Multi-subject/daily-life activity EMG-based control of mechanical hands

Background: Forearm surface electromyography (EMG) has been in use since the Sixties to feed-forward control active hand prostheses in a more and more refined way. Recent research shows that it can be used to control even a dexterous polyarticulate hand prosthesis such as Touch Bionics's i-LIMB, as well as a multifingered, multi-degree-of-freedom mechanical hand such as the DLR II. In this paper we extend previous work and investigate the robustness of such fine control possibilities, in two ways: firstly, we conduct an analysis on data obtained from 10 healthy subjects, trying to assess the general applicability of the technique; secondly, we compare the baseline controlled condition (arm relaxed and still on a table) with a Daily-Life Activity (DLA) condition in which subjects walk, raise their hands and arms, sit down and stand up, etc., as an experimental proxy of what a patient is supposed to do in real life. We also propose a cross-subject model analysis, i.e., training a model on a subject and testing it on another one. The use of pre-trained models could be useful in shortening the time required by the subject/patient to become proficient in using the hand. Results: A standard machine learning technique was able to achieve a real-time grip posture classification rate of about 97% in the baseline condition and 95% in the DLA condition; and an average correlation to the target of about 0.93 (0.90) while reconstructing the required force. Cross-subject analysis is encouraging although not definitive in its present state. Conclusion: Performance figures obtained here are in the same order of magnitude of those obtained in previous work about healthy subjects in controlled conditions and/or amputees, which lets us claim that this technique can be used by reasonably any subject, and in DLA situations. Use of previously trained models is not fully assessed here, but more recent work indicates it is a promising way ahead.