A human–computer interface for wrist rehabilitation: a pilot study using commercial sensors to detect wrist movements

Abstract

Health conditions might cause muscle weakness and immobility in some body parts; hence, physiotherapy exercises play a key role in the rehabilitation. To improve the engagement during the rehabilitation process, we therefore propose a human–computer interface (serious game) in which five wrist movements (extension, flexion, pronation, supination and neutral) are detected via two commercial sensors (Leap motion controller and Myo armband). Leap motion provides data regarding positions of user’s finger phalanges through two infrared cameras, while Myo armband facilitates electromyography signal and inertial motion of user’s arm through its electrodes and inertial measurement unit. The main aim of this study is to explore the performance of these sensors on wrist movement recognition in terms of accuracy, sensitivity and specificity. Eight healthy participants played 5 times a proposed game with each sensor in one session. Both sensors reported over 85% average recognition accuracy in the five wrist movements. Based on t test and Wilcoxon signed-rank test, early results show that there were significant differences between Leap motion controller and Myo armband recognitions in terms of average sensitivities on extension ( $$p = 0.0356$$ ), flexion ( $$p = 0.0356$$ ) and pronation ( $$p = 0.0440$$ ) movements, and average specificities on extension ( $$p = 0.0276$$ ) and pronation ( $$p = 0.0249$$ ) movements.