Patent Application
20250152043
Health, activity and fitness interests of consumers have been increasing immensely. This increase is caused mostly by an increased understanding of the connection between physical activity and wellbeing. Assessing user performance on clinically relevant tests such as timed-up-and-go and 30 seconds sit-to-stand tests in addition to tracking daily activities is important to get clinically meaningful data on user wellbeing. Also, by assessing user performance on tests such as gait speed, 30-seconds sit-to-stand test, dual-task walking test or timed-up-and-go it is possible analyze user mobility, physical function, executive function and cognitive function.
Falls may also have a significant impact on a person's health and wellbeing. Therefore, fall risk assessment, predicting falls, and analyzing possible reasons of falls in order to provide relevant feedback that can possibly reduce fall risk or prevent falls can be very important to maintain function and independence.
Management of dementia and neurodegenerative diseases are also most effective when detected early in the disease state. Gait parameters may serve to identify deficits in executive and/or cognitive function which may serve as a surrogate to identify early dementia states and neurodegenerative diseases, and to track disease progression.
Present disclosure is directed to a system intended for fall risk assessment, fall detection, fall prediction, active monitoring of user performance on clinically meaningful assessments such as sit-to-stand test (STS) or timed-up-and-go test (TUG), passive monitoring and recording metrics related to user mobility, physical function, cognitive function, executive function, health, and user functional health status, etc.
This invention overcomes limitations in existing work, intending to provide early detection of mobility and cognitive decline that could lead to life altering traumatic falls, or dementia and neurodegenerative diseases. It provides a comprehensive and unified hardware/software platform for: (1) Fall Detection: Machine Learning based fall detection algorithms that are i.) power efficient to run on mobile devices, and ii.) maintain high accuracy since detection metrics are personalized to the user; (2) Fall prevention: Underlying root cause of a recurring fall could be due to mobility, physical (acute or chronic), cognitive, or deficits in the executive functioning of an individual, and identifying these parameters individually is important towards quantifying metrics for fall risk and fall prevention; (3) Ability to provide Dual-task gait assessments, remotely: Gait data collection with respect to dual-task assessments (such as counting back by 3's while normal walking) is critical in assessing a person's executive function, used in identifying mobility and cognitive health; (4) Data collection from habitual setting: Data collection from a clinical setting relies on sophisticated/expensive clinical resource (staff, equipment, etc.) setup and/or monitoring setup that captures motion signatures using a camera/video system. In the clinical setup case, it requires subjects to perform gait tests at a clinician's office, where subjects are typically in their best form limiting the performance value of such gait assessments whereas the ability to perform and collect these metrics within the users' habitual setting provides a more accurate metric of functional performance, compared to data collection from a clinician office; (5) Context-aware information: A person could show acute mobility decline due to a recent medication, temporary dehydration, or depression, not that they are particularly at risk for a fall; (6) combining metrics in a hardware/software platform to evaluate the concept of functional health, which includes mobility or physical function, cognitive function, and executive function. (7) Fall Risk Assessment: Algorithms that quantify clinically-meaningful gait metrics based on their mobility gait assessment, cognitive gait assessment, and physical function gait assessment, taking into account their current health status, to provide a fall risk score categorizing people into low risk (green band), medium risk (yellow band) and high risk (red band) for a potentially life-altering type traumatic fall, and providing metrics for areas to specifically improve and minimize fall risk. This could also provide an objective measure for the need to be in care; (8) “Quality of Life” Assessment: to provide an objective metric to assess the quality of life of a patient (such as geriatric cancer patients) for physical reserve in the determination of treatment options, and to track physical function during and after treatment.
