Reducing the limb position effect in pattern recognition based myoelectric control using a high density electrode array

Pattern recognition based myoelectric control schemes are an active field of research. However, there are numerous disparities between current research findings and actual clinical results. In literature, electromyographic signals are usually recorded in a fixed position and used for both, training and testing of the classifier. This supports the test subject in performing repeatable contractions throughout the trials of the experiment and generally results in a high classification accuracy. In clinical testing however, subjects have to perform various activities of daily living, causing the limb to move in different positions. Recent studies have shown that these variations in limb positions significantly decrease robustness and usability of myoelectric control systems. This so-called limb position effect has been previously studied but remains an unsolved problem. In this study we investigate if increasing the number of electrode channels and recording locations can improve the degraded classification accuracy caused by the limb position effect. In our experiment we use a 96 channel high density electrode array to distinguish 11 different hand and wrist movements recorded in three different limb positions. Our results show that training in multiple positions in combination with an increasing number of channels helps reducing the limb position effect.