WO2023158619A1 - Fall prevention system - Google Patents

Fall prevention system Download PDF

Info

Publication number
WO2023158619A1
WO2023158619A1 PCT/US2023/012944 US2023012944W WO2023158619A1 WO 2023158619 A1 WO2023158619 A1 WO 2023158619A1 US 2023012944 W US2023012944 W US 2023012944W WO 2023158619 A1 WO2023158619 A1 WO 2023158619A1
Authority
WO
WIPO (PCT)
Prior art keywords
bed
executive
machine learning
height
determination
Prior art date
Application number
PCT/US2023/012944
Other languages
French (fr)
Inventor
Lawrence HUSICK
Original Assignee
Husick Lawrence
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Husick Lawrence filed Critical Husick Lawrence
Publication of WO2023158619A1 publication Critical patent/WO2023158619A1/en

Links

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
    • G08B21/0407—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons based on behaviour analysis
    • G08B21/043—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons based on behaviour analysis detecting an emergency event, e.g. a fall
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1116—Determining posture transitions
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1116—Determining posture transitions
    • A61B5/1117—Fall detection
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0475—Generative networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/10—Interfaces, programming languages or software development kits, e.g. for simulating neural networks
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • A61B5/7455—Details of notification to user or communication with user or patient ; user input means characterised by tactile indication, e.g. vibration or electrical stimulation
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
    • G08B21/0438—Sensor means for detecting
    • G08B21/0476—Cameras to detect unsafe condition, e.g. video cameras
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

