Mitigating the Impact of Electrode Shift on Classification Performance in Electromyography-Based Motion Prediction Using Sliding-Window Normalization

Electromyography (EMG) signals are used in many applications, including prosthetic hands, assistive suits, and rehabilitation. Recent advances in motion estimation have improved performance, yet challenges remain in cross-subject generalization, electrode shift, and daily variations. When electrode shift occurs, both transfer learning and adversarial domain adaptation improve classification performance by reducing the performance gap to -1\% (eight-class scenario). However, additional data are needed for re-training in transfer learning or for training in adversarial domain adaptation. To address this issue, we investigated a sliding-window normalization (SWN) technique in a real-time prediction scenario. This method combines z-score normalization with a sliding-window approach to reduce the decline in classification performance caused by electrode shift. We validated the effectiveness of SWN using experimental data from a target trajectory tracking task involving the right arm. For three motions classification (rest, flexion, and extension of the elbow) obtained from EMG signals, our offline analysis showed that SWN reduced the differential classification accuracy to -1.0\%, representing a 6.6\% improvement compared to the case without normalization (-7.6\%). Furthermore, when SWN was combined with a strategy that uses a mixture of multiple electrode positions, classification accuracy improved by an additional 2.4\% over the baseline. These results suggest that SWN can effectively reduce the performance degradation caused by electrode shift, thereby enhancing the practicality of EMG-based motion estimation systems.