Wearable Fall Risk Prediction With Personalized Motion Scoring
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Solution Overview
Problem
Existing fall detection systems only react after a fall has occurred, failing to predict and prevent falls by alerting individuals or caregivers of an imminent risk.
Innovation Solution
A wearable system with sensors (accelerometer, magnetometer, gyroscope) processes data to calculate a profile score and motive indices, using machine learning models to predict a fall risk score, adjusting detection thresholds based on individual characteristics and activity patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fall detection systems use simple movement sensors and threshold-based detection, then the device complexity is low and ease of manufacture is high, but the system cannot predict falls before they occur and can only detect falls after they have happened
Solution Approach 1:
The system performs preliminary analysis of movement patterns, activity levels, and physiological data to establish baseline profiles and detect early warning signs of fall risk before actual falls occur. The processing unit continuously monitors motive indices and fall risk scores in advance, enabling preventive alerts rather than reactive detection.
Solution Approach 2:
The system dynamically adapts detection thresholds and monitoring parameters based on individual user profiles, activity contexts, and changing risk factors. The fall risk assessment is not static but continuously updated based on real-time sensor data and historical patterns, allowing the system to adjust sensitivity and prediction models according to current conditions.
2Measurement precision
If the system implements individualized profile scores and motive indices based on multiple sensors, then the measurement precision and adaptability improve, but the device complexity and processing requirements increase
Solution Approach 1:
The complex risk assessment is divided into separate modular components: accelerometer data processing, gyroscope analysis, magnetometer calibration, motive index calculation, profile score generation, and fall risk scoring. Each sensor and processing function operates as an independent module that can be developed, tested, and optimized separately, reducing overall system complexity while maintaining high measurement precision.
Solution Approach 2:
The sensor system is designed to serve multiple functions: fall detection, activity monitoring, posture analysis, and risk prediction. The same accelerometer, gyroscope, and magnetometer units used for basic motion tracking are also utilized for sophisticated fall risk assessment, eliminating the need for separate specialized sensors and reducing device complexity while maintaining high measurement precision.
3Reliability
If the system continuously monitors and processes sensor data to calculate fall risk scores, then the fall prediction capability improves, but the energy consumption increases
Solution Approach 1:
Instead of continuous full-power processing, the system uses periodic sampling of sensor data at optimized intervals. The processing unit calculates motive indices and fall risk scores at specific time points rather than continuously, reducing computational load and energy consumption while maintaining reliable fall prediction capability through strategic monitoring of critical parameters.
Solution Approach 2:
The system intelligently manages its own energy consumption by monitoring battery levels and activity patterns, dynamically adjusting the frequency and intensity of sensor processing. During low-risk periods, processing is reduced to maintain accuracy while conserving energy; during high-risk situations or when anomalies are detected, the system automatically increases monitoring intensity without requiring external power management intervention.
Data Source
AI summary
The invention relates to a system and a method for predicting that an individual will fall, comprising: a casing (10) worn by an individual (8), in which casing there is housed a plurality of sensors for acquiring measurements representative of the posture and/or movements of the individual; a unit (100) for processing the measurements provided by said plurality of sensors and comprising: a module (110) for determining a profile score, based on data representative of characteristics specific to the individual; a module (120) for computing motive indices, based on the measurements provided by said plurality of sensors and representative of the activity of the individual over a predetermined time interval; a module (130) for computing a fall risk score, based on said motive indices and on said profile score; a module (140) for determining a risk of falling, based on a variation in said fall risk score beyond a predetermined threshold over a predetermined time interval.