  • FallGAI Feall Generative Artificial Intelligence
  • FallGAI uses deep learning and reinforcement learning Al, supplemented by generative adversarial networks, classical rule and data-driven expert systems, literature extraction, relational, object and big data models, and a historically-aware world model to produce high fidelity Al-driven results.
  • Older persons are more likely experience falls and fall injuries in particular activities of daily living, and that these may be identified and the risk quantified and then mitigated.
  • behaviors are segmented by context, and position, posture, center of gravity and velocity and are sampled to characterize each context using machine learning coupled with a generative adversarial network for training and refinement. Edge cases tending to precede falls are identified for each context. This model is then validated against passive observations.
  • “Nudge” interventions are successfully be used to alter behavior away from those actions that present enhanced risk of fall and injury, and a combination of explicit interventions and nudges is believed to be most effective in reducing fall risk and fall injury in order persons.
  • Effectors using Internet of Things (loT) technologies, as well as purpose-built effectors are deployed, actuated in context and the results evaluated by observation.
  • One object of FallGAI is to prevent the majority of fall-induced injuries, first in in-patient settings (hospitals, skilled nursing facilities and assisted living settings), and then later in independent living settings and aging in place in seniors’ homes.
  • the system of the present invention uses sensors that detect the position and posture of human subjects without requiring either worn devices or physical contact. Sensors are deployed in healthcare facilities and residential settings and transduced information is used to refine the machine learning models of the system.
  • FallGAI uses integrated multimodal sensing (RADAR, LiDAR, LowRez Video, IRVideo, Audio, Temperature, Air Quality, force sensing, etc.) and deep machine learning Al to learn human behavior in context. FallGAI applies a postural center of gravity dynamic model to predict the probability of falls and to warn and intervene, based on that probability, before a fall occurs.
  • FallGAI intervenes in many ways. It uses visual and auditory warnings, notifies facility staff, employs vibrotactile stimulation, and controls environment factors in real time to prevent falls.
  • FallGAI does not require worn devices of physical contact, it is able to integrate information derived from such devices, and also address them to provide information and feedback to the wearer.
  • an Apple Watch Apple, Inc.
  • an Apple Watch may be provided with an “app” (application software module) to enable auditory and haptic cues, as well as to provide positional and acceleration/velocity information to the FallGAI system.
  • apps application software module
  • Al uses PyTorch, an off the shelf machine learning tool that makes it simple to set up generative adversarial networks, and operates in the Amazon Web Services cloud, if directed to do so.
  • AWS cloud is used for training, however the production version of FallGAI uses embedded computing and "edge computing” to avoid network latency that results from large sensor data rates.
  • a machine vision motion-capture system determines pose, center of gravity and velocity, and using a generative adversarial network classifies both present and likely near term future pose, center of gravity and velocity to identify fall risk.
  • Examples of such systems include FreeMoCap (www.freemocap.org). Chordata (www.chordata.ee), and EasyMocap (http s : // github . com/ zj u3 dv/EasyMocap) .
  • markerless capture of motion provides pose data in both static and dynamic regimes to permit both identification of an individual within the field of view of the system, as well as changes in pose in near-real-time to permit extrapolation of stability, frailty and other parameters of motion.
  • poses representing both normal activities are classified, as well as those poses representing undesired activities (resting prone on a floor, kneeling adjacent to furniture or a wall or door, etc.).
  • dynamic changes in pose are also learned and classified (rising from seated to standing position, lowering from standing to seated position, transitioning from seated to prone positing in bed, etc.)
  • a system executive using data-driven entropy minimization and Baysian processing uses postural and positional data derived from machine vision and motion tracking models to determine human behavioral context (e.g., is the person about to arise from bed or chair, seated on a toilet, entering or exiting a shower, dressing, etc.) This context is used to select from among various Al modules, each having specialized training related to the context activated by the executive.
  • the executive takes action (warn, increase lighting intensity, vibrationally stimulate, lower or raise bed or chair height, etc.) to reduce the probability of fall and injury through a network of both commercial off the shelf (COTS) loT devices and bespoke actuators and effectors developed specifically for FallGAI.
  • COTS commercial off the shelf
  • the system actuators is an interface to a hospital bed that, upon receipt of an actuating signal, engages the height control circuitry of the bed to adjust the bed to a predetermined height above the floor.
  • a predetermined height above the floor For example, the lowest height during sleep, or a height of about 1.3x the length of the bed occupant’s tibia at times when transitions to and from bed are likely.
  • the system reduces the probability of fall injury should the bed occupant fall from the bed, and also reduces the ability of an older person to exit the bed, thus reducing the likelihood of falls.
  • the same type of interface may be used with a chair having adjustable height, to achieve similar results.
  • Another bespoke actuator is a vibrotactile actuator embedded in a toilet seat. Because falls during toileting are a significant risk for injury, and many occur because the occupant become drowsy or falls asleep, the system of the present invention may sense lack of stability and incipient sleep. In response, the system may trigger the vibrotactile actuator according to one or more strengths and/or patterns to alert and awaken the occupant, thus reducing the probability of falls and resultant injuries.
  • the postural dynamics dataset generated will inform ancillary health assessments such as impairment from stroke, vestibular instability, positional hypertension, and many other conditions, aiding telemedicine and direct contact providers.
  • the first embodiment of the present invention uses a commercial 60GHz RADAR (Aqara),, motion capture software (FreeMoCap), and a PyTorch implementation of the system executive, embedding several explicit contexts (bed exit, toileting, chair exit, ambulation) and link to a few effectors (COTS loT devices.)
  • Aqara 60GHz RADAR
  • FreeMoCap motion capture software
  • PyTorch implementation of the system executive

Landscapes

  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Molecular Biology (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Artificial Intelligence (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Software Systems (AREA)
  • Mathematical Physics (AREA)
  • Computing Systems (AREA)
  • Medical Informatics (AREA)
  • Pathology (AREA)
  • Computational Linguistics (AREA)
  • Veterinary Medicine (AREA)
  • Public Health (AREA)
  • General Engineering & Computer Science (AREA)
  • Animal Behavior & Ethology (AREA)
  • Surgery (AREA)
  • Physiology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Data Mining & Analysis (AREA)
  • Psychiatry (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Dentistry (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Signal Processing (AREA)
  • Emergency Management (AREA)
  • Business, Economics & Management (AREA)
  • Gerontology & Geriatric Medicine (AREA)
  • Social Psychology (AREA)
  • Psychology (AREA)
  • Fuzzy Systems (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)

