Wearable sensor-based intent recognition for adaptive control of intelligent ankle-foot prosthetics

被引：0

作者：

Kumar, Vidyapati ^{[1
]}

Pratihar, Dilip Kumar ^{[1
]}

机构：

[1] Department of Mechanical Engineering, Indian Institute of Technology Kharagpur

来源：

Measurement: Sensors | 2025年 / 39卷

关键词：

Adaptive control; Gait analysis; Lower limb prosthetics; Machine learning; Wearable sensors;

D O I：

10.1016/j.measen.2025.101865

中图分类号：

学科分类号：

摘要：

Prosthetic motor control requires improvement to better adapt to varying gait speeds and terrain inclinations in real time. Traditional methods often fail to meet these demands, prompting research into advanced sensor data and machine learning algorithms. This research study tackles the challenge by using wearable sensors and comparing various machine learning approaches, namely Sparse Bidirectional Long Short-Term Memory (SBLSTM), Adaptive Neuro-Fuzzy Inference System (ANFIS), convolutional neural network (CNN), logistic regression, and K-nearest neighbors (KNN) for effective classification of gait speed and terrain inclination. Various wearable sensor data, such as FSR and accelerometers, were employed to develop robust models for prosthetic control. The SBLSTM model, which utilizes time-series data through Bi-Directional LSTM layers, demonstrated impressive performance with an accuracy of 96.3 %, precision of 96.4 %, recall of 96.5 %, and an F1-score of 96.4 %. In contrast, the ANFIS model, combining gradient-based learning and least squares estimation, showed reasonable predictive capabilities with root mean square error (RMSE) values of 0.12 for speed and 0.14 for inclination. The accuracy of CNN, logistic regression, and KNN was reported to be 60 %, 31 %, and 93 %, respectively. Comparing the other models in terms of computation, the mean inference time for SBLSTM was found to be 25 ms, which proved to balance speed and accuracy better than other models. Furthermore, the SBLSTM model is particularly suited for time-dependent data, making it more appropriate for real-time prosthetic control. The results highlight that using advanced machine learning algorithms and wearable sensor data has great potential to increase the responsiveness and adaptability of lower-limb prosthetic systems. Ultimately, the goal of this work is for prosthetic users to benefit in terms of quality of life-related to improved mobility and adaptability across a range of environmental conditions. © 2025 The Authors

引用

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