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Experimental Methods to Study Human Postural Control
Published on: September 11, 2019
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An algorithm for real time minimum toe clearance estimation from signal of in-shoe motion sensor
Summary
A new algorithm estimates minimum toe clearance (MTC), a key indicator of tripping risk, using in-shoe motion sensors. This real-time MTC estimation method achieves high accuracy, enabling daily monitoring and fall prevention.
Area of Science:
- Biomechanics
- Gait Analysis
- Wearable Technology
Background:
- Minimum toe clearance (MTC) is a critical gait parameter for assessing tripping risk.
- Existing methods for MTC measurement can be cumbersome or impractical for daily monitoring.
Purpose of the Study:
- To develop and validate a real-time algorithm for estimating MTC using in-shoe motion sensor (IMS) data.
- To identify optimal feature points within IMS signals for accurate MTC event detection.
Main Methods:
- Candidate feature points in IMS signals were identified for MTC event detection.
- Temporal agreement, accuracy, and precision of MTC estimation were evaluated against a 3-D optical motion-capture system.
- A geometric model and IMS data were used to estimate MTC, with accuracy validated using prior study data.
Main Results:
- MTC events were accurately estimated by detecting the crossing point of anterior-posterior and superior-inferior acceleration waveforms, achieving 2.0% gait cycle accuracy.
- The developed algorithm enables real-time MTC estimation with an accuracy of 8.6 mm.
Conclusions:
- The algorithm provides a feasible method for real-time MTC estimation using IMS.
- This technology has the potential to enable continuous MTC monitoring in daily living for fall risk assessment.
