Verification of Normalization Method to Improve Usability and Versatility among Users of Applications that Predict Continuous Motion Using Electromyography

In applications using electromyography (EMG), it is important to ensure high performance for all users (versatility among users) and to enable use without prior preparation (usability). Some of the current applications that use EMG normalize the signal through methods based on the measured maximum absolute value of EMG (maEMG), such as dynamic contraction (DC). However, usability is low when using DC because the reference value must be measured first every time the application is used. Further, the versatility among users is low because of the nonlinearity of EMG and the fact that maEMG varies among users. This study aimed to improve usability and versatility among users for continuous classification tasks using EMG. To this end, we developed a normalization method using sliding-window and z-score normalization techniques. The results reveal that the proposed method exhibits higher usability and versatility among users than DC. The proposed method does not require any calibration time, suggesting improved usability, while yielding the same classification accuracy as DC (57% for three target tasks) for a model trained using a subject’s own data. Further, for a model trained with other users’ data, the proposed method yields a classification accuracy of 53%, which is 18% higher than that of DC (35%), suggesting versatility among users. These results demonstrate that the proposed normalization method improves usability and versatility for users of practical applications that use EMG and perform continuous classification, such as prosthetic hands.