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سی و سومین کنفرانس بین المللی مهندسی برق
Dynamic Gait Analysis Using Hybrid Neural Networks: Online classification and Prediction of Locomotion Modes
نویسندگان :
Samira Dehghanian
1
Hamed Jalaly Bidgoly
2
1- دانشگاه صنعتی ا
2- دانشگاه صنعتی اص
کلمات کلیدی :
CTN،Attention،Gait Classification،Gait Prediction
چکیده :
Dynamic gait analysis is a cornerstone in wearable robotics and rehabilitation, enabling advanced applications such as exoskeleton control and personalized healthcare. This paper proposes a hybrid neural network integrating convolutional layers, bidirectional LSTMs, and an attention mechanism to achieve robust gait classification and prediction across diverse locomotion modes, including level ground, stair ascent/descent, and ramp ascent/descent. The model processes time-series data from inertial measurement units (IMUs), effectively capturing local temporal patterns and long-term dependencies. A two-stage framework is introduced, starting with the prediction of gait parameters such as joint angles, followed by classification of locomotion modes. Experimental results demonstrate the model’s superior performance, achieving 98.92% accuracy in classifying gait conditions and reliably generalizing across subjects. This approach highlights the potential for seamless integration into wearable assistive devices, providing enhanced adaptability and precision in diverse real-world scenarios.
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بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 44.7.2
