CALIBRATION OF PREDICTION RESULTS OF TRUNK EMG BASED ON MUSCULOSKELETAL MANNEQUIN AND INTENTION LABELING
Prediction of trunk muscle electromyography(EMG)has great application potential in the field of man-machine interaction.However,the control modes of the trunk muscles alternate with human intentions,hand operations,balance conditions and other factors,which undermines the mapping relationship between motion signals and EMG signals.Therefore,it is difficult to realize the high-precision prediction of the trunk EMG.In order to achieve EMG prediction corresponding to the intentions,the EMG signals of a group of paravertebral muscles and motion signals were measured during preset flexion-extension tasks.The multi-dimensional EMG signals of paravertebral muscles were transformed into action vectors composed of Two-step Clustering numbers.The action vectors were used as the input of BiLSTM-CRF algorithm to realize the tagging of trunk muscle actions during different periods,and the musculoskeletal mannequin was used to calibrate the trunk EMG prediction results.The calibration results can reflect the intention of the trunk,the hands and the individual characteristics.
Paravertebral musclesAction intentionTwo-step clusteringBidirectional long and short-term neural networkElectromygraphy
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