Further, the invention includes a mobile application using either InThePendant's wearable or a commercially available wearable, such as an Apple Watch, that provides a prescribed, in a very specific manner, set of voice-guided physical movements that capture daily variations in dual task-gait and physical function that can be used for detecting the pre-clinical and prodromal stages of amnestic and non-amnestic Mild Cognitive Impairment (aMCI, naMCI) and Alzheimer's Disease and Related Dementias (ADRD). The assessment provides the ability for high-frequency monitoring of proven laboratory-based assessments, conducted remotely, from the user's habitual setting. The Application enables completion of evidenced-based tests and monitoring of clinically-meaningful outcomes on a regular basis. Such high-frequency monitoring enables tracking of daily variation in performance, which is more sensitive to initial pathology than a ‘snapshot’ of performance in time (e.g., annual clinical visit), or even one's mean performance over time. When used over extended periods, high-frequency monitoring further affords estimation of when an individual's performance deviates from ‘normal’ in a meaningful way. This approach is critical to the development of ‘digital biomarkers’ of functional health for people living with ADRD.
Specifically, this system for fall detection, fall prediction, mobility, physical and cognitive function analysis includes one or more wearable devices that may have one or more motion sensors such as but not limited to accelerometers and gyroscopes for detecting motion of the device and providing data of the detected motions. In some embodiments, wearable device may also include one or more feedback devices such as but not limited to speakers, used to provide notice or feedback to the user. Wearable device also may include one or more input devices such as but not limited to buttons or microphones. Wearable device also may include one or more processors for monitoring motion, for processing one or more metrics of the user, for determining when a user falls. In some implementations, wearable device may further include a memory to store sensor data. Processors may be configured to store and retrieve motion data from memory. In some implementations, wearable device may be worn as a freely moving pendant or can be attached to a user's body, such as, trunk, extremities or head. In some implementations wearable device may include other sensors such as but not limited to barometer, temperature/humidity sensor. Wearable device may also include a network interface. Wearable device may also have wake word detection.
In this patent disclosure, some implementations of the system for fall detection, fall prediction, mobility, physical and cognitive function analysis may include: one or more wearable devices with previously disclosed sensors and features, a server (which may be a local server, implemented in a cloud, or in any other method) and associated mobile app all of which are connected through a network. A mobile application or “mobile app” is a computer program or software application designed to run on a mobile device such as a phone, tablet, or watch (hereinafter referred to as mobile app). Some implementations may also include other wearable or non-wearable devices such as heart-rate sensors, blood pressure sensors, and other monitors etc. Wearable device network interface may be configured to transfer data between wearable device, server and associated mobile app.
In some implementations, the server in the disclosed system may be configured to collect output data from one or more previously disclosed wearable devices, process the wearable device outputs that may include scores on user performance related to user mobility, cognitive function, fall risk, fall detection algorithms that may include but are not limited to machine learning based fall detections, fall prediction algorithms that may include but are not limited to machine learning based fall predictions, generate feedback on user performance and transmit them to associated mobile app.
In some implementations of the disclosed system based on gait and movement analysis for clinical or in-home remote fall risk assessment, mobility assessment, physical and cognitive function assessment, fall detection and fall prediction are disclosed. The disclosed system may include various software modules for classifying movements and processing raw data from gait or other body movements such as sitting down, standing up and turns to generate metrics for fall risk assessment, mobility assessment, physical function assessment, cognitive function assessment, for machine learning based fall detection or fall prediction such as but not limited to gait analysis module, sit-to-stand analysis module, fall prediction module, fall detection module, etc.
In some implementations, disclosed system can be used for both active monitoring, where the user performs a prescribed clinically relevant test routines which may include tests like normal walk, dual-task walk, fast walk, 30 second sit-to-stand-test, timed-up and go, balance test or for passive monitoring, where the system collects metrics as the subject performs normal activities of daily living in order to collect user data for fall risk, mobility, physical or cognitive function assessment.
In some implementations, some of the user feedback generated by the disclosed system may be generated by wearable device or the server can be specifically designed to be actionable so the user can use the feedback to reduce fall risk and prevent future falls and can be transmitted to associated mobile app through the network.
In some implementations, disclosed system for fall detection, fall prediction, mobility, physical and cognitive function analysis can be used for early detection of mobility decline, early detection of physical or cognitive function decline in the user's habitual setting, using data collected from passive monitoring of daily activities or active monitoring of prescribed test routines. User feedback may also include actionable feedback on which aspect of the function is on decline and recommend an action to prevent or slow down the decline.
