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Advanced sensors/electrodes and signal processing techniques provide powerful tools to analyze surface electromyographic signals (sEMG) and their features, to decompose sEMG into the constituent motor unit action potential trains, and to identify synergies, neural muscle drive, and EEG–sEMG coherence. However, despite thousands of articles, dozens of textbooks, tutorials, consensus papers, and European and International efforts, the translation of this knowledge into clinical activities and assessment procedures has been very slow, likely because of lack of clinical studies and competent operators in the field. Understanding and using sEMG-based hardware and software tools requires a level of knowledge of signal processing and interpretation concepts that is multidisciplinary and is not provided by most academic curricula in physiotherapy, movement sciences, neurophysiology, rehabilitation, sport, and occupational medicine. The chasm existing between the available knowledge and its clinical applications in this field is discussed as well as the need for new clinical figures. The need for updating the training of physiotherapists, neurophysiology technicians, and clinical technologists is discussed as well as the required competences of trainers and trainees. Indications and examples are suggested and provide a basis for addressing the problem. Two teaching examples are provided in the Supplementary Material.

The motor features of people with cerebellar ataxia suggest that locomotion is substantially impaired due to incoordination of the head, trunk, and limbs. The purpose of this study was to investigate how well a wearable soft passive exoskeleton worked for motor coordination in these patients. We used an optoelectronic system to examine the gait of nine ataxic people in three different conditions: without an exoskeleton and with two variants of the exoskeleton, one less and the other more flexible. We investigated kinematics using trunk ranges of motion, the displacement of the center of mass in the medio-lateral direction, and the parameters of mechanical energy consumption and recovery. Furthermore, we investigated the lower limb and trunk muscle coactivation. The results revealed a reduction of the medio-lateral sway of the center of mass, a more efficient behavior of the body in the antero-posterior direction, an energy expenditure optimization, a reduction of muscle coactivation and a better coordination between muscle activations. As a result, the findings laid the groundwork for the device to be used in the rehabilitation of individuals with cerebellar ataxia. [ABSTRACT FROM AUTHOR]

In this study, we have analyzed the behavior of shear and compression forces at the L5-S1 joint during the execution of controlled lifting tasks designed on the basis of the revised NIOSH (National Institute for Occupational Safety and Health) lifting equation (RNLE) with an increasing lifting index (LI = 1, LI = 2, and LI = 3). We aim to verify the sensitivity of force indices with regard to risk levels. Twenty subjects performed the tasks, and the kinematic and kinetic data of their movement were acquired by using an optoelectronic motion analysis system and platform, respectively. Lumbosacral forces were calculated using the lower and upper body models, and some indices (i.e., maximum, medium, and range values) were extracted. Our findings confirm that the kinetic-based indices extracted from shear and compression forces at the L5-S1 joint are related to risk conditions, and they could improve the quantitative tools and machine-learning approaches that can also be used in a workspace to assess risk conditions during lifting tasks. [ABSTRACT FROM AUTHOR]

The central nervous system uses muscle co-activation for body coordination, effector movement control, and joint stabilization. However, co-activation increases compression and shear stresses on the joints. Lifting activity is one of the leading causes of work-related musculoskeletal problems worldwide, and it has been shown that when the risk level rises, lifting enhances trunk muscle co-activation at the L5/S1 level. This study aims to investigate the co-activation of lower limb muscles during liftings at various risk levels and lifting types (one-person and vs. two-person team lifting), to understand how the central nervous system governs lower limb rigidity during these tasks. The surface electromyographic signal of thirteen healthy volunteers (seven males and six females, age range: 29–48 years) was obtained over the trunk and right lower limb muscles while lifting in the sagittal plane. Then co-activation was computed according to different approaches: global, full leg, flexor, extensor, and rostro-caudal. The statistical analysis revealed a significant increase in the risk level and a decrease in the two-person on the mean and/or maximum of the co-activation in almost all the approaches. Overall, our findings imply that the central nervous system streamlines the motor regulation of lifting by increasing or reducing whole-limb rigidity within a distinct global, extensor, and rostro-caudal co-activation scheme, depending on the risk level/lifting type. [ABSTRACT FROM AUTHOR