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Challenges of Stride Segmentation and Their Implementation for Impaired Gait
Summary
This study compared four stride segmentation methods for gait analysis. Force sensing resistors offer higher accuracy than inertial sensors (accelerometers, gyroscopes) for detecting gait events in healthy individuals and Parkinson
Area of Science:
- Biomechanics and Biomedical Engineering
- Gait Analysis and Wearable Sensor Technology
Background:
- Accurate stride segmentation is crucial for gait analysis but remains challenging.
- Wearable sensors, including force sensors and inertial sensors, are commonly used for gait event detection.
Purpose of the Study:
- To compare the performance of four distinct stride segmentation methods.
- To evaluate these methods using data from force sensing resistors, accelerometers, and gyroscopes.
- To assess accuracy in both healthy individuals and patients with Parkinson's disease.
Main Methods:
- Collected gait data using force sensing resistors, accelerometers, and gyroscope sensors.
- Implemented and compared four different algorithms for stride segmentation.
- Evaluated segmentation accuracy based on imprecisely detected, missed, or wrongly detected gait events and temporal absolute error.
Main Results:
- Methods utilizing inertial data (accelerometers, gyroscopes) exhibited up to 12% higher error rates compared to methods using force sensing resistors.
- Force sensing resistors demonstrated superior precision in stride segmentation.
Conclusions:
- Force sensing resistors provide more accurate stride segmentation compared to inertial sensors for gait analysis.
- Inertial sensor-based methods require further refinement to match the accuracy of force-based methods, particularly in clinical populations like Parkinson's disease.
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