  Many researchers have used machine learning models to control artificial
hands, walking aids, assistance suits, etc., using the biological signal of
electromyography (EMG). The use of such devices requires high classification
accuracy of machine learning models. One method for improving the
classification performance of machine learning models is normalization, such as
z-score. However, normalization is not used in most EMG-based motion prediction
studies, because of the need for calibration and fluctuation of reference value
for calibration (cannot re-use). Therefore, in this study, we proposed a
normalization method that combines sliding-window analysis and z-score
normalization, that can be implemented in real-time processing without need for
calibration. The effectiveness of this normalization method was confirmed by
conducting a single-joint movement experiment of the elbow and predicting its
rest, flexion, and extension movements from the EMG signal. The proposed
normalization method achieved a mean accuracy of 64.6%, an improvement of 15.0%
compared to the non-normalization case (mean of 49.8%). Furthermore, to improve
practical applications, recent research has focused on reducing the user data
required for model learning and improving classification performance in models
learned from other people's data. Therefore, we investigated the classification
performance of the model learned from other's data. Results showed a mean
accuracy of 56.5% when the proposed method was applied, an improvement of 11.1%
compared to the non-normalization case (mean of 44.1%). These two results
showed the effectiveness of the simple and easy-to-implement method, and that
the classification performance of the machine learning model could be improved.
