Exploration on the feasibility of building muscle-computer interfaces using neck and shoulder motions

This paper investigates the feasibility of building muscle-computer interfaces starting from surface Electromyography (SEMG) -based neck and shoulder motion recognition. In order to reach the research goal, a real-time SEMG sensing, processing and classification system was developed firstly. Then two types of SEMG recognition experiments, namely user-specific and user-independent classification, were designed and conducted on seven kinds of neck and shoulder motions to explore the feasibility of using these motions as input commands of muscle-computer interfaces. In all 9 subjects took part in these experiments, 97.8% and 84.6% overall average recognition accuracies were obtained in user-specific and user-independent experiments respectively. The experimental results demonstrate that it is possible to build muscle-computer interfaces with neck and shoulder motions. In addition, the results of cross-time experiments designed to explore the relationship between training and accuracy in user-specific recognition indicate that users can interact accurately with computers using the defined motions only after four times training in different days.