Abstract
The dynamics of human arms has a high impact on the humans’ activities in daily life, especially when a human operates a tool such as interactions with a robot with the need for high dexterity. The dexterity of human arms depends largely on motor functionality of muscle. In this sense, the dynamics of human arms should be well analyzed. In this paper, in order to analyse the characteristic of human arms, a neural-network-based algorithm is proposed for exploring the potential model between electromyography (EMG) signal and human arm’s force. Based on the analysis of force for humans, the mean absolute value of the electromyographic signal is selected as the input for the potential model. In this paper, in order to accurately estimate the potential model, three domains fuzzy wavelet neural network (TDFWNN) algorithm without prior knowledge of the biomechanical model is utilized. The performance of the proposed algorithm has been demonstrated by the experimental results in comparison with the conventional radial basis function neural network (RBFNN) method. By comparison, the proposed TDFWNN algorithm provides an effective solution to evaluate the influence of human factors based on biological signals.
Highlights
Humans are adept at performing the tasks which need high dexterity
The results indicated that long short-term memory (LSTM) and convolutional neural network (CNN)-LSTM could achieve relatively better performance [28]
It has demonstrated that mean absolute value (MAV) was superior to other features such as wave length (WL) and Willison amplitude (WA) for surface EMG (sEMG)-force estimation applications [37], [38]
Summary
For a cooperative task between a human and a robot, the human needs to be more dexterous and skillful to perform the task in order to achieve the security and smooth interaction, especially for the tasks involving interactive force or torque [1], [2]. In such tasks or activities, the robot should be developed to match the skillful and dexterous operation of the humans’ arm. In order to achieve smooth interaction between the human and the robot instead of simple rigid interaction, The associate editor coordinating the review of this manuscript and approving it for publication was Yanzheng Zhu
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