The paper describes the development of an open-source, low-cost, wearable hand myoelectrical orthosis (neuro-orthosis) device for people with hand disabilities. The device uses functional electrical stimulation (FES) driven by myoelectrical signals (MES) to assist hand movements, enabling users to perform daily activities with greater ease and independence. The device comprises a forearm-wearable device developed using the 3D additive manufacturing principle, allowing user customization. Fixed non-disposable electrodes are attached to the myoelectrical orthosis, aiding the correct positioning for the user. The whole control system is stand-alone, and parameters can be controlled by Bluetooth communication, making the device wireless. The paper describes the MES-FES device's design, development, and testing, including its technical specifications, usability, and effectiveness. The open-source project aims to provide an accessible and affordable solution for people with spinal cord lesions while contributing to the growing research on noninvasive muscle-machine interfaces.

Purpose: In incomplete spinal cord injury (SCI), there is a partial decrease in motor or sensory or autonomic function. Mainly due to the motor impairment in SCI, a muscle–machine interface is a tool that can bring functional benefits to this population. Objective: To investigate the feasibility of the non-invasive myoelectric signal–functional electrical stimulation (MES-FES) interface on the response of the quadriceps muscle in an individual with incomplete SCI. Methods: This is a quasi-experimental, uncontrolled, longitudinal case report study carried out with an individual with incomplete SCI in the chronic phase. The assessments performed before (pre) and after eight (post₈) interventions were neuromuscular assessment (surface electromyography (EMG) in rectus femoris (RF) and vastus lateralis (VL) muscles); muscle strength (load cell); knee extension range of motion (goniometry); spasticity (Modified Ashworth Scale); and quality of life (Spinal Cord Injury Quality-of-Life Questionnaire (SCI-QoL.Br)). The MES-FES interface was associated with physical therapy exercises on the extension knee joint muscle group. Results: Improvement in neuromuscular activation (normalized increase in EMG_RMS of 2% (RF) and 3.3% (VL)) and synchronism of the motor units (normalized reduction in EMG_MDF of 22.8% (RF) and 5.9% (VL)); 1.4 kgf increase in quadriceps strength; 10.6° increase in knee joint extension amplitude; 1 point spasticity reduction; improved quality of life, confirmed by a 12-point reduction in the SCI-QoL.Br score. Moreover, along with interventions, the participant increased the correct FES activation rate, indicating a user learning curve ($\rho$ = 0.78, p-value = 0.04). Conclusions: The MES-FES interface associated with physical therapy promotes neuromuscular and quality of life improvements in the SCI participant.

The use of assistive technologies, such as a non-invasive interface for neuroelectrical signal and functional electrical stimulation (NES${}_s$-FES${}_s$), can mitigate the effects of spinal cord injury (SCI), including impairment of motor, sensory, and autonomic functions. However, it requires an adaptation process to enhance the user’s performance by tuning the learning curve to a point of extreme relevance. Therefore, in this pilot study, the learning curves of two people with complete SCI ($P_A$: paraplegic-T${}_6$, and $P_B$: quadriplegic-C${}_4$) were analyzed, with results obtained on the accuracy of the classifier (Ac${}_{CSP-LDA}$), repetitions of intra-day training, and number of hits and misses in the activation of FES${}_s$ for sixteen interventions using the NES${}_s$-FES${}_s$ interface. We assumed that the data were non-parametric and performed the Spearman’s $\rho$ test (and p-value) for correlations between the data. There was variation between the learning curves resulting from the training of the NES${}_s$-FES${}_s$ interface for the two participants, and the variation was influenced by factors both related and unrelated to the individual users. Regardless of these factors, $P_A$ improved significantly in its learning curve, as it presented lower values in all variables in the first interventions compared to the $P_B$, although only $P_A$ showed statistical correlation (on Ac${}_{CSP-LDA}$ values in RLL). It was concluded that despite the variations according to factors intrinsic to the user and the functioning of the equipment used, sixteen interventions were sufficient to achieve a good learning effect to control the NES${}_s$-FES${}_s$ interface.

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