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Training-Efficient Personalised Knee Joint Moment Estimation via an Incremental Broad Learning System
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DOI：10.1049/cit2.70165.png)
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Abstract
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Knee joint moment estimation is a critical component in biomechanical analysis, with profound implications for rehabilitation assessment and the development of assistive devices like exoskeletons. However, existing data-driven approaches often rely on highly complex network architectures in pursuit of high accuracy, resulting in inherently inefficient training procedures. Furthermore, incorporating new subject-specific data typically requires retraining the entire model, which substantially increases computational cost. These limitations make it challenging for current data-driven methods to achieve personalised knee joint moment estimation. To address this challenge, this paper proposes a novel framework based on an Incremental Broad Learning System (IBLS) that can achieve efficient model training through a flattened network architecture and an analytical ridge regression solver. More importantly, it integrates an incremental learning algorithm that enables rapid model updates using only newly acquired subject-specific data, eliminating the need for costly full retraining. These capabilities collectively make efficient personalised knee joint moment estimation feasible. Extensive experiments on Dataset A and Dataset B demonstrate that our method achieves higher prediction accuracy compared to standard baselines (ANN and LSTM). Furthermore, our approach exhibits competitive performance when compared with advanced deep learning architectures (Transformer and TCN-LSTM). Notably, the training efficiency is significantly enhanced: the training time required by our method is approximately 30% of that for ANN, 7.5% for LSTM, and only 3.5% for both the Transformer and TCN-LSTM. Further experimental evidence indicates that incorporating incremental learning provides an additional 60% reduction in training time relative to full retraining, without sacrificing prediction accuracy. In addition, we systematically identify optimal Inertial Measurement Unit (IMU) configurations that balance accuracy with practical wearability, providing actionable guidelines for implementation. This work provides an efficient, accurate, and personalised solution for knee joint moment estimation, paving the way for the development of adaptive and efficient wearable robotic systems.
Keywords:
incremental broad learning system
joint torque estimation
knee joint moment
wearable robots
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