I-RIM Conference 2019 October 18 - 20, Rome, Italy ISBN: 9788894580501 EMG-based decoding of motor tasks in amputees using a minimal set of EMG electrodes. F. Barberi* f.barberi@sant annapisa.it F. Aprigliano* f.aprigliano@san tannapisa.it E. Gruppioni° e.gruppioni@i nail.it A. Davalli° a.davalli@inail .it R. Sacchetti° r.sacchetti@in ail.it A. Mazzoni* a.mazzoni@sa ntannapisa.it S. Micera* s.micera@sant annapisa.it *The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy °INAIL Prosthesis Center Vigorso di Budrio (BO), Italy Abstract— Lower-limb powered prostheses have been widely studied and optimized over the years to help amputees improving their life quality and their walking performances during different motor tasks. Prostheses control can rely on various kinds of sensors, like inertial sensors and accelerometers. Among these, electromyographic (EMG) - control of powered prostheses seems to be a promising approach. In this study, we recorded EMG signals from four muscles (i.e. Adductor, Tensor Fasciae Latae, Rectus Femoris and Biceps Femoris) of the stump of thirteen transfemoral amputee patients while they were performing five motor tasks: ground level walking, stairs ascending, stairs descending, ramp ascending and ramp descending. Recorded signals were processed and the mean absolute value (MAV) was extracted over a 20ms moving window. Post-processed signals were then used as input of a Support Vector Machine-based algorithm to classify the motor tasks. The algorithm followed two steps of decoding of motor tasks, classifying ground level walking tasks first, and trying to identify stairs ascending, stairs descending, ramp ascending and ramp descending afterwards. Results showed that it was possible to identify motor tasks with one single muscle, with an accuracy ranging between 70% and 83%. Increasing the number of muscles used for the classification led to higher performances: combining EMG from three or four muscles resulted in performances above 90%. These results proved that it is possible to reliably decode motor tasks with a EMG-based algorithm relying on as little as three recording sites, ensuring a minimum discomfort of the system for the users. Keywords — EMG, classifier, prosthesis, decoder, lower limb I. INTRODUCTION Lower-limb amputees experience a general low confidence while using their prostheses, expecially during challenging motor tasks. This leads to a bad distribution of their weight and asymmetric walking patterns, increasing the chances of suffering from back pain and joint degeneration [1]. Prostheses differ in the degree of assistance that they provide to the users. Passive prostheses do not actively assist the users and are used just as a physical support. Semi-active prostheses provide assistance adapting the stiffness of their joints based on the gait modality. Powered prostheses actively participate in the movement, changing the position of the prosthetic joints based on the performed activity. The actuation of these prostheses can be based on information exctracted from sensors like gyroscopes, accelerometers, goniometers, electromyographic (EMG) electrodes etc [2]. Using EMG signals can allow high degrees of voluntary control of the prosthesis. EMG signals were widely used to control ankle-foot prostheses in transtibial amputees [3]. EMG-controlled powered knee prostheses is still an open challenge, and many studies investigated this kind of control by using both EMG signals alone or in combination with other sensors [4]. Many studies used high number of EMG electrodes [5]. This can be in some cases difficult to implement, depending on the type and length of the amputation. In this study, a new decoding algorithm was implemented and tested on electromyographic signals collected from 13 transfemoral amputees. We investigated which was the minimum number of EMG electrodes that could guarantee high performances of the classification algorithm while maintaining minimum discomfort i