Skeletal muscle contains a highly hierarchical structure, leading to anisotropic mechanical properties, with varying morphological responses to mechanical loadings from different directions. However, this feature is rarely studied in clinical studies, mainly due to the challenges in quantifying muscle anisotropic mechanical properties in vivo. The aim of the current study was to quantify the anisotropic mechanical properties of skeletal muscle using an integrated approach combining multi-frequency magnetic resonance elastography (MRE) and diffusion tensor imaging (DTI). Muscle fascicle orientation was determined through DTI tractography. Direct inversion of the curl-based wave equation was used to quantify three complex-valued moduli (μ⊥∗, μ‖∗, and E‖∗) assuming muscle as an incompressible transversely isotropic material. This approach was evaluated on one ex vivo muscle sample by comparing MRE-derived moduli to rheometry measurements, and further assessed in vivo in the ankle plantarflexors of nine able-bodied subjects. Consistency in the anisotropic ratio was observed between rheometry and MRE measurements in the ex vivo muscle sample, though discrepancies were noted in absolute shear moduli values. In vivo, the anisotropy of skeletal muscle was observed by the relationship of μ⊥∗≠1/3E‖∗ and μ‖∗≠1/3E‖∗ at different MRE driving frequencies with higher parallel shear modulus (μ‖∗) than the perpendicular shear modulus (μ⊥∗). This study demonstrated a promising approach for quantifying the muscle anisotropic mechanical properties in vivo, which can be useful in various clinical applications.

Machine learning (ML) has emerged as a powerful tool to analyze gait data, yet the “black-box” nature of many ML models hinders their clinical application. Explainable artificial intelligence (XAI) promises to enhance the interpretability and transparency of ML models, making them more suitable for clinical decision-making. This systematic review, registered on PROSPERO (CRD42024622752), assessed the application of XAI in gait analysis by examining its methods, performance, and potential for clinical utility. A comprehensive search across four electronic databases yielded 3676 unique records, of which 31 studies met inclusion criteria. These studies were categorized into model-agnostic (n = 16), model-specific (n = 12), and hybrid (n = 3) interpretability approaches. Most applied local interpretation methods such as SHAP and LIME, while others used Grad-CAM, attention mechanisms, and Layer-wise Relevance Propagation. Clinical populations studied included Parkinson’s disease, stroke, sarcopenia, cerebral palsy, and musculoskeletal disorders. Reported outcomes highlighted biomechanically relevant features such as stride length and joint angles as key discriminators of pathological gait. Overall, the findings demonstrate that XAI can bridge the gap between predictive performance and interpretability, but significant challenges remain in standardization, validation, and balancing accuracy with transparency. Future research should refine XAI frameworks and assess their real-world clinical applicability across diverse gait disorders.

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.

Wearable robotic exoskeletons are frequently explored for their efficacy in physical rehabilitation and for assistance in daily activities in people with motor disorders, yet relatively few have convincing evidence for use. The concept of human-in-the-loop optimization has been used to identify ideal exoskeleton assistive torques based on measured individual performance metrics, whereas few studies report optimizing several performance metrics simultaneously. In this study, we propose a multi-objective human-in-the-loop optimization that adjusts exoskeleton assistive profiles to improve several gait quality measures, specifically foot segment kinematics and step length symmetry. A preliminary proof-of-concept evaluation was conducted with five non-disabled participants, where a weighted shoe was used to simulate the gait deviations associated with dropfoot and excessive inversion. The gait quality metrics improved more for each new generation. With optimal assistive solutions, foot segment kinematics improved (10 mm higher foot clearance height and a 7∘ increase in inclination angle) and step length became more symmetric (asymmetry reduced from 9% to 2%), compared to simulated impairment. Within this set of solutions, each represents a unique balance between the two objectives, wherein a solution that prioritized normalizing step length symmetry could slightly sacrifice normalizing foot segment kinematics, and vice versa. By taking advantage of the trade-off relationship between the two objectives, more flexible, individualized, and situation-dependent assistance can be obtained. These results demonstrate the method's usefulness in determining subject-specific exoskeleton control parameters that improve several measures of gait. Future applications include refining this protocol for actual dropfoot, offering personalized assistance to restore their functional mobility and reduce compensatory movements, and validating its long-term effects.

