The development of fatigue at the muscle fibre level can be assessed in terms of a decrease in conduction velocity (CV). The present study aimed to investigate if work-related muscular disorders in occupations characterised by static loads of long duration affect fatigue resistance in the painful muscle. A group of eight secretaries suffering from bilateral chronic muscle pain in the shoulder/neck region was compared to a group of healthy subjects. The upper trapezius muscle was studied under isometric contractions, holding the arm in the horizontal plane up to the endurance point. Changes in CV estimated at the motor unit level were investigated using a non-invasive high spatial resolution electromyographic (EMG) approach. In addition, the number of motor unit potentials per second (PPS), and the root mean square (RMS) of bipolar signals were assessed, and the results reported as the mean and standard error for each value. Subjects with work-related disorders showed less pronounced changes in CV with respect to healthy subjects. No differences between subjects with and without work-related disorders were encountered for the PPS and RMS. The present findings on CV indicate an increased fatigue-related recruitment of MUs in the painful muscle with respect to a healthy muscle. The fact that this recruitment is not reflected in the PPS and RMS estimates might be due to a fatigue-induced decrease in the firing rate and/or the de-recruitment of fatigued MUs. Furthermore, methodological limitations of the adopted method in the estimation of 'global' parameters such as the PPS and RMS have to be considered.

Wearable devices play an increasing role in the rehabilitation of patients with movement disorders. Although information about muscular activation is highly interesting, no approach exists that allows reliable collection of this information when the sensor is applied autonomously by the patient. This paper aims to demonstrate the proof-of-principle of an innovative sEMG sensor system, which can be used intuitively by patients while detecting their muscular activation with sufficient accuracy. The sEMG sensor system utilizes a multichannel approach based on 16 sEMG leads arranged circularly around the limb. Its design enables a stable contact between the skin surface and the system’s dry electrodes, fulfills the SENIAM recommendations regarding the electrode size and inter-electrode distance and facilitates a high temporal resolution. The proof-of-principle was demonstrated by elbow flexion/extension movements of 10 subjects, proving that it has root mean square values and a signal-to-noise ratio comparable to commercial systems based on pre-gelled electrodes. Furthermore, it can be easily placed and removed by patients with reduced arm function and without detailed knowledge about the exact positioning of the sEMG electrodes. With its features, the demonstration of the sEMG sensor system’s proof-of-principle positions it as a wearable device that has the potential to monitor muscular activation in home and community settings.