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Activity classification in users of ankle foot orthoses
Cameron M Archer1, John Lach, Shanshan Chen
1Department of Mechanical & Aerospace Engineering, University of Virginia, Charlottesville, VA, United States.
Gait & Posture
|July 30, 2013
Summary
Ankle foot orthoses (AFOs) with inertial sensors can accurately classify activities in users with perturbed gait. This system achieves high sensitivity with limited training data, enabling future gait monitoring research.
Area of Science:
- Biomedical Engineering
- Rehabilitation Technology
- Wearable Sensors
Background:
- Ankle foot orthoses (AFOs) are crucial for mobility assistance.
- Activity classification in AFO users with perturbed gait is challenging.
- Inertial sensors offer a potential solution for gait monitoring.
Purpose of the Study:
- To develop and validate a framework for activity classification using inertial sensors integrated into AFOs.
- To assess the efficacy of the classification system with various gait perturbations and postures.
- To determine the minimum training data required for accurate activity recognition.
Main Methods:
- A decision tree-nearest neighbor algorithm was employed for activity classification.
- Eight subjects wore AFOs equipped with triaxial accelerometers and gyroscopes.
- Gait perturbations (free rotation, equinus, locked ankle) and postures (standing, sitting, lying) were analyzed.
- Video data served as the ground truth for algorithm validation.
Main Results:
- The algorithm achieved an overall mean sensitivity of 95% with 50% training data (≈140s).
- High sensitivities (≥87%) and PPV (≥90%) were observed for most gait patterns, excluding stair negotiation.
- Posture classification sensitivity varied, with lying (98%) and standing (80%) performing better than sitting (64%).
Conclusions:
- AFOs equipped with inertial sensors provide a viable platform for activity classification in AFO users.
- The developed framework demonstrates effectiveness even with limited subject-specific training data.
- This approach holds promise for future research in gait monitoring and rehabilitation for individuals using AFOs.
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