In some implementations, associated mobile app may have accounts for users or caregivers that can be used to view relevant user metrics or feedback. Associated mobile app can be used to enter relevant health information such as medications or health diagnosis or may be configured to get environmental conditions such as temperature to get context-aware information from the system. Associated mobile app can also be used to initiate prescribed tests that include clinically meaningful tests such as but not limited to 30 seconds sit-to-stand test, dual task walking and timed-up-and-go. The visual or audible feedback that the user can view using the mobile app may include historical trends for various performance scores such as strength score, mobility score and cognitive score derived from active monitoring of user performance on clinically meaningful tests or passive monitoring of daily activities. Feedback may include context aware details such as increased fall risk that may be caused by recently prescribed medication. Also, mobile app may be configured to call a selected caregiver when an impactful fall is detected.
In some implementations, Bluetooth beacons can be added to the system for fall detection, fall prediction, mobility, physical and cognitive function analysis in order to detect fall location or to quantify interaction time (i.e., social isolation). In some implementations, various components of the system for fall detection, fall prediction, mobility, physical and cognitive function analysis can communicate with each other directly using means such as but not limited to Bluetooth, Wi-Fi, LoRaWan, Zigbee, Cellular, etc.
The present solution combines a number of separate aspects that can be used together or separately to enable fall detection, fall prediction, mobility, physical and cognitive function analysis.
Disclosed features of the invention such as details of modules, combinations of components and other advantages will now be more specifically described with reference to the drawings. It will be understood that the specific method and system embodying the invention are shown by way of illustration and not a limitation of the invention. The principles and features of this invention may be employed in various and numerous embodiments without departing from the scope of the invention by the appended claims.
Detailed description is aimed to provide the means to any person skilled in the art to make and use the embodiments. In addition, the description is given in the context of a specific application and its necessities and changes made to the disclosed embodiments will be obvious to those skilled in the art. The general principles disclosed herein can be implemented to various other embodiments and operations while remaining in the essence and extent of the given disclosure which means the present disclosure is not limited to the shown embodiments and to be accorded the widest extent consistent with the principles and attributes disclosed herein.
As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. Further, the singular forms and the articles “a”, “an”, and “the” are intended to include the plural forms as well, unless expressly stated otherwise. It will be further understood that the terms: includes, comprises, including 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. Further, it will be understood that when an element, including component or subsystem, is referred to and/or shown as being connected or coupled to another element, it can be directly connected or coupled to the other element or intervening elements may be present.
Persons with mobility, cognitive deficits and/or other health conditions present a higher risk of falling and falls can lead to serious injuries. Hence it is important to both detect the falls and report to caregivers for immediate medical attention. It is also important to predict falls before they happen and detect contributing factors and intervene to reduce the likelihood of falls. Present disclosure is directed to a system intended for fall risk assessment, fall detection, fall prediction, active monitoring of user performance on clinically meaningful assessments such as but not limited to: sit-to-stand test or timed-up-and-go test, dual task assessment, passive monitoring and recording metrics related to user mobility, cognitive function, health, user functional health status, etc., that can be used by such persons in unsupervised settings or by clinicians in supervised settings, or for remote supervision. Passive monitoring refers to collecting user data without any additional user action such as monitoring of daily actions of the user whereas active monitoring refers to collecting data from a prescribed clinically relevant test routine which is activated by the user. Disclosed system for fall detection, fall prediction, mobility, physical and cognitive function analysis can include various software modules such gait analysis module, sit-to-stand analysis module, turn analysis module, balance test assessment module, fall prediction module, fall detection module and lastly; fall risk, mobility, physical function and cognitive function assessment module. Software modules can be used to process data or generate feedback.
Specifically, this system for fall detection, fall prediction, mobility, physical and cognitive function analysis includes one or more wearable devices. As shown in
As can be seen in
In some implementations, the server 204 in the disclosed system for fall detection, fall prediction, mobility, physical and cognitive function analysis 200 may be configured to collect outputs of the one or more previously disclosed wearable devices, process the wearable device outputs that may include scores on user performance related to user mobility, cognitive function, base fall risk, machine learning based fall predictions, generate feedback on user performance and transmit them to associated mobile app.
