Zhang Peng, Wu Pengcheng, Wang Wendong
Engineering Training Centre, Northwestern Polytechnical University, Xi'an 710000, China.
College of Automation, Northwestern Polytechnical University, Xi'an 710000, China.
Micromachines (Basel). 2023 Feb 26;14(3):546. doi: 10.3390/mi14030546.
Wearable exoskeletons play an important role in people's lives, such as helping stroke and amputation patients to carry out rehabilitation training and so on. How to make the exoskeleton accurately judge the human action intention is the basic requirement to ensure that it can complete the corresponding task. Traditional exoskeleton control signals include pressure values, joint angles and acceleration values, which can only reflect the current motion information of the human lower limbs and cannot be used to predict motion. The electromyography (EMG) signal always occurs before a certain movement; it can be used to predict the target's gait speed and movement as the input signal. In this study, the generalization ability of a BP neural network and the timing property of a hidden Markov chain are used to properly fuse the two, and are finally used in the research of this paper. Experiments show that, using the same training samples, the recognition accuracy of the three-layer BP neural network is only 91%, while the recognition accuracy of the fusion discriminant model proposed in this paper can reach 95.1%. The results show that the fusion of BP neural network and hidden Markov chain has a strong solving ability for the task of wearable exoskeleton recognition of target step speed.
可穿戴外骨骼在人们的生活中发挥着重要作用，比如帮助中风和截肢患者进行康复训练等。如何使外骨骼准确判断人体动作意图是确保其能够完成相应任务的基本要求。传统的外骨骼控制信号包括压力值、关节角度和加速度值，这些信号只能反映人体下肢当前的运动信息，无法用于预测运动。肌电（EMG）信号总是在特定运动之前出现；它可以作为输入信号用于预测目标的步态速度和运动。在本研究中，利用BP神经网络的泛化能力和隐马尔可夫链的时序特性对二者进行合理融合，并最终应用于本文的研究。实验表明，使用相同的训练样本，三层BP神经网络的识别准确率仅为91%，而本文提出的融合判别模型的识别准确率可达95.1%。结果表明，BP神经网络与隐马尔可夫链的融合对于可穿戴外骨骼识别目标步速的任务具有很强的解决能力。
