Working towards the development of robust motion recognition systems for assistive technology control, the widespread approach has been to use a plethora of, often times, multi-modal sensors. In this paper, we develop single-sensor motion recognition systems. Utilising the peripheral nature of surface electromyography (sEMG) data acquisition, we optimise the information extracted from sEMG sensors. This allows the reduction in sEMG sensors or provision of contingencies in a system with redundancies. In particular, we process the sEMG readings captured at the trapezius descendens and platysma muscles. We demonstrate that sEMG readings captured at one muscle contain distinct information on movements or contractions of other agonists. We used the trapezius and platysma muscle sEMG data captured in able-bodied participants and participants with tetraplegia to classify shoulder movements and platysma contractions using white-box supervised learning algorithms. Using the trapezius sensor, shoulder raise is classified with an accuracy of 99%. Implementing subject-specific multi-class classification, shoulder raise, shoulder forward and shoulder backward are classified with a 94% accuracy amongst object raise and shoulder raise-and-hold data in able bodied adults. A three-way classification of the platysma sensor data captured with participants with tetraplegia achieves a 95% accuracy on platysma contraction and shoulder raise detection.

This paper analyses diversity and intersectionality aspects in the R&D of wearable assistive and rehabilitation technologies (WEARTechs). We advocate for inclusive, innovative research that we hope will help bridge the gap between laboratories and the real world and reduce disparities in healthcare and technology development. We performed a systematic literature review of the intersections between assistive technologies and diversity and conducted a thematic analysis of the diversity factors identified in the literature. In addition, we carried out a supplementary literature search on WEARTechs to discover which, if any, diversity aspects are currently being reported on. Our findings indicate that diversity has not been addressed in the field of WEARTechs. There is not sufficient knowledge to determine, which diversity-related aspects researchers must consider when evaluating the performance of any specific WEARTech device. Nor about how these can be properly addressed in the R&D process. We, therefore, provide actionable recommendations on how to integrate diversity-relevant aspects at different R&D stages. We hope that our review will help scientists rethink and reformulate approaches to the R&D of WEARTechs and build the way towards more inclusive solutions. It is our belief that this will spark innovation and enhance discovery potential in the field.

Background: Muscle fatigue affects motor neuron function and, in turn, the electromyography\,(EMG) signals used to control wearable technologies. By accounting for this, we can more accurately regulate movement assistance. Muscle fatigue has been widely studied in the fields of sports science, rehabilitation, and occupational health. However, there are contradictory findings in the literature, and some aspects of muscle fatigue mechanisms are not yet well understood. Furthermore, none of the literature found focuses on the detection of early-stage muscle fatigue. Results: In this paper we specifically focus on early-stage muscle fatigue and show that the spectral variance (\((S_{var})\)) of the EMG signal is a more reliable measure of fatigue across participants and contraction levels compared to other widely used features. We then make recommendations on how these findings can be used in EMG-controlled assist-as-needed technologies. Experiments were carried out with 16 participants who performed intermittent isometric contractions of the biceps brachii at 20\,%, 50\,% and 75\,% MVC. The reliability of the motor unit action potential conduction velocity (CV) and the median/mean power frequencies (MDPF/MNPF) as measures of fatigue was tested. CV exhibited some linear correlation with perceived fatigue only at the higher contraction levels, for the majority of participants (R2=\((0.18\pm0.23)\)). Conclusions: CV was found to be an unreliable measure for muscle fatigue and no relationship was found between MDPF/MNPF and fatigue. However, for all participants and contraction levels, the fatigue-\((S_{var})\) relationship could be characterized using a Gaussian model\,(R2=\((0.87\pm0.12)\)).