Fall Detection System Using Tri-Axial Accelerometer and EMG Sensors
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Solution Overview
Problem
Current fall detection systems lack mechanisms for anticipating and detecting falls in real-time, particularly for individuals who are unconscious or unable to seek help, and fail to correlate causal factors with detection techniques to improve efficacy.
Innovation Solution
A method and system that utilize sensors such as tri-axial accelerometers, force transducers, and EMG data to detect falls by analyzing changes in acceleration, muscle tone, and body position, issuing warnings, and administering neurological tests to assess responsiveness and awareness, while logging fall data for history and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual button pressing is used for fall detection, then the system is simple to implement, but it fails when the person is unconscious or unable to seek help
Solution Approach 1:
The system automatically detects falls through sensors and algorithms without requiring the person to manually press a button or actively participate in the detection process, enabling unconscious or incapacitated individuals to receive assistance
Solution Approach 2:
The patent replaces manual mechanical button pressing with automated sensor-based detection systems including accelerometers, gyroscopes, and EMG sensors that continuously monitor physiological and motion parameters
2Measurement precision
If multiple sensors and neurological tests are used to improve fall detection accuracy, then detection precision improves, but device complexity increases
Solution Approach 1:
The system divides fall detection into multiple independent measurement dimensions (motion parameters, muscle tone, neurological responsiveness) that can be processed separately and combined, allowing high precision through comprehensive monitoring while maintaining manageable system architecture
Solution Approach 2:
The wearable device integrates multiple sensor types (accelerometers, gyroscopes, EMG sensors) and testing capabilities into a single multi-functional platform that performs motion detection, muscle tone monitoring, and neurological assessment
3Reliability
If fall detection systems correlate causal factors with detection techniques, then detection efficacy improves, but data processing requirements increase
Solution Approach 1:
The system pre-establishes correlation models between causal factors (muscle tone changes, motion patterns) and fall events, allowing real-time detection to rely on pattern matching against pre-analyzed data rather than processing all raw sensor data from scratch
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
Enables early detection and warning of falls, reduces injuries by deploying safety measures, and improves fall detection accuracy by correlating causal factors with detection techniques, facilitating timely assistance.
Implementation Method 1
The received data is data detected by at least one of a tri-axial accelerometer
Implementation Method 2
the method for detecting a fall may further include receiving and analyzing electromyogram (EMG) data
Data Source
AI summary
Methods, systems, and apparatuses are provided for detecting fall events of a person. Fall events are falls that are likely to occur, are occurring, or have occurred. Fall detectors and fall detector systems detect fall events of the person. Data relating to the person are received from sensors and analyzed to perform fall detection. Data relating to the person includes accelerations and forces experience by the person, changes in body position of the person, movements of the person, and body signals and sounds of the person. Neurological tests are administered to determine levels of responsiveness and awareness of the person in response to detections. Warnings are issued, and safety measures are deployed, in response to detections. Data relating to fall events are recorded and logged. Fall event histories based upon the logged data and fall detection algorithm performance are used to improve future fall detection and prediction.