Background: Individuals who have experienced spinal cord injury (SCI) may exhibit various muscle-related neurophysiological adaptations, including alterations in motor unit (MU) size and firing behavior. However, due to the technical challenges of in vivo measurement, our understanding of the alterations in the electrophysiological parameters of these MUs remains limited. This study proposed an integrated approach using high-density electromyography (HD-EMG) decomposition and motor neuron (MN) modelling to estimate the intrinsic properties of MUs in vivo and investigated alterations of these properties in persons with SCI.

Methods: HD-EMG signals were recorded during submaximal isometric dorsiflexion and plantar flexion tasks on tibialis anterior (TA), soleus, and gastrocnemius medialis muscles from twenty-six participants with SCI and eighteen non-disabled controls. The HD-EMG signals were subsequently decomposed into MN spike trains and the common synaptic input to the MN pool was estimated. A simplified leaky integrate-and-fire neuron model was then used to simulate MN spiking trains, with soma size and inert period as tunning parameters, which are crucial for MU recruitment and firing patterns, respectively. These parameters were estimated by fitting the instantaneous discharge frequencies of decomposed and simulated spike trains via a genetic algorithm.

Results: The results showed a prolonged inert period in the TA of the persons with SCI. This finding suggested that the MUs in the TA have a slower recovery period before becoming excitable again, which may result in a lower firing rate of MUs in the TA muscle. No significant differences were observed in the soleus and gastrocnemius medialis muscles between the SCI and control groups for either the soma size or inert period parameters.

Conclusions: The simplified leaky integrate-and-fire model exhibited robustness in estimating MN parameters in vivo, offering valuable insights into personalized MU behavior monitoring. To the best knowledge of authors, this is the first study to combine HD-EMG and MU modeling to investigate MU electrophysiological changes in persons with SCI in vivo. This novel approach offers a comprehensive understanding of MU properties adaptations following neurological disorders and informs the development of novel rehabilitation strategies.

Background: Spinal cord injury (SCI) can lead to various neurophysiological changes, altering the neural motor control strategies. The lower limb muscles are of high importance for locomotion; however, there exists a significant knowledge gap in neurophysiological changes following SCI in these muscles. This study aims to explore the neuromuscular adaptations in the soleus and tibialis anterior muscles in persons with incomplete SCI. Methods: Ankle joint torque, high-density electromyography (HD-EMG) and motor unit parameters of tibialis anterior and soleus were analyzed during repeated sub-maximal voluntary isometric contractions (20% and 50% of the maximal torque) and compared to those from a control cohort. Results: We observed muscle-dependent alterations in motor control between the SCI and control groups. Namely, SCI group required significantly higher normalized EMG amplitudes than the control group to achieve the same contraction levels. At 50% contraction level, compared to control group, the SCI group motor units were recruited at lower thresholds in both muscles and fired at lower rates in the tibialis anterior muscle. We observed no significant differences in intramuscular motor unit coherence or muscle co-contraction between the two groups. Conclusions: The observed combination of between-group differences in motor unit behavior may indicate that even in muscles of high-functioning individuals with incomplete SCI, is a shift towards larger motor units in both tibialis anterior and soleus muscles. These results contribute to knowledge of neurophysiological modifications in major ankle muscles following a SCI and provide deeper insights into neurophysiological changes that can be used complementary to clinical SCI evaluation.

Robotic-powered exoskeletons represent a promising avenue for aiding individuals with movement disorders in their daily activities and rehabilitation efforts. However, achieving precise joint torque estimation, particularly during dynamic movements, remains a significant challenge. While machine learning and deep learning techniques have been ex-plored for estimation, their efficacy has been limited, especially in dynamic scenarios. Our target is to improve ankle joint torque estimation during dynamic movements by employing multiple data augmentation techniques. Augmentation methods did not significantly improve cases involving the same subject or session. However, our experiments reveal substantial performance gains when combining spatial and signal augmentation methods, particularly in scenarios involving different subjects. This indicated that when facing an over-fitting problem caused by a lack of subjects, a combined data augmentation method will be a proper solution to improve the predicting performance.

Reliable and accurate EMG-driven prediction of joint torques are instrumental in the control of wearable robotic systems. This study investigates how different EMG input features affect the machine learning algorithm-based prediction of ankle joint torque in isometric and dynamic conditions. High-density electromyography (HD-EMG) of five lower leg muscles were recorded during isometric contractions and dynamic tasks. Four datasets (HD-EMG, HD-EMG with reduced dimensionality, features extracted from HD-EMG with Convolutional Neural Network, and bipolar EMG) were created and used alone or in combination with joint kinematic information for the prediction of ankle joint torque using Support Vector Regression. The performance was evaluated under intra-session, inter-subject, and inter-session cases. All HD-EMG-derived datasets led to significantly more accurate isometric ankle torque prediction than the bipolar EMG datasets. The highest torque prediction accuracy for the dynamic tasks was achieved using bipolar EMG or HD-EMG with reduced dimensionality in combination with kinematic features. The findings of this study contribute to the knowledge allowing an informed selection of appropriate features for EMG-driven torque prediction.

Spinal cord injury (SCI) often results in impaired motor control and coordination. Previous studies have highlighted the role of muscle synergies in coordinating motor tasks and their alternations following SCI.  However, the adaptation in muscle synergy patterns at the motor unit (MU) level after SCI remains unexplored. This study aimed to investigate MU synergies and clustering in the synergetic soleus (SOL) and gastrocnemius medialis (GM) muscles and to explore how these patterns are altered in person with SCI. High-density electromyography (HD-EMG) was used to record MU activity in the SOL and GM muscles of fifteen participants with incomplete SCI and eight non-disabled participants during 20% and 50% maximal voluntary contraction tasks. The HD-EMG signals were decomposed into individual MU spike trains. Inter-muscle coherence analysis was employed to evaluate the shared neural drive between the SOL and GM muscles, and factor analysis was performed to identify synergistic clusters of MUs innervating each muscle. As expected, both participant groups demonstrated significant coherence between the SOL and GM muscles, highlighting a shared neural drive for coordinated function. However, participants with SCI showed altered coherence in the delta frequency band, with significantly higher coherence observed at 50% maximal voluntary contraction (p < 0.01). Additionally, factor analysis revealed that participants with SCI had a reduced proportion of MUs in the shared cluster within GM muscle at 20% maximal voluntary contraction (p < 0.01). These findings suggested that SCI may disrupt MU synergies and clustering, potentially impairing motor coordination. This research offered valuable insights into the underlying mechanism of muscle synergies and the neural adaptations following SCI, providing crucial information for the development of future rehabilitation strategies.