Empirical classification of fatigue-induced physiological tremor in robot-assisted manipulation tasks using BiLSTM-GRU network

The stages of progression of tremor are classified into five levels in this study, and classified using BiLSTM GRU with four different input feature sets. The performance evaluation metrics, such as the accuracy, precision, recall and F1 score, have been reported to ascertain the efficiency of the proposed feature group. The proposed feature set and classification strategy are capable of estimating stages of FIPT with 99% classification accuracy. This can be used to design state-of-the-art movement training platforms for both experienced and novice surgeons that allow informed decision making to attend to their tremor condition, either by taking a break or including a limb support to minimize its effects. At the same time, the identification methodology can be extended to pathological tremor rehabilitation and any other movement disorder diagnostics.