Aim To investigate whether activity‐monitors and machine learning models could provide accurate information about physical activity performed by children and adolescents with cerebral palsy (CP) who use mobility aids for ambulation. Method Eleven participants (mean age 11y [SD 3y]; six females, five males) classified in Gross Motor Function Classification System (GMFCS) levels III and IV, completed six physical activity trials wearing a tri‐axial accelerometer on the wrist, hip, and thigh. Trials included supine rest, upper‐limb task, walking, wheelchair propulsion, and cycling. Three supervised learning algorithms (decision tree, support vector machine [SVM], random forest) were trained on features in the raw‐acceleration signal. Model‐performance was evaluated using leave‐one‐subject‐out cross‐validation accuracy. Results Cross‐validation accuracy for the single‐placement models ranged from 59% to 79%, with the best performance achieved by the random forest wrist model (79%). Combining features from two or more accelerometer placements significantly improved classification accuracy. The random forest wrist and hip model achieved an overall accuracy of 92%, while the SVM wrist, hip, and thigh model achieved an overall accuracy of 90%. Interpretation Models trained on features in the raw‐acceleration signal may provide accurate recognition of clinically relevant physical activity behaviours in children and adolescents with CP who use mobility aids for ambulation in a controlled setting. What this paper adds Machine learning may assist clinicians in evaluating the efficacy of surgical and therapy‐based interventions. Machine learning may help researchers better understand the short‐ and long‐term benefits of physical activity for children with more severe motor impairments.

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Serotonin (5-HT) is a neuromodulator that is critical for regulating the excitability of spinal motoneurons and the generation of muscle torque. However, the role of 5-HT in modulating human motor unit activity during rapid contractions has yet to be assessed. Nine healthy participants (23.7 ± 2.2 yr) ingested 8 mg of the competitive 5-HT2 antagonist cyproheptadine in a double-blinded, placebo-controlled, repeated-measures experiment. Rapid dorsiflexion contractions were performed at 30%, 50% and 70% of maximal voluntary contraction (MVC), where motor unit activity was assessed by high-density surface electromyographic decomposition. A second protocol was performed where a sustained, fatigue-inducing dorsiflexion contraction was completed prior to undertaking the same 30%, 50% and 70% MVC rapid contractions and motor unit analysis. Motor unit discharge rate (p < 0.001) and rate of torque development (RTD; p = 0.019) for the unfatigued muscle were both significantly lower for the cyproheptadine condition. Following the fatigue inducing contraction, cyproheptadine reduced motor unit discharge rate (p < 0.001) and RTD (p = 0.024), where the effects of cyproheptadine on motor unit discharge rate and RTD increased with increasing contraction intensity. Overall, these results support the viewpoint that serotonergic effects in the central nervous system occur fast enough to regulate motor unit discharge rate during rapid powerful contractions.

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Recent advancements in the analysis of high-density surface electromyography (HDsEMG) have enabled the identification, and tracking, of motor units (MUs) to study muscle activation. This study aimed to evaluate the reliability of MU tracking using two common methods: blind source separation filters and two-dimensional waveform cross-correlation. An experiment design was developed to assess physiological reliability, and reliability for a drug intervention known to reduce the firing rate of motoneurones (cyproheptadine). HDsEMG signals were recorded from tibialis anterior during isometric dorsiflexions to 10%, 30%, 50% and 70% of maximal voluntary contraction. MUs were matched within session (2 hr) using the filter method, and between sessions (7 days) via the waveform method. Both tracking methods demonstrated similar reliability during physiological conditions (e.g., MU discharge: filter ICC 10% of MVC = 0.76, to 70% of MVC = 0.86; waveform ICC: 10% of MVC = 0.78, to 70% of MVC = 0.91). Although reliability slightly reduced after the pharmacological intervention, there were no discernible differences in tracking performance (e.g., MU disc filter ICC: 10% of MVC = 0.73, to 70% of MVC = 0.75; DR waveform ICC: 10% of MVC = 0.84, to 70% of MVC = 0.85). The poorest reliability typically occurred at higher contraction intensities, which aligned with the greatest variability in MU characteristics. This study confirms that tracking method may not impact the interpretation of MU data, provided that an appropriate experiment design is employed. However, caution should be used when tracking MUs during higher intensity isometric contractions.