StrideSense: Enriching Lower Extremity and Kinetics in ACLR Patients via Sonic Insights

Date

2025-01-01

Author

Ahmed, Awais
Yang, Panlong
Butt, Abdul Haleem
Rizwan, Muhammad
Angın, Pelin
Khan, Taha

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Tearing the anterior cruciate ligament requires repair and rehabilitation to restore lower limb functionality fully. This study outlines a method for monitoring rehabilitation after knee surgery by analyzing footstep sounds and applying deep learning techniques. The process involves examining gait sounds during the initial four weeks of rehabilitation. The suggested system, StrideSense, recognizes walking sounds, enabling seamless and ongoing monitoring of patients' gait rehabilitation. The research proposes a novel method for event detection by analyzing walking patterns using dynamic time-warping and sequential footstep duration. It leverages physiological data like gait sound and energy descriptors to develop a deep-learning model for gait improvement assessment, evaluated by a physiotherapist through lower extremity functional scores. The suggested model, AtdNet, which utilizes Densenet169 and attention mechanisms, achieves 96% accuracy in classifying walking on various post-surgical days. It also predicts lower extremity functional scores with a mean absolute error of 4.63%. A deeper analysis of bone-conducted footstep sounds enriched the acoustic sensing method. We assessed the suggested acoustic model alongside existing methods, showing that rehabilitation monitoring driven by acoustics outperforms traditional clinic-based approach assessments. Future efforts will focus on validating the model with a larger dataset and integrating it into smart homes.

Subject Keywords

Acoustic Sensing, Computer Vision, Emerging Technologies, Smart Homes, Transparent Rehab Monitoring

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85218933252&origin=inward
https://hdl.handle.net/11511/113938

Collections

Department of Computer Engineering, Article