Abstract

The system of the present invention uses sensors that detect the position and posture of human subjects without requiring either worn devices or physical contact. Using integrated multimodal sensing (RADAR, LiDAR, LowRezVideo, IRVideo, Audio, Temperature, Air Quality, force sensing, etc.) and deep machine learning AI to learn human behavior in context, the invention applies a postural center of gravity dynamic model to predict the probability of falls and to warn and intervene, using visual and auditory warnings, notices to facility staff, vibrotactile stimulation, and controls of environment factors in real time, before a fall occurs.

Description

Fall Prevention System
Background of the Invention
More than half of Americans aged 65+ experience a fall each year, costing the US healthcare system over $55B and the nation over 30,000 lives. Falling once doubles the likelihood or a second fall. The system and method of the present invention (termed “FallGAI” (Fall Generative Artificial Intelligence) for commercial purposes) is a ubiquitous, yet unobtrusive network of inexpensive sensors driven by artificial intelligence (Al) to not only predict falls, but to intervene in order prevent them. Designed to be installed in hospitals, senior centers, assisted living centers, and the homes of senior citizens, FallGAI uses deep learning and reinforcement learning Al, supplemented by generative adversarial networks, classical rule and data-driven expert systems, literature extraction, relational, object and big data models, and a historically-aware world model to produce high fidelity Al-driven results.
Older persons are more likely experience falls and fall injuries in particular activities of daily living, and that these may be identified and the risk quantified and then mitigated. Using multimodal sensing, behaviors are segmented by context, and position, posture, center of gravity and velocity and are sampled to characterize each context using machine learning coupled with a generative adversarial network for training and refinement. Edge cases tending to precede falls are identified for each context. This model is then validated against passive observations.
“Nudge” interventions are successfully be used to alter behavior away from those actions that present enhanced risk of fall and injury, and a combination of explicit interventions and nudges is believed to be most effective in reducing fall risk and fall injury in order persons. Effectors using Internet of Things (loT) technologies, as well as purpose-built effectors are deployed, actuated in context and the results evaluated by observation.
The present state of the art in dealing with falls among seniors is best exemplified by the trademark slogan, “I’ve fallen and I can’t get up!” (LifeCall Systems) Rather than prevent falls, current systems concentrate on detecting them (or more commonly, having the person who has just fallen take action) and providing assistance and medical care after they occur. Healthy aging requires that older persons not sustain life-threatening injuries from preventable falls.
One object of FallGAI is to prevent the majority of fall-induced injuries, first in in-patient settings (hospitals, skilled nursing facilities and assisted living settings), and then later in independent living settings and aging in place in seniors’ homes.
Description of the Invention
The system of the present invention uses sensors that detect the position and posture of human subjects without requiring either worn devices or physical contact. Sensors are deployed in healthcare facilities and residential settings and transduced information is used to refine the machine learning models of the system.
FallGAI uses integrated multimodal sensing (RADAR, LiDAR, LowRez Video, IRVideo, Audio, Temperature, Air Quality, force sensing, etc.) and deep machine learning Al to learn human behavior in context. FallGAI applies a postural center of gravity dynamic model to predict the probability of falls and to warn and intervene, based on that probability, before a fall occurs.
Rather than merely detect and respond after a fall occurs, FallGAI intervenes in many ways. It uses visual and auditory warnings, notifies facility staff, employs vibrotactile stimulation, and controls environment factors in real time to prevent falls.
Although FallGAI does not require worn devices of physical contact, it is able to integrate information derived from such devices, and also address them to provide information and feedback to the wearer. For instance, an Apple Watch (Apple, Inc.) may be provided with an “app” (application software module) to enable auditory and haptic cues, as well as to provide positional and acceleration/velocity information to the FallGAI system. It must be recognized, however, that older individuals and those with cognitive impairment often forget to don such devices, or to ensure that their batteries are fully charged, and thus, reliance on these devices may impair the operation of any system that critically depends on their operation.
Implementation of the Al uses PyTorch, an off the shelf machine learning tool that makes it simple to set up generative adversarial networks, and operates in the Amazon Web Services cloud, if directed to do so. AWS cloud is used for training, however the production version of FallGAI uses embedded computing and "edge computing” to avoid network latency that results from large sensor data rates.
