Frailty Detection Using PIR, Door, and Pedometer Sensor Data
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Solution Overview
Problem
Conventional systems for frailty detection in the elderly often rely on clinical data collection, which can be inconvenient and prone to false results due to varying data collection methods and assumptions, such as misinterpreting room stay duration as sleep time.
Innovation Solution
A processor-implemented method using PIR sensor data, door sensor data, and pedometer data to generate room movement, day-time bedroom-stay, and outdoor home movement models, assigning weights to these data points to calculate a cumulative frailty percentage, determining frailty if it exceeds a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If clinical data collection is used for frailty detection, then detection capability is provided, but user convenience deteriorates due to frequent monitoring requirements
Solution Approach 1:
The system enables self-service monitoring by automatically collecting data from sensors in the environment (PIR sensors, door sensors, pedometer) without requiring active user participation. The elderly person simply goes about their daily activities while the system passively monitors and analyzes their movement patterns, room occupancy, and activity levels to detect frailty indicators.
Solution Approach 2:
The patent replaces manual clinical assessment methods with automated sensor-based monitoring. Instead of requiring clinicians to manually collect and analyze data, or requiring patients to actively report their condition, the system uses PIR sensors, door sensors, and pedometers to automatically track movement patterns and generate frailty assessments.
2Device complexity
If assumptions are made during monitoring (e.g., room stay = sleep), then processing simplicity is improved, but measurement precision deteriorates due to false results
Solution Approach 1:
The system merges data from multiple independent sources (PIR sensor data indicating room occupancy, door sensor data indicating entry/exit events, pedometer data indicating movement intensity) to create a comprehensive view of user behavior. By combining these diverse data streams, the system can distinguish between different activities more accurately than any single sensor could alone.
Solution Approach 2:
The system continuously monitors movement patterns and uses this feedback to refine its understanding of user behavior. Instead of making static assumptions, the system analyzes temporal patterns in the combined sensor data to dynamically adjust its interpretation of activities, thereby improving detection accuracy without requiring overly complex processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides a non-invasive, accurate method for frailty detection by analyzing movement patterns and activity levels, reducing the need for frequent clinical data collection and minimizing false results.
Implementation Method 1
a Passive Infrared (PIR) sensor data and door sensor data are obtained, from a plurality of PIR sensors
Data Source
AI summary
Elderly people suffer from health issues, and timely detection can save lives. State of the art techniques either make certain assumptions or require clinical data in order to perform the frailty detection, which affects the quality as well as cause inconvenience to the users. The disclosure herein generally relates to patient monitoring and, more particularly, to frailty detection using pedometer sensor data, PIR sensor data, and door sensor data. The system determines activity levels of the user being monitored, based on data from the pedometer sensors, PIR sensors, and door sensors, and based on the determined activity levels, further determines whether the user has frailty or not.