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Background: Respiratory muscle electromyography (EMG) can identify whether a muscle is activated, its activation amplitude, and timing. Most studies have focused on the activation amplitude, while differences in timing and duration of activity have been less investigated. Detection of the timing of respiratory muscle activity is typically based on the visual inspection of the EMG signal. This method is time-consuming and prone to subjective interpretation.Aims: Our main objective was to develop and validate a method to assess the respective timing of different respiratory muscle activity in an objective and semi-automated manner.Method: Seven healthy adults performed an inspiratory threshold loading (ITL) test at 50% of their maximum inspiratory pressure until task failure. Surface EMG recordings of the costal diaphragm/intercostals, scalene, parasternal intercostals, and sternocleidomastoid were obtained during ITL. We developed a semi-automated algorithm to detect the onset (EMG, onset) and offset (EMG, offset) of each muscle’s EMG activity breath-by-breath with millisecond accuracy and compared its performance with manual evaluations from two independent assessors. For each muscle, the Intraclass Coefficient correlation (ICC) of the EMG, onset detection was determined between the two assessors and between the algorithm and each assessor. Additionally, we explored muscle differences in the EMG, onset, and EMG, offset timing, and duration of activity throughout the ITL.Results: More than 2000 EMG, onset s were analyzed for algorithm validation. ICCs ranged from 0.75–0.90 between assessor 1 and 2, 0.68–0.96 between assessor 1 and the algorithm, and 0.75–0.91 between assessor 2 and the algorithm (p 0.63).Conclusion: We developed an algorithm to detect EMG, onset of several respiratory muscles with millisecond accuracy that is time-efficient and validated against manual measures. Compared to the inherent bias of manual measures, the algorithm enhances objectivity and provides a strong standard for determining the respiratory muscle EMG, onset.

Real-life daily handcycling requires combined propulsion and steering to control the front wheel. Today, the handcycle cranks are mostly mounted synchronously unlike the early handcycle generations. Alternatively, arm cycle ergometers do not require steering and the cranks are mostly positioned asynchronously. The current study aims to evaluate the effects of combining propulsion and steering requirements on synchronous and asynchronous submaximal handcycle ergometry. We hypothesize that asynchronous handcycling with steering results in the mechanically least efficient condition, due to compensation for unwanted rotations that are not seen in synchronous handcycling, regardless of steering. Sixteen able-bodied male novices volunteered in this lab-based experiment. The set-up consisted of a handcycle ergometer with 3D force sensors at each crank that also allows “natural” steering. Four submaximal steady-state (60 rpm, ~35 W) exercise conditions were presented in a counterbalanced order: synchronous with a fixed steering axis, synchronous with steering, asynchronous with a fixed axis and asynchronous with steering. All participants practiced 3 × 4 mins with 30 mins rest in between every condition. Finally, they did handcycle for 4 mins in each of the four conditions, interspaced with 10 mins rest, while metabolic outcomes, kinetics and kinematics of the ergometer were recorded. The additional steering component did not influence velocity, torque and power production during synchronous handcycling and therefore resulted in an equal metabolically efficient handcycling configuration compared to the fixed condition. Contrarily, asynchronous handcycling with steering requirements showed a reduced mechanical efficiency, as velocity around the steering axis increased and torque and power production were less effective. Based on the torque production around the crank and steering axes, neuromuscular compensation strategies seem necessary to prevent steering movements in the asynchronous mode. To practice or test real-life daily synchronous handcycling, a synchronous crank set-up of the ergometer is advised, as exercise performance in terms of mechanical efficiency, metabolic strain, and torque production is independent of steering requirements in that mode. Asynchronous handcycling or arm ergometry demands a different handcycle technique in terms of torque production and results in higher metabolic responses than synchronous handcycling, making it unsuitable for testing.

Background: Electrocardiogram (ECG) contamination is present in diaphragm electromyography (EMGdi) recordings. Obtaining EMGdi without ECG contamination is crucial for EMG amplitude analysis. Manually selecting EMGdi in between QRS complexes has been most commonly applied in recent years (manual method). We developed a semi-automated analysis method based on Least Mean Square Adaptive Filtering combined with a synchronously recorded separate ECG channel to remove ECG artifacts from the EMGdi signals. We hypothesized that this approach would shorten analysis duration and might minimize the potential for inter-rater disagreement.Aims: We aimed to evaluate agreement between the semi-automated method and the manual method and inter-rater reliability of the manual method.Methods: Electromyography signals of seven patients with COPD were recorded using an esophageal catheter during an exercise test on a cycle ergometer. Four patients subsequently participated in an inspiratory muscle training (IMT) program for 8 weeks. After IMT, the tests were repeated. EMGdi/EMGdiMax as obtained either manually by the two assessors or retrieved from the semi-automated method were compared.Results: Semi-automated EMGdi/EMGdiMax agreed well with values obtained by one of the two manual assessors (assessor 1) both at pre-intervention measurements (mean difference −0.5%, 95% CI: −19.6 to 18.6%) and for the pre/post IMT differences (mean difference 1.2%, 95% CI: −16.8 to 19.2%). Intra-class correlation coefficients between methods were 0.96 (95% CI: 0.94–0.97) at pre-intervention measurements and 0.78 (95% CI: 0.58–0.89) for pre/post IMT differences (both p < 0.001). EMGdi/EMGdiMax from assessor 2 was systematically lower than from assessor 1 and agreed less well with the semi-automated method both at pre-intervention measurements (mean difference: 9.3%, 95% CI: −11.4 to 29.9%) and for pre/post IMT differences (mean difference 7.0%, 95% CI: −20.4 to 34.4%). Analysis duration of the semi-automated method was significantly shorter (29 ± 9 min) than the manual method (82 ± 20 min, p < 0.001).Conclusion: The developed semi-automated method is more time efficient and will be less prone to inter-rater variability that was observed when applying the manual analysis method. It is, therefore, proposed as a new standard for objective EMGdi amplitude analyses in future studies.