The restoration of sensory feedback is one of the current challenges in the field of prosthetics. This work, following the analysis of the various types of sensory feedback, aims to present a prototype device that could be used both for implantable applications to perform PNS and for wearable applications, performing TENS, to restore sensory feedback. The two systems are composed of three electronic boards that are presented in detail, as well as the bench tests carried out. To the authors’ best knowledge, this work presents the first device that can be used in a dual scenario for restoring sensory feedback. Both the implantable and wearable versions respected the expected values regarding the stimulation parameters. In its implantable version, the proposed system allows simultaneous and independent stimulation of 30 channels. Furthermore, the capacity of the wearable version to elicit somatic sensations was evaluated on healthy participants demonstrating performance comparable with commercial solutions.

Introduction: Muscular activation sequences have been shown to be suitable time-domain features for classification of motion gestures. However, their clinical application in myoelectric prosthesis control was never investigated so far. The aim of the paper is to evaluate the robustness of these features extracted from the EMG signal in transient state, on the forearm, for classifying common hand tasks.Methods: The signal associated to four hand gestures and the rest condition were acquired from ten healthy people and two persons with trans-radial amputation. A feature extraction algorithm allowed for encoding the EMG signals into muscular activation sequences, which were used to train four commonly used classifiers, namely Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Non-linear Logistic Regression (NLR) and Artificial Neural Network (ANN). The offline performances were assessed with the entire sample of recruited people. The online performances were assessed with the amputee subjects. Moreover, a comparison of the proposed method with approaches based on the signal envelope in the transient state and in the steady state was conducted.Results: The highest performance were obtained with the NLR classifier. Using the sequences, the offline classification accuracy was higher than 93% for healthy and amputee subjects and always higher than the approach with the signal envelope in transient state. As regards the comparison with the steady state, the performances obtained with the proposed method are slightly lower (<4%), but the classification occurred at least 200 ms earlier. In the online application, the motion completion rate reached up to 85% of the total classification attempts, with a motion selection time that never exceeded 218 ms.Discussion: Muscular activation sequences are suitable alternatives to the time-domain features commonly used in classification problems belonging to the sole EMG transient state and could be potentially exploited in control strategies of myoelectric prosthesis hands.