# US 12,765,805 B2 · System for fall detection, fall prediction, mobility, physical and cognitive function analysis

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- Number: US 12,765,805 B2
- Document ID: US12765805B2
- Status: Granted
- Kind: B2
- Application number: 18/507,479
- Filed: 2023-11-13
- Published: 2025-05-15
- Granted: 2026-10-20
- Earliest claimed: 2022-11-14
- Assignees: In The Pendant, Inc.
- Applicants: Baturalp Arslan; Nuri Denizcan Vanli; Murat Bayboga; Bulent Yilmaz; Bradley Manor
- Inventors: Baturalp Arslan; Nuri Denizcan Vanli; Murat Bayboga; Bulent Yilmaz; Bradley Manor
- Web page: https://patentgrep.com/patent/US12765805B2

## CPC

- A61B 5/1117: {Fall detection}
- A61B 5/002: {Monitoring the patient using a local or closed circuit, e.g. in a room or building (A61B5/0017 takes precedence)}
- A61B 5/0022: {Monitoring a patient using a global network, e.g. telephone networks, internet}
- A61B 5/112: {Gait analysis}
- A61B 5/4842: {Monitoring progression or stage of a disease}
- A61B 5/486: {Biofeedback (using electroencephalography [EEG] A61B5/375)}
- A61B 5/4884: {inducing physiological or psychological stress, e.g. applications for stress testing}
- A61B 5/681: {Wristwatch-type devices}
- A61B 5/7267: {involving training the classification device}
- A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor}
- A61B 5/7282: {Event detection, e.g. detecting unique waveforms indicative of a medical condition (cough events A61B5/0823; seizures A61B5/4094; sleep apnoea A61B5/4818)}
- A61B 5/747: {in case of emergency, i.e. alerting emergency services}
- A61B 5/749: {Voice-controlled interfaces}
- G16H 40/67: for remote operation
- A61B 2010/0003: {including means for analysis by an unskilled person}
- A61B 2503/08: Elderly
- A61B 2505/07: Home care
- A61B 2560/0209: adapted for power saving
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

Various embodiments of a system for fall detection, fall prediction, mobility, physical and cognitive function analysis are disclosed. System for fall detection, fall prediction, mobility, physical and cognitive function analysis can receive user data from wearable devices or other sensors and monitors by passive monitoring of the user during normal daily activities and active monitoring of the user during clinically relevant tests. The system can detect falls and alert caregivers in the event of a fall, predict falls and alert user in case there is an imminent fall; analyze, monitor and detect decline in user mobility, physical function and cognitive function, provide relevant feedback to the user or care provider that can be used as targeted information on how to assess, improve, recover or reduce the rate of functional decline.

## Claims

### Claim 1

An end-to-end system, comprising of a hardware and software platform, for fall risk analysis, fall detection, fall prediction, and fall prevention, physical function, cognitive function, and executive function, wherein the system has a wearable or non-wearable device, comprising:
one or more sensors for detecting motion from the wearable device or non-wearable device, wherein the wearable device can be fixed or freely moving;
one or more processors monitoring motion and processing metrics of a user;
measuring baseline data of physical function, cognitive function, executive function, gait metrics, audio, and health parameters;
a memory storing one or more sensor data and the baseline data;
one or more input devices for receiving instructions and feedback from the user;
one or more output devices providing instructions and feedback to the user;
an application stored on the wearable device or non-wearable device, or in the back-end, assessing the user's mobility, physical function, cognitive function, and executive function, wherein the application can be performed remotely without clinician oversight;
the application further comprises a user interface that includes information on health conditions, metabolic conditions, comorbidities and medications taken;
monitoring user's performance, and assessments of gait; wherein the gait measures normal walk, fast walk, dual task walk, sit-to-stand test (STS), timed-up-and-go (TUG), and rotation around hip tests, and identify root causes of a fall risk;
algorithms assessing mobility, physical function, cognitive function, and executive function using assessments of the gait; wherein the gait measures normal walk, fast walk, dual task walk, sit-to-stand test (STS), timed-up-and-go test (TUG), and rotation around hip tests, and other gait metrics, provide fall-risk and fall-risk root cause metrics;
a fall detection machine learning algorithm identifying a fall;
a fall prediction machine learning algorithm combining health parameters, cognitive function, executive function, physical function, fall risk metrics and gait metrics, dual task gait, declining gait speed, and other gait metrics over a period of time;
predicting a fall based on the combined sensor metrics, the fall detection algorithm, the fall prediction algorithm, the gait metrics, health parameter metrics, environmental metrics, and audio processing;
accessing cognitive and executive function based on audio recordings of the user's performance of dual task gait and daily variations in dual task gait, in addition to gait metrics and physical function;
a Fractal Scale Index based algorithm to identify gait patterns in ADRD (Alzheimer's Disease and Related Dementias) and cardiovascular health;
transmitting feedback to the user if the gait and balance metrics is higher than the user's baseline metrics; wherein the feedback can be visual or audible;
transmitting feedback to the user if the physical function of the user is lower than the user's baseline metrics; wherein the feedback can be visual or audible;
transmitting feedback to the user if the cognitive function of the user is lower than the user's baseline metrics; wherein the feedback can be visual or audible; and
transmitting feedback to the user if the executive function of the user is lower than the user's baseline metrics; wherein the feedback can be visual or audible.

### Claim 2 (depends on 1)

The system as claimed in claim 1, further comprising an analysis providing indication of high-risk, medium-risk and low-risk fall risk categories.

### Claim 3 (depends on 1)

The system as claimed in claim 1, wherein the wearable or non-wearable device is wirelessly connected to a back-end server and automatically uploads data without user intervention or the need of an intermediary mobile device, wherein the back-end server can be located on premise or on the cloud.

### Claim 4 (depends on 1)

The system as claimed in claim 1, wherein the wearable or non-wearable device combines gait assessment, fall prediction, and fall detection, in a single device, wherein the single device operates independently of an intermediary mobile device and includes the ability to record voice and sound, provide voice guidance and respond to wake words.

### Claim 5 (depends on 1)

The system as claimed in claim 1, further comprising a custom algorithm residing on the wearable or non-wearable device manipulating user voice recorded from the microphone so that the voice is not recognizable or traceable to a person when automatically uploaded to the back-end server.

### Claim 6 (depends on 1)

The system as claimed in claim 1, further comprises the ability to automatically call for emergency help, either through an automatic fall detection algorithm or through a wake word via the wearable or non-wearable device.

### Claim 7 (depends on 1)

The system as claimed in claim 1, further comprising location information, through additional sensors and custom algorithms, wherein the location information includes both geographic location and specific room location within a dwelling.

### Claim 8 (depends on 1)

The system as claimed in claim 1, further comprising a context-aware software platform that includes health information and environmental conditions along with gait providing context to falls and reasons for falls, or physical function, or cognitive function, or executive function.

### Claim 9 (depends on 1)

The system as claimed in claim 1, wherein the Application further comprises historical data of mobility, physical function, cognitive function, and executive function.

### Claim 10 (depends on 1)

The system as claimed claim 1, wherein the Application further compares individual performance to a population-based dataset to indicate how their individual performance compares.

### Claim 11 (depends on 1)

The system as claimed in claim 1, the system further comprising of user data output that can be shared with a caregiver or healthcare provider, wherein the data is user selectable.

### Claim 12 (depends on 1)

The system as claimed in claim 1, wherein the platform provides measures of fall risk and balance improvement assessment, neurodegenerative cognitive status assessment, and physical reserve assessment, to provide measures of a Quality-of-Life assessment.