Bluetooth beacons 205-1, 205-2, . . . , 205-N can be used for fall location identification and then can be used to provide crucial information to the first responders about the location of the resident within a site. The wearable devices detect beacons by their Generic Attribute Profile (GATT) service and therefore do not require any prior configuration to be “paired” with the beacons. The communication between the beacon and the wearable is intended to determine the closest beacon or triangulate an approximate location by using the “Received Signal Strength Indicator” (RSSI) information.
In some implementations the wearables can be designed to communicate with each other in a peer-to-peer type topology over Bluetooth Low Energy (BLE) in order to quantify time spent between each wearable. Each wearable device is both able to provide unique information about the wearer and obtain information from another wearable which is providing the same information for its wearer. This information is later processed to determine who is present for each interaction and the length of each interaction.
In some implementations, disclosed system for fall detection, fall prediction, mobility, physical and cognitive function analysis can include multiple other sensors or monitors 203-1, 203-2, . . . , 203-N such as a heart rate monitor to provide additional data on user functional health status. This data can be further analyzed in server 204 using a software module that uses methods such as fractal analysis to get additional information on user function as well as physiological function.
In some implementations, disclosed system for fall detection, fall prediction, mobility, physical and cognitive function analysis can be used for both active monitoring, where the user does a prescribed clinically relevant test routine which may include tests like normal walk, dual-task walk, fast walk, 30 second sit-to-stand-test, timed-up and go, balance test or for passive monitoring, where the disclosed system collects metrics as the subject performs normal activities of daily living in order to collect relevant user data for fall risk, mobility, physical or cognitive function assessment.
In some implementations, some of the user feedback generated by the disclosed system for fall detection, fall prediction, mobility, physical and cognitive function analysis may be generated by wearable devices 202-1, . . . , 202-N or server 204 can be specifically designed to be actionable so the user can use the feedback to reduce fall risk and prevent future falls and can be transmitted to associated mobile app 212 installed in mobile device 208-1, . . . , 208-N through network 220.
In some implementations, associated mobile app 212 may have accounts for users or caregivers that can be used to view relevant user metrics or feedback. Mobile app 212 can be used to enter relevant health information or may be configured to get environmental conditions such as temperature and humidity, to provide context-aware information. Mobile app 212 can also be used to initiate prescribed tests that include clinically meaningful tests such as but not limited to 30 seconds sit-to-stand test, dual task walking and timed-up-and-go. In one embodiment of the system for fall detection, fall prediction, mobility, physical and cognitive function analysis the prescribed test routine is as follows: 2-minute walk test, 1-minute dual task walking test, 30 seconds fast walk, timed-up-and-go, 30 seconds sit-to-stand. Users can be given instructions on how to complete the prescribed test routine using the output device wearable device. The prescribed test routine can be changed to include additional clinically relevant tests or exclude tests in other cases. The feedback that users can view using the mobile app 212 may include historical trends for various performance scores such as strength score, mobility score and cognitive score derived from active monitoring of user performance on clinically meaningful tests or passive monitoring of daily activities. Feedback may include context aware details such as early signs of cognitive decline. Also, mobile app 212 may be configured to call a selected caregiver when an impactful fall is detected. In some implementations, mobile app 212 can be configured to have different authorization levels for users and caregivers such that an authorized caregiver can view user functional health status or feedback of the users that wear wearable devices 202-1, . . . , 202-N.
In some implementations, disclosed system for fall detection, fall prediction, mobility, physical and cognitive function analysis can be used for early detection of mobility decline, early detection of physical or cognitive function decline in the user's habitual setting, using data collected from passive monitoring of daily activities or active monitoring of prescribed test routines. User feedback may also include actionable feedback generated on server 204 on which aspect of the function is on decline and recommend an action to prevent or slow down the decline and can be viewed by the users using mobile app 212 installed in mobile devices 208-1, . . . , 208-N.
This invention was made with Government support under Grant Number 2013985 awarded by the National Science Foundation. The Government has certain rights in this invention.
| Number | Date | Country | |
|---|---|---|---|
| 63383515 | Nov 2022 | US |
