Fall Detection Algorithm Using Velocity Profile Segmentation
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Solution Overview
Problem
Existing fall detection apparatuses, particularly those worn on the wrist, suffer from high false alarm rates due to the reliance on maximum velocity thresholds, which cannot distinguish between falls and daily activities like handshaking.
Innovation Solution
A method and apparatus that acquire time-varying accelerations to compute velocities and displacements, determining lead and return times, and using these metrics along with smoothness thresholds to differentiate falls from other activities, reducing false alarms by considering peak velocities, lead time, and smoothness profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If maximum velocity threshold is used for fall detection, then detection sensitivity is improved, but false alarm rate increases
Solution Approach 1:
The patent segments the velocity profile into distinct phases: lead time (from original point to extreme point) and return time (from extreme point to final point). This segmentation allows the system to analyze the temporal structure of movement patterns, distinguishing falls from daily activities based on the ratio and characteristics of these time segments rather than relying solely on maximum velocity threshold.
2Device complexity
If simple velocity threshold criteria are used, then device complexity is reduced, but detection precision deteriorates
Solution Approach 1:
The patent introduces a temporal dimension to the velocity analysis by calculating lead time and return time based on the velocity profile's extreme points. This transforms the single-point velocity threshold assessment into a multi-parameter temporal pattern recognition system, improving precision by analyzing the time structure of movements rather than just their magnitude.
3Reliability
If lead time and return time criteria are added, then false alarm rate is reduced, but computational complexity increases
Solution Approach 1:
The system pre-defines the criteria for lead time and return time calculation based on the velocity profile's extreme points. By establishing these temporal parameters in advance and using them as decision rules, the system reduces the need for complex real-time computation while maintaining high reliability in distinguishing falls from daily activities.
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 enhances the reliability of fall detection by accurately distinguishing falls from daily activities, reducing false alarms and improving detection accuracy.
Implementation Method 1
detects falls in accordance with the velocity profile of a user in the vertical direction, since a vertical body movement in the direction of gravity occurs during the fall
Implementation Method 2
acquiring a plurality of time-varying accelerations associated with a movement of the object
Data Source
AI summary
A method for detecting the fall of an object includes: acquiring a plurality of time-varying accelerations associated with a movement of said object; computing a plurality of respective velocities and displacements in the direction of gravity, based on the plurality of time-varying accelerations; determining lead time and return time of the movement, based on the plurality of accelerations, velocities and displacements, wherein the lead time equals a time interval from an original point of the movement to an extreme point of the movement, and the return time equals a time interval from the extreme point of the movement to a final point of the movement; and determining whether the fall of said object occurs, based on whether a peak value of the velocities is larger than a predefined threshold value and whether the lead time is longer than the return time.