A machine vision motion-capture system determines pose, center of gravity and velocity, and using a generative adversarial network classifies both present and likely near term future pose, center of gravity and velocity to identify fall risk. Examples of such systems include FreeMoCap (www.freemocap.org). Chordata (www.chordata.ee), and EasyMocap (http s : // github . com/ zj u3 dv/EasyMocap) . Ideally, markerless capture of motion provides pose data in both static and dynamic regimes to permit both identification of an individual within the field of view of the system, as well as changes in pose in near-real-time to permit extrapolation of stability, frailty and other parameters of motion. Using machine learning, poses representing both normal activities (sitting in a chair, on a bed, on a commode, resting prone in bed, standing) are classified, as well as those poses representing undesired activities (resting prone on a floor, kneeling adjacent to furniture or a wall or door, etc.). Similarly, dynamic changes in pose are also learned and classified (rising from seated to standing position, lowering from standing to seated position, transitioning from seated to prone positing in bed, etc.)
A system executive using data-driven entropy minimization and Baysian processing uses postural and positional data derived from machine vision and motion tracking models to determine human behavioral context (e.g., is the person about to arise from bed or chair, seated on a toilet, entering or exiting a shower, dressing, etc.) This context is used to select from among various Al modules, each having specialized training related to the context activated by the executive.
The executive takes action (warn, increase lighting intensity, vibrationally stimulate, lower or raise bed or chair height, etc.) to reduce the probability of fall and injury through a network of both commercial off the shelf (COTS) loT devices and bespoke actuators and effectors developed specifically for FallGAI.
It is well known that persons experiencing neuropathy in the feet are more likely to fall because of reduced proprioception. Such persons may be aided by good lighting, as visual feedback and cues augment one’s sense of position in space and proper placement of the feet to support standing and ambulation. In such cases, actuation of loT dimmers and switches in response to system-sensed changes in location and position may be employed to reduce the probability of falls and resultant injuries.
Among the system actuators is an interface to a hospital bed that, upon receipt of an actuating signal, engages the height control circuitry of the bed to adjust the bed to a predetermined height above the floor. (For example, the lowest height during sleep, or a height of about 1.3x the length of the bed occupant’s tibia at times when transitions to and from bed are likely.) For the lowest bed position, the system reduces the probability of fall injury should the bed occupant fall from the bed, and also reduces the ability of an older person to exit the bed, thus reducing the likelihood of falls. The same type of interface may be used with a chair having adjustable height, to achieve similar results.
Another bespoke actuator is a vibrotactile actuator embedded in a toilet seat. Because falls during toileting are a significant risk for injury, and many occur because the occupant become drowsy or falls asleep, the system of the present invention may sense lack of stability and incipient sleep. In response, the system may trigger the vibrotactile actuator according to one or more strengths and/or patterns to alert and awaken the occupant, thus reducing the probability of falls and resultant injuries.
Preventing a significant fraction of in-patient fall injuries will shorten length of stay, reduce complications, reduce legal liabilities, and free staff time to attend to primary medical needs. In today’s world of staff shortages and lengthening response times to patient calls for assistance (average is now over 13 minutes), leveraging automation to reduce frequent rounding and enforced toileting benefits care staff directly.
The reduction of in-home fall incidence will encourage aging in place, which has been shown to improve quality of life, reduce cost, and extend longevity.
The postural dynamics dataset generated will inform ancillary health assessments such as impairment from stroke, vestibular instability, positional hypertension, and many other conditions, aiding telemedicine and direct contact providers.
The first embodiment of the present invention uses a commercial 60GHz RADAR (Aqara),, motion capture software (FreeMoCap), and a PyTorch implementation of the system executive, embedding several explicit contexts (bed exit, toileting, chair exit, ambulation) and link to a few effectors (COTS loT devices.) System Components
Single-board computers - Raspberry Pi4B or similar and peripheral hardware
Sensors - 60-90GHz RADAR
20-40GHz Doppler RADAR NearIR Video Camera
Low Resolution Video Cameras
LIDAR
Ultrasonic Standing Wave Detector
Effectors - loT switches, dimmers, etc. Relay controller (purpose built)
Vibrotactiles

