Mitigate the Effect of Arm Posture on Electromyography Pattern Recognition.

The real-time use of electromyography (EMG)-based mechatronic rehabilitation devices aiming to detect stroke patients' hand grasping intention is hindered by a significant concern: the lack of robustness against variations in EMG signal patterns due to arm posture changes. This problem results in degraded EMG signal measurements and inaccurate recognition of muscle patterns. Several studies have aimed at tracking changes in EMG patterns by placing multiple EMG sensors around the forearm and developing a classifier using data collected from various arm postures recorded by all sensors. Although these methods show promise, the significant computational resources required for real-time data processing become notable concerns when using multiple EMG sensors. To address these challenges, this study introduces a novel approach that aims to reduce the number of EMG channels that need to be processed. The study proposes a new optimal-channel-selection technique, coupled with a convolutional neural network (CNN), which selects two out of eight EMG channels within an armband based on the arm posture and individual demographics. As a result of using only two channels rather than the entire array (eight channels), the user's grasping intention prediction time took only 2.3 seconds with a classification accuracy of around 81%. In comparison, the commonly used eight-channel method took 8.6 seconds for grasping intention detection with an accuracy level of 79%. These findings show potential in tackling the challenge of EMG measurement degradation caused by arm motion, offering a path towards enhanced accuracy and quicker responsiveness in EMG-based mechatronic rehabilitation devices.