Real-time gait analysis is essential for the development of responsive and reliable motorized prosthetics. Deploying advanced deep learning models on resource-constrained embedded systems, however, remains a major challenge. This proof-of-concept study presents a TinyML-based approach for knee joint angle prediction using convolutional neural networks (CNNs) trained on inertial measurement unit (IMU) signals. Gait data were acquired from four healthy participants performing multiple stride types, and data augmentation strategies were applied to enhance model robustness. Multi-objective optimization was employed to balance accuracy and computational efficiency, yielding specialized CNN architectures tailored for short, natural, and long strides. A lightweight classifier enabled real-time selection of the appropriate specialized model. The proposed framework achieved an average RMSE of 2. 05°, representing a performance gain of more than 35% compared to a generalist baseline, while maintaining low inference latency (16. 8 ms) on a 40 embedded platform (Sipeed MaixBit with Kendryte K210). These findings demonstrate the feasibility of deploying compact and specialized deep learning models on low-cost hardware, enabling affordable prosthetic solutions with real-time responsiveness. This work contributes to advancing intelligent assistive technologies by combining efficient model design, hardware-aware optimization, and clinically relevant gait prediction performance.
Yamashita et al. (Tue,) studied this question.