Claims

I claim as my invention:
1. A system for reducing the incidence of injury-producing falls among older adults comprising a general purpose digital computer system running a system executive artificial intelligence machine learning application; one or more non-contacting sensors selected from the group comprising RADAR, Doppler RADAR, NearIR Video, Visible Light Low Resolution Video, LIDAR, and Ultrasonic Standing Wave Detectors interfaced to the computer system to provide sensor information to the system executive; a motion capture application running on the computer system to identify the position, pose and velocity of one or more persons from the sensor information; and one or more effectors selected from the group comprising Internet-of-Things devices, relay controllers, and vibrotactile devices; wherein one or more selected effectors are actuated by the system executive when the system executive detects, based on position, pose, velocity and learned information, that a fall is more likely to occur than not.
2. The system of Claim 1 wherein the system executive artificial intelligence machine learning application is a specially configured version of PyTorch.
3. The system of Claim 1 wherein the system executive artificial intelligence machine learning application learns, at least in part, using generative artificial intelligence methods.
4. The system of Claim 1 wherein at least one effector controls the height of a bed above the floor.
5. The system of Claim 4 wherein the height of a bed is reduced to a minimum height upon determination by the system executive that the bed occupant is prone and asleep.
6. The system of Claim 4 wherein the height of a bed is set to about 1.3 times the length of the tibia of the bed occupant upon determination by the system executive that the older adult bed occupant is awake and may exit the bed.
7. The system of Claim 1 wherein one or more Internet-of-Things devices are actuated to increase illumination intensity upon a determination by the system executive that the older adult is about to ambulate.
8. The system of Claim 1 wherein at least one effector is a vibrotactile device physically connected to a toilet seat.
9. The system of Claim 8 wherein the vibrotactile effector is actuated upon determination by the system executive that the older adult toilet occupant is about to fall asleep.
PCT/US2023/012944 2022-02-15 2023-02-13 Fall prevention system WO2023158619A1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202263310278P 2022-02-15 2022-02-15
US63/310,278 2022-02-15

Publications (1)

Publication Number Publication Date
WO2023158619A1 true WO2023158619A1 (en) 2023-08-24

Family

ID=87578970

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2023/012944 WO2023158619A1 (en) 2022-02-15 2023-02-13 Fall prevention system

Country Status (1)

Country Link
WO (1) WO2023158619A1 (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060258915A1 (en) * 2003-06-20 2006-11-16 Matsushita Electric Industrial Co., Ltd. Vibration-detecting device and toilet seat
US20090044334A1 (en) * 2007-08-13 2009-02-19 Valence Broadband, Inc. Automatically adjusting patient platform support height in response to patient related events
US20150109442A1 (en) * 2010-09-23 2015-04-23 Stryker Corporation Video monitoring system
US20200205746A1 (en) * 2018-12-27 2020-07-02 Starkey Laboratories, Inc. Predictive fall event management system and method of using same
US20210052221A1 (en) * 2019-08-23 2021-02-25 Vitaltech Properties, Llc System, method, and smartwatch for protecting a user
US20210063578A1 (en) * 2019-08-30 2021-03-04 Nvidia Corporation Object detection and classification using lidar range images for autonomous machine applications
US20220084383A1 (en) * 2020-09-14 2022-03-17 Curbell Medical Products, Inc. System and method for monitoring an individual using lidar
US20220180723A1 (en) * 2020-12-09 2022-06-09 MS Technologies Doppler radar system with machine learning applications for fall prediction and detection

