Abstract
Surface electromyography (sEMG) plays a crucial role in prediction of elbow torque for human-robot interaction. However, accurately predicting joint torque still experiences a critical challenge, including the complexity of the human neuromuscular system, limitations in sensor technology, and real-time constraint. This study proposes an improved African vulture optimization algorithm(IAVOA) to calibrate the neuromusculoskeletal(NMS) model. To enhance the diversity of the population and prevent the algorithm from converging to local optima, the tent chaotic mapping and cauchy variation are integrated into the algorithm, based on AVOA. The conjugate gradient(CG) algorithm is also integrated into the algorithm to accelerate the convergence rate. The experimental results indicate that IAVOA is highly effective, with the global determination coefficient greater than 0.914 and root mean square error lower than 0.37 N\(\cdot \)m. These results demonstrate the potential of proposed approach as a promising method for improving human-robot interaction in rehabilitation robotics.
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This work is supported by the National Natural Science Foundation of China under Grant 52075398 and 52275029.
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Xia, Y., Liu, H., Zhu, C., Meng, W., Chen, M. (2023). Prediction of Elbow Torque Using Improved African Vultures Optimization Algorithm in Neuromusculoskeletal Model. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14271. Springer, Singapore. https://doi.org/10.1007/978-981-99-6495-6_25
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