a permanent basis. Multiple solutions utilizing Wi-Fi networks and compact radars have been recently proposed for the purpose of tracking elderly individuals and other audiences. Examples include the Radar Health Monitor, developed by Kyoto University in partnership with Panasonic and Aura Wi-Fi motion technology by Cognitive Systems Corp.

“Notwithstanding noticeable progress in the development of non-invasive tracking technologies and systems for the elderly individuals, the existing solutions are still rudimentary and don’t address the majority of issues and tasks at hand. Wi-Fi motion technology uses low-frequency signals and receives negative reviews for its imprecision and failures to detect individual’s status. The Radar Health Monitor is focused on monitoring vital signs of individuals, such as a heart rate, which requires static positioning of the individuals; but the existing solution cannot distinguish between a walking, standing and sitting individual and therefore accuracy and even applicability of its measurements at a specific moment may be questionable.

“Accordingly, it is desirable to create a technology and system for a comprehensive, non-invasive and intelligent monitoring of elderly individuals.”

In addition to obtaining background information on this patent, NewsRx editors also obtained the inventors’ summary information for this patent: “According to the system described herein, determining a physical state of a person includes detecting positions of different portions of the person, transforming detected positions of the person into a point cloud having a density that varies according to movement of each of the portions, correlating movement and position data from the point cloud with known physical state positions and transitions between different states, choosing a particular physical state by matching the data from the point cloud with the particular physical state, and obtaining vital signs of the person during an optimal period of time for automatic capturing of vital signs by detecting when the person is in a particular state. The particular state may be a static state. The static state may be standing, sitting, or laying down. The vital signs may include measuring a breathing rate and measuring a heartbeat rate. Vital signs may be obtained by detecting pulsations in the point cloud representing breathing and heartbeats. Positions of different portions of the person may be detected using a non-contact detector. The non-contact detector may be provided by reflections of a radar signal from a wide-band radar. Measured vital signs of the person may be used to detect a dangerous situation for the person. The dangerous situation may be sleep deprivation, sleep apnea, stoppage of breathing, heart disease, arrhythmia, Parkinson’s, and/or Alzheimer’s. Each of the states may be associated with point densities, sizes, orientations, centers of gravity, and dispositions of bounding boxes of the point clouds. Parametric representations of the bounding boxes, the point densities and positions of the centers of gravity of samples of different states may be provided as input to a neural network classifier. The neural network may be trained by providing the neural network on a server in a cloud computing system that receives data from tracking devices that detect positions of different portions of the person and communicate wirelessly with the cloud computing system. The neural network may be a long short-term memory recurrent neural network. The neural network classifier may correlate movement and position data from the point cloud with known physical state positions and transitions between different states to choose the particular physical state.

“According further to the system described herein, a non-transitory computer readable medium contains software that determines a physical state of a person. The software includes executable code that detects positions of different portions of the person, executable code that transforms detected positions of the person into a point cloud having a density that varies according to movement of each of the portions, executable code that correlates movement and position data from the point cloud with known physical state positions and transitions between different states, executable code that chooses a particular physical state by matching the data from the point cloud with the particular physical state, and executable code that obtains vital signs of the person during an optimal period of time for automatic capturing of vital signs by detecting when the person is in a particular state. The particular state may be a static state. The static state may be standing, sitting, or laying down. The vital signs may include measuring a breathing rate and measuring a heartbeat rate. Vital signs may be obtained by detecting pulsations in the point cloud representing breathing and heartbeats. Positions of different portions of the person may be detected using a non-contact detector. The non-contact detector may be provided by reflections of a radar signal from a wide-band radar. Measured vital signs of the person may be used to detect a dangerous situation for the person. The dangerous situation may be sleep deprivation, sleep apnea, stoppage of breathing, heart disease, arrhythmia, Parkinson’s, and/or Alzheimer’s. Each of the states may be associated with point densities, sizes, orientations, centers of gravity, and dispositions of bounding boxes of the point clouds. Parametric representations of the bounding boxes, the point densities and positions of the centers of gravity of samples of different states may be provided as input to a neural network classifier. The neural network may be trained by providing the neural network on a server in a cloud computing system that receives data from tracking devices that detect positions of different portions of the person and communicate wirelessly with the cloud computing system. The neural network may be a long short-term memory recurrent neural network. The neural network classifier may correlate movement and position data from the point cloud with known physical state positions and transitions between different states to choose the particular physical state.

