Fall Detection System Using Motion and Biomedical Sensors
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Solution Overview
Problem
Existing fall detection applications experience a high rate of false alerts due to their inability to distinguish between normal living activities and true fall events, primarily because they rely solely on mechanical signals without considering accompanying biological and physiological changes.
Innovation Solution
A system and method that utilize a combination of motion sensors and biomedical sensors to collect and analyze both mechanical and physiological information, applying multiple qualifying conditions to determine the occurrence of a fall incident, thereby reducing false alerts by incorporating acceleration amplitude, reverse impact, and physiological data such as heart rate and blood pressure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion sensors alone are used for fall detection, then the device complexity is reduced, but the reliability of fall detection deteriorates due to high false alert rates
Solution Approach 1:
The patent combines motion sensors (accelerometers, gyroscopes) with biomedical sensors (heart rate monitors, blood pressure sensors, temperature sensors) into an integrated monitoring system. This merging of different sensor types enables cross-validation of fall detection signals, where physiological changes during actual falls differ from those during normal activities, thereby reducing false alerts while maintaining manageable device complexity through unified processing architecture.
2Reliability
If multiple sensors and qualifying conditions are implemented, then the reliability of fall detection improves, but the device complexity increases
Solution Approach 1:
The patent segments the fall detection process into distinct evaluation stages with multiple qualifying conditions. The system divides monitoring into: (1) motion pattern analysis using motion sensors, (2) physiological change detection using biomedical sensors, (3) contextual behavior assessment, and (4) temporal pattern recognition. This segmentation allows complex multi-sensor data to be processed in manageable stages, improving reliability through comprehensive analysis while controlling processing complexity through structured decision pathways.
Solution Approach 2:
The patent employs parameter changes by establishing multiple qualifying conditions with different thresholds and weightings for various sensor inputs. The system dynamically adjusts detection parameters based on individual user baselines, environmental context, and temporal patterns. This approach enables reliable fall detection by comparing actual sensor readings against multiple calibrated parameters, reducing false alerts while maintaining systematic processing through defined parameter sets.
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
Significantly improves the reliability of fall detection by accurately differentiating between actual falls and false alarms, reducing unnecessary alerts and enhancing user safety through comprehensive signal processing and analysis.
Implementation Method 1
a motion sensor for collecting motion information
Implementation Method 2
a biomedical sensor for collecting physiological information
Data Source
AI summary
The present disclosure provides a method and a system for fall detection. Measurement signals are received from a plurality of sensors to monitor user activities, the plurality of sensors including a motion sensor for collecting motion information and a biomedical sensor for collecting physiological information. According to a signal processing sequence, whether the measurement signals meet multiple qualifying conditions for a fall incident is determined. The multiple qualifying conditions include: a condition evaluating at least the motion information, and a condition evaluating at least the physiological information. The method further includes: when the measurement signals do not meet the multiple qualifying conditions, continuing to monitor the user activities; and when the measurement signals meet the multiple conditions, determining that a fall incident has occurred, and sending an alert message to a designated contact.