Gait Analysis for Fall Risk Prevention Through Speed and Balance
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Solution Overview
Problem
Existing wearable devices lack the capability to accurately analyze gait data to estimate fall risks and provide effective fall prevention strategies based on health-related data.
Innovation Solution
A fall prevention apparatus that collects gait data, performs preprocessing, peak detection, and erroneous detection removal to determine gait speed and balance, generating fall prevention data to alert users of potential falls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If gait data is collected and analyzed to estimate fall risk, then fall prevention capability is improved, but device complexity increases
Solution Approach 1:
The gait data analysis process is segmented into distinct stages: data collection, preprocessing, peak detection, erroneous detection removal, and fall risk estimation. Each stage processes specific aspects of the data independently, making the overall complex system more manageable and maintainable while improving accuracy through specialized processing at each stage.
Solution Approach 2:
The system performs preliminary actions by collecting and preprocessing gait data before actual fall risk estimation. Preprocessing steps including normalization, filtering, and peak detection are performed in advance to prepare the data for accurate analysis, ensuring that the estimation process receives cleaned and ready-to-process data.
2Measurement precision
If peak detection and erroneous detection removal operations are performed, then measurement precision is improved, but processing time increases
Solution Approach 1:
The system performs partial peak detection by identifying only the critical peaks (maximum and minimum peaks) rather than all possible peaks in the gait data. The erroneous detection removal operation selectively removes only the fake peaks that violate the critical period constraint, avoiding unnecessary processing of all data points while maintaining high measurement precision.
Solution Approach 2:
The system changes processing parameters dynamically by adjusting the critical period threshold and peak detection sensitivity based on the gait data characteristics. This allows the system to optimize between processing speed and precision by adapting parameters to the specific patterns detected in the data.
3Reliability
If gait data is preprocessed to remove fake peaks, then fall risk estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The erroneous detection removal operation uses feedback from the peak detection process to identify and remove fake peaks. By comparing detected peaks against the critical period constraint and other gait pattern characteristics, the system feedbacks to correct errors in peak identification, improving the overall accuracy of fall risk estimation.
Data Source
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AI summary
The present invention relates to a method of preventing a fall risk and an apparatus for performing the method. The method includes receiving, by a fall prevention apparatus, gait data, generating, by the fall prevention apparatus, gait analysis data on the basis of analysis of the gait data, and generating, by the fall prevention apparatus, fall prevention data on the basis of the gait analysis data.