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060258915A1 (en) * 2003-06-20 2006-11-16 Matsushita Electric Industrial Co., Ltd. Vibration-detecting device and toilet seat
US20090044334A1 (en) * 2007-08-13 2009-02-19 Valence Broadband, Inc. Automatically adjusting patient platform support height in response to patient related events
US20150109442A1 (en) * 2010-09-23 2015-04-23 Stryker Corporation Video monitoring system
US20200205746A1 (en) * 2018-12-27 2020-07-02 Starkey Laboratories, Inc. Predictive fall event management system and method of using same
US20210052221A1 (en) * 2019-08-23 2021-02-25 Vitaltech Properties, Llc System, method, and smartwatch for protecting a user
US20210063578A1 (en) * 2019-08-30 2021-03-04 Nvidia Corporation Object detection and classification using lidar range images for autonomous machine applications
US20220084383A1 (en) * 2020-09-14 2022-03-17 Curbell Medical Products, Inc. System and method for monitoring an individual using lidar
US20220180723A1 (en) * 2020-12-09 2022-06-09 MS Technologies Doppler radar system with machine learning applications for fall prediction and detection

Similar Documents

Publication Publication Date Title
JP6823160B2 (en) Devices, systems, and methods for patient monitoring to predict and prevent bed falls
CN108601560A (en) Personnel monitor and personal help system, are especially used for the elderly and with special and understanding demand people
EP2575113A1 (en) Method and device for fall detection and a system comprising such device
JP6692987B2 (en) Devices, Systems, and Methods for Patient Monitoring to Predict and Prevent Bed Fall
EP3158502B1 (en) Furniture occupancy monitoring
Ganyo et al. Ethical issues in the use of fall detectors
JP2019537082A (en) Devices, systems and methods for patient monitoring to predict and prevent bed falling
WO2019013257A1 (en) Monitoring assistance system and method for controlling same, and program
JP2019535052A (en) Device, system, and method for patient monitoring to predict and prevent bed fall
US20210219873A1 (en) Machine vision to predict clinical patient parameters
US11941506B2 (en) System and method for monitoring via smart devices
WO2023158619A1 (en) Fall prevention system
O'Brien et al. Survey of assistive technology devices and applications for aging in place
JP5517285B2 (en) Wake-up monitoring device
Kumar et al. Architecture for IOT based geriatric care fall detection and prevention
KR102608941B1 (en) Abnormal behavior detecting system using artificial intelligence
Nchena Smart House Assistive Technologies for Senior Citizens
Chiridza et al. A Smart Home environment to support risk monitoring for the elderly living independently
US11881096B2 (en) System and method for performing mobility management using haptic guidance
Moshnyaga et al. Identification of basic behavioral activities by heterogeneous sensors of in-home monitoring system
Sophia et al. Ambient‐Assisted Living of Disabled Elderly in an Intelligent Home Using Behavior Prediction—A Reliable Deep Learning Prediction System
JP7314939B2 (en) Image recognition program, image recognition device, learning program, and learning device
O'Brien et al. Design and implementation of an embedded system for monitoring at-home solitary Alzheimer's patients
Heng et al. Internet of Things (IoT) Based Patient Fall Prediction and Monitoring System
Crespo et al. Monitoring Report of the DSH Project (Sensory Device for Homes)

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23756792

Country of ref document: EP

Kind code of ref document: A1