“According further to the system described herein, determining a physical state of a person includes detecting positions of different portions of the person, transforming detected positions of the person into a point cloud having a density that varies according to movement of each of the portions, correlating movement and position data from the point cloud with known physical state positions and transitions between different states, and choosing a particular physical state by matching the data from the point cloud with the particular physical state. Positions of different portions of the person may be detected using a tracking device. The tracking device may be a non-contact tracking device. The tracking device may include at least one wide band radar. The tracking devices may communicate wirelessly with at least one server in a cloud computing system. The states may include walking, standing, sitting, laying down, turning in bed, falling, and/or departed. Falling may be detected in response to the person transitioning from the walking state to the laying down state. During the transitioning, the person may be detected as having a progressively lower center of gravity of the point cloud. An audio generating device may communicate with the person following a fall to confirm the fall and offer basic instructions to the person. A caregiver may be contacted if the person does not move or communicate following the audio generating device communicating with the person. The audio generating device may include a microphone that receives audio communication from the person. Each of the states may be associated with point densities, sizes, orientations, centers of gravity, and dispositions of bounding boxes of the point clouds. Parametric representations of the bounding boxes, the point densities and positions of the centers of gravity of samples of different states may be provided as input to a neural network classifier. The neural network may be trained by providing the neural network on a server in a cloud computing system that receives data from tracking devices that detect positions of different portions of the person and communicate wirelessly with the cloud computing system. The neural network may be a long short-term memory recurrent neural network. The neural network classifier may correlate movement and position data from the point cloud with known physical state positions and may transition between different states to choose the particular physical state. Determining a physical state of a person may also include maintaining information corresponding to customary routine state transitions and locations of the person. Customary routine state transitions and locations may be determined by detecting clusters of points in a multi-dimensional space of sequences of objects, time intervals, locations, and state transitions that represent complex user behaviors. An alarm may be provided to a caretaker in response to the person deviating from the customary routine state transitions and locations. Deviating from the customary routine state transitions and locations may include cycling around a room for a prolonged period of time or repetitively moving back and forth between two objects. The clusters of points corresponding to customary routines may be provided to a cloud computing system for comparison with clusters of points corresponding to customary routines for other people to further categorize behaviors and improve detection of dangerous situations. The person may be in a room and objects in the room may be initially detected by monitoring absolute coordinates of a bounding box of the point cloud in various user states. The objects may include a bed, a table, a chair, a bookshelf, a door, and/or a window. Objects in the room may be detected by subtracting multiple positions of the bounding box from the area of the room. A bed may be detected by observing the person in a laying down state at a certain height off the floor. Boundaries of the bed may be determined by tracking bounding boxes corresponding to a laying down state, a sitting state either before or after entering the laying down state, and a standing state prior to entering the sitting. A bed or a couch may be detected by observing adjacent states of standing, sitting and laying down at a position corresponding to the bed or the couch. A window may be detected by observing the person standing a relatively long time at a boundary of the room.”

There is additional summary information. Please visit full patent to read further.

The claims supplied by the inventors are:

“1. A method of determining a physical state of a person, comprising: detecting positions of different portions of the person; transforming detected positions of the person into a point cloud having a density that varies according to movement of each of the portions; correlating movement and position data from the point cloud with known physical state positions and transitions between different states; choosing a particular physical state by matching the data from the point cloud with the particular physical state; and obtaining vital signs of the person during an optimal period of time for automatic capturing of vital signs by detecting when the person is in a particular state.

“2. A method, according to claim 1, wherein the particular state is a static state.

“3. A method, according to claim 2, wherein the static state is one of: standing, sitting, or laying down.

“4. A method, according to claim 1, wherein the vital signs include measuring a breathing rate and measuring a heartbeat rate.

“5. A method, according to claim 4, wherein vital signs are obtained by detecting pulsations in the point cloud representing breathing and heartbeats.

“6. A method, according to claim 5, wherein positions of different portions of the person are detected using a non-contact detector.

“7. A method, according to claim 6, wherein the non-contact detector is provided by reflections of a radar signal from a wide-band radar.

“8. A method, according to claim 1, wherein measured vital signs of the person are used to detect a dangerous situation for the person.

“9. A method, according to claim 8, wherein the dangerous situation is one or more of: sleep deprivation, sleep apnea, stoppage of breathing, heart disease, arrhythmia, Parkinson’s, or Alzheimer’s.

“10. A method, according to claim 1, wherein each of the states is associated with point densities, sizes, orientations, centers of gravity, and dispositions of bounding boxes of the point clouds.

“11. A method, according to claim 10, wherein parametric representations of the bounding boxes, the point densities and positions of the centers of gravity of samples of different states are provided as input to a neural network classifier.

“12. A method, according to claim 11, wherein the neural network is trained by providing the neural network on a server in a cloud computing system that receives data from tracking devices that detect positions of different portions of the person and communicate wirelessly with the cloud computing system.

“13. A method, according to claim 12, wherein the neural network is a long short-term memory recurrent neural network.

“14. A method, according to claim 11, wherein the neural network classifier correlates movement and position data from the point cloud with known physical state positions and transitions between different states to choose the particular physical state.

“15. A non-transitory computer readable medium containing software that determines a physical state of a person, the software comprising: executable code that detects positions of different portions of the person; executable code that transforms detected positions of the person into a point cloud having a density that varies according to movement of each of the portions; executable code that correlates movement and position data from the point cloud with known physical state positions and transitions between different states; executable code that chooses a particular physical state by matching the data from the point cloud with the particular physical state; and executable code that obtains vital signs of the person during an optimal period of time for automatic capturing of vital signs by detecting when the person is in a particular state.

“16. A non-transitory computer readable medium, according to claim 15, wherein the particular state is a static state.

“17. A non-transitory computer readable medium, according to claim 16, wherein the static state is one of: standing, sitting, or laying down.

“18. A non-transitory computer readable medium, according to claim 15, wherein the vital signs include measuring a breathing rate and measuring a heartbeat rate.

“19. A non-transitory computer readable medium, according to claim 18, wherein vital signs are obtained by detecting pulsations in the point cloud representing breathing and heartbeats.

“20. A non-transitory computer readable medium, according to claim 19, wherein positions of different portions of the person are detected using a non-contact detector.

“21. A non-transitory computer readable medium, according to claim 20, wherein the non-contact detector is provided by reflections of a radar signal from a wide-band radar.

“22. A non-transitory computer readable medium, according to claim 15, wherein measured vital signs of the person are used to detect a dangerous situation for the person.

“23. A non-transitory computer readable medium, according to claim 22, wherein the dangerous situation is one or more of: sleep deprivation, sleep apnea, stoppage of breathing, heart disease, arrhythmia, Parkinson’s, or Alzheimer’s.”

For more information, see this patent: Coke, Tania Abedian. Vital signs with non-contact activity sensing network for elderly care. U.S. Patent Number 11114206, filed October 2, 2018, and published online on September 7, 2021. Patent URL: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=11114206.PN.&OS=PN/11114206RS=PN/11114206

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