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.

Accurate evaluation of electrophysiological and morphological characteristics of the skeletal muscles is critical to establish a comprehensive assessment of the human neuromusculoskeletal function in vivo. However, current technological challenges lie in unsynchronized and unparallel operation of separate acquisition systems such as surface electromyography (sEMG) and ultrasonography. Key problem is the lack of ultrasound transparency of current electrophysiological electrodes. In this work, ultrasound (US) transparent electrode based on cellulose nanofibrils (CNF) substrate are proposed to solve the issue. US transparency of the electrodes are evaluated using a standard US phantom. The effects of nanocellulose type and ion-bond introduction on electrode performance is investigated. Simultaneous US image and sEMG signal acquisition of biceps brachii during isometric muscle contraction are studied. Reliable correlation analysis of the US and sEMG signals is realized which is rarely reported in the previous literatures. Recyclability and biodegradability of the current electrode are evaluated. The reported technology opens up new pathways to provide coupled anatomical and electrical information of the skeletal muscles, enables reliable anatomical and electrical information correlation analysis and largely simplify the sensor integration for assessment of the human neuromusculoskeletal function.

This is a protocol for comprehensive analysis of gait and affecting factors in individuals with incomplete paraplegia due to spinal cord injury (SCI). A SCI is a devastating event affecting both sensory and motor functions. Due to better care, the SCI population is changing, with a greater proportion retaining impaired ambulatory function. Optimizing ambulatory function after SCI remains challenging. To investigate factors influencing optimal ambulation, a multi-professional research project was grounded with expertise from clinical rehabilitation, neurophysiology, and biomechanical engineering from Karolinska Institutet, the Spinalis Unit at Aleris Rehab Station (Sweden's largest center for specialized neurorehabilitation), and the Promobilia MoveAbility Lab at KTH Royal Institute of Technology. Ambulatory adults with paraplegia will be consecutively invited to participate. Muscle strength, sensitivity, and spasticity will be assessed, and energy expenditure, 3D movements, and muscle function (EMG) during gait and submaximal contractions will be analyzed. Innovative computational modeling and data-driven analyses will be performed, including the identification of clusters of similar movement patterns among the heterogeneous population and analyses that study the link between complex sensorimotor function and movement performance. These results may help optimize ambulatory function for persons with SCI and decrease the risk of secondary conditions during gait with a life-long perspective.

Secondary morphological and mechanical property changes in the muscle-tendon unit at the ankle joint are often observed in post-stroke individuals. These changes may alter the force generation capacity and affect daily activities such as locomotion. This work aimed to estimate subject-specific muscle-tendon parameters in individuals after stroke by solving the muscle redundancy problem using direct collocation optimal control methods based on experimental electromyography (EMG) signals and measured muscle fiber length. Subject-specific muscle-tendon parameters of the gastrocnemius, soleus, and tibialis anterior were estimated in seven post-stroke individuals and seven healthy controls. We found that the maximum isometric force, tendon stiffness and optimal fiber length in the post-stroke group were considerably lower than in the control group. We also computed the root mean square error between estimated and experimental values of muscle excitation and fiber length. The musculoskeletal model with estimated subject-specific muscle tendon parameters (from the muscle redundancy solver), yielded better muscle excitation and fiber length estimations than did scaled generic parameters. Our findings also showed that the muscle redundancy solver can estimate muscle-tendon parameters that produce force behavior in better accordance with the experimentally-measured value. These muscle-tendon parameters in the post-stroke individuals were physiologically meaningful and may shed light on treatment and/or rehabilitation planning.

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.