| Summary: | Aiming to provide feasible solutions for the realization of the robust and natural myoelectric control systems, a novel myoelectric control scheme supporting gesture recognition and muscle force estimation is proposed in this study. Eleven grasping gestures abstracted from daily life are selected as the target gesture set. The high-density surface electromyography (HD-sEMG) of the forearm flexor and the grasping force signal are collected simultaneously. The synchronous prediction of gesture category and instantaneous force is realized by the multi-task learning (MTL) technique. Especially, a post-processing algorithm based on threshold method is conducted to overcome the influence of force variation on the accuracy of gesture recognition. The experimental results show that the proposed post-processing method can decrease the classification error significantly. Specifically, the overall gesture classification error is reduced by 27 ~ 30 percent compared with not using the post-processing method; and 16 ~ 24 percent compared with using classical post-processing methods. The whole scheme can realize the synchronous gesture recognition and force estimation with 9.35 ± 11.48% gesture classification error and 0.1479 ± 0.0436 root-mean-square deviation force estimation accuracy. Meanwhile, it is feasible in different number of electrodes and well meets the real-time requirement of the EMG control system in response time delay (about 28.22 ~ 113.16ms on average). The proposed framework provides the possibility for myoelectric control supporting synchronous gesture recognition and force estimation, which can be extended and applied in the fields of myoelectric prosthesis and exoskeleton devices.
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