Online Bimanual Manipulation Using Surface Electromyography and Incremental Learning

The paradigm of simultaneous and proportional myocontrol of hand prostheses is gaining momentum in the rehabilitation robotics community. As opposed to the traditional surface electromyography classification schema, in simultaneous and proportional control the desired force/torque at each degree of freedom of the hand/wrist is predicted in real-time, giving to the individual a more natural experience, reducing the cognitive effort and improving his dexterity in daily-life activities. In this study we apply such an approach in a realistic manipulation scenario, using 10 non-linear incremental regression machines to predict the desired torques for each motor of two robotic hands. The prediction is enforced using two sets of surface electromyographyelectrodesand an incremental, non-linear machine learning technique called Incremental RidgeRegressionwith Random Fourier Features. Nine ablebodied subjects were engaged in a functional test with the aim to evaluate the performance of the system. The robotic hands were mounted on two hand/wrist orthopedic splints worn by healthy subjects and controlled online. An average completion rate of more than 95% was achieved in singlehanded tasks and 84% in bimanual tasks. On average, 5min of retraining were necessary on a total session duration of about 1 h and 40 min. This work sets a beginning in the study of bimanualmanipulation with prostheses and will be carried on through experiments in unilateral and bilateral upper limb amputees thus increasing its scientific value.