Abstract
In this paper, an adaptive human-machine interaction (HMI) method that is based on surface electromyography (sEMG) signals is proposed for the hands-free control of an intelligent wheelchair. sEMG signals generated by the facial movements are obtained by a convenient dry electrodes sensing device. After the signals features are extracted from the autoregressive model, control data samples are updated and trained by an incremental online learning algorithm in real-time. Experimental results show that the proposed method can significantly improve the classification accuracy and training speed. Moreover, this method can effectively reduce the influence of muscle fatigue during a long time operation of sEMG-based HMI.
Highlights
The deployment of electric powered wheelchairs (EPWs) has been increased rapidly for better quality of life for handicapped and elderly people over the last 20 years [1]
40–60 min Experimental results indicate that the performance of the surface electromyography (sEMG) based human-machine interaction (HMI) system with LIBSVM
Muscle fatigue can negatively influence the performance of human-machine interaction using sEMG signals
Summary
The deployment of electric powered wheelchairs (EPWs) has been increased rapidly for better quality of life for handicapped and elderly people over the last 20 years [1]. Most of these EPWs are traditionally controlled by joysticks, which are not suitable for people with severe physical disabilities like spinal cord injury or hemiplegia. Lai Wei combined forehead sEMG signals and color face image information to control the smart robot [5]. It suffers from poor reliability and robustness in long-term operations, which were mainly caused by muscle fatigue. The improved incremental training algorithm is presented and applied on the HMI system
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