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After exposure of the human body to resistive exercise, the force‐generation capacity of the trained muscles increases significantly. Despite decades of research, the neural and muscular stimuli that initiate these changes in muscle force are not yet fully understood. The study of these adaptations is further complicated by the fact that the changes may be partly specific to the training task. For example, short‐term strength training does not always influence the neural drive to muscles during the early phase (<100 ms) of force development in rapid isometric contractions. Here we discuss some of the studies that have investigated neuromuscular adaptations underlying changes in maximal force and rate of force development produced by different strength training interventions, with a focus on changes observed at the level of spinal motor neurons. We discuss the different motor unit adjustments needed to increase force or speed, and the specificity of some of the adaptations elicited by differences in the training tasks. [ABSTRACT FROM AUTHOR]

The advancements in deep neural network design have led to a significant increase in the possibilities and functioning of AI-assisted medical hardware. To make use of this progress in the field of mobile applications or even as wearable devices, a suitable hardware-software ecosystem must be identified to meet the high computation and memory demands of neural networks with minimal energy consumption. In this paper, we analyze an up-to-date heterogenous embedded platform employing a deep convolutional network for hand position recognition through electromyography signals. Our evaluation aimed to determine the optimization efforts required for the architecture to function as a human wearable device and identify the most suitable accelerators on the given platform for this task. [ABSTRACT FROM AUTHOR]

Motor units convert the last neural code of movement into muscle forces. The classic view of motor unit control is that the CNS sends common synaptic inputs to motoneuron pools and that motoneurons respond in an orderly fashion dictated by the size principle. This view, however, is in contrast with the large number of dimensions observed in motor cortex, which may allow individual and flexible control of motor units. Evidence for flexible control of motor units may be obtained by tracking motor units longitudinally during tasks with some level of behavioral variability. Here we identified and tracked populations of motor units in the brachioradialis muscle of two macaque monkeys during 10 sessions spanning >1 month with a broad range of rate of force development (1.8–38.6 N · m · s-1). We found a very stable recruitment order and discharge characteristics of the motor units over sessions and contraction trials. The small deviations from orderly recruitment were fully predicted by the motor unit recruitment intervals, so that small shifts in recruitment thresholds happened only during contractions at a high rate of force development. Moreover, we also found that one component explained more than ;50% of the motor unit discharge rate variance, and that the remaining components represented a time-shifted version of the first. In conclusion, our results show that the recruitment of motoneurons is determined by the interplay of the size principle and common input and that this recruitment scheme is not violated over time or by the speed of the contractions. [ABSTRACT FROM AUTHOR]

Motor neurons convey information about motor intent that can be extracted and interpreted to control assistive devices. However, most methods for measuring the firing activity of single neurons rely on implanted microelectrodes. Although intracortical brain-computer interfaces (BCIs) have been shown to be safe and effective, the requirement for surgery poses a barrier to widespread use that can be mitigated by instead using noninvasive interfaces. The objective of this study was to evaluate the feasibility of deriving motor control signals from a wearable sensor that can detect residual motor unit activity in paralyzed muscles after chronic cervical spinal cord injury (SCI). Despite generating no observable hand movement, volitional recruitment of motor units below the level of injury was observed across attempted movements of individual fingers and overt wrist and elbow movements. Subgroups of motor units were coactive during flexion or extension phases of the task. Single digit movement intentions were classified offline from the electromyogram (EMG) power [root-mean-square (RMS)] or motor unit firing rates with median classification accuracies >75% in both cases. Simulated online control of a virtual hand was performed with a binary classifier to test feasibility of real-time extraction and decoding of motor units. The online decomposition algorithm extracted motor units in 1.2 ms, and the firing rates predicted the correct digit motion 88 ± 24% of the time. This study provides the first demonstration of a wearable interface for recording and decoding firing rates of motor units below the level of injury in a person with motor complete SCI. NEW & NOTEWORTHY A wearable electrode array and machine learning methods were used to record and decode myoelectric signals and motor unit firing in paralyzed muscles of a person with motor complete tetraplegia. The myoelectric activity and motor unit firing rates were task specific, even in the absence of visible motion, enabling accurate classification of attempted single-digit movements. This wearable system has the potential to enable people with tetraplegia to control assistive devices through movement intent. [ABSTRACT FROM AUTHOR]

The aim of the present paper is to examine to what extent the application of surface electromyography (sEMG) in the field of exercise and, more in general, of human movement, is adopted by professionals on a regular basis. For this purpose, a brief history of the recent developments of modern sEMG techniques will be assessed and evaluated for a potential use in exercise physiology and clinical biomechanics. The idea is to understand what are the limitations that impede the translation of sEMG to applied fields such as exercise physiology. A cost/benefits evaluation will be drawn in order to understand possible causes that prevents sEMG from being routinely adopted. Among the possible causative factors, educational, economic and technical issues will be considered. Possible corrective interventions will be proposed. We will also give an overview of the parameters that can be extracted from the decomposition of the sHDEMG signals and how this can be related by professionals for assessing the health and disease of the neuromuscular system. We discuss how the decomposition of surface EMG signals might be adopted as a new non-invasive tool for assessing the status of the neuromuscular system. Recent evidences show that is possible to monitor the changes in neuromuscular function after training of longitudinally tracked populations of motoneurons, predict the maximal rate of force development by an individual via motoneuron interfacing, and identify possible causal relations between aging and the decrease in motor performance. These technologies will guide our understanding of motor control and provide a new window for the investigation of the underlying physiological processes determining force control, which is essential for the sport and exercise physiologist. We will also illustrate the challenges related to extraction of neuromuscular parameters from global EMG analysis (i.e., root-mean-square, and other global EMG metrics) and when the decomposition is needed. We posit that the main limitation in the application of sEMG techniques to the applied field is associated to problems in education and teaching, and that most of the novel technologies are not open source. [ABSTRACT FROM AUTHOR]

Key points: Previous studies have indicated that several weeks of strength training is sufficient to elicit significant adaptations in the neural drive sent to the muscles.There are few data, however, on the changes elicited by strength training in the recruitment and rate coding of motor units during voluntary contractions. We show for the first time that the discharge characteristics of motor units in the tibialis anterior muscle tracked across the intervention are changed by 4 weeks of strength training with isometric voluntary contractions.The specific adaptations included significant increases in motor unit discharge rate, decreases in the recruitment‐threshold force of motor units and a similar input–output gain of the motor neurons.The findings suggest that the adaptations in motor unit function may be attributable to changes in synaptic input to the motor neuron pool or to adaptations in intrinsic motor neuron properties. The strength of a muscle typically begins to increase after only a few sessions of strength training. This increase is usually attributed to changes in the neural drive to muscle as a result of adaptations at the cortical or spinal level. We investigated the change in the discharge characteristics of large populations of longitudinally tracked motor units in tibialis anterior before and after 4 weeks of strength training the ankle‐dorsiflexor muscles with isometric contractions. The adaptations exhibited by 14 individuals were compared with 14 control subjects. High‐density electromyogram grids with 128 electrodes recorded the myoelectric activity during isometric ramp contractions to the target forces of 35%, 50% and 70% of maximal voluntary force. The motor unit recruitment and derecruitment thresholds, discharge rate, interspike intervals and estimates of synaptic inputs to motor neurons were assessed. The normalized recruitment‐threshold forces of the motor units were decreased after strength training (P < 0.05). Moreover, discharge rate increased by 3.3 ± 2.5 pps (average across subjects and motor units) during the plateau phase of the submaximal isometric contractions (P < 0.001). Discharge rates at recruitment and derecruitment were not modified by training (P < 0.05). The association between force and motor unit discharge rate during the ramp‐phase of the contractions was also not altered by training (P < 0.05). These results demonstrate for the first time that the increase in muscle force after 4 weeks of strength training is the result of an increase in motor neuron output from the spinal cord to the muscle. Key points: Previous studies have indicated that several weeks of strength training is sufficient to elicit significant adaptations in the neural drive sent to the muscles.There are few data, however, on the changes elicited by strength training in the recruitment and rate coding of motor units during voluntary contractions. We show for the first time that the discharge characteristics of motor units in the tibialis anterior muscle tracked across the intervention are changed by 4 weeks of strength training with isometric voluntary contractions.The specific adaptations included significant increases in motor unit discharge rate, decreases in the recruitment‐threshold force of motor units and a similar input–output gain of the motor neurons.The findings suggest that the adaptations in motor unit function may be attributable to changes in synaptic input to the motor neuron pool or to adaptations in intrinsic motor neuron properties. [ABSTRACT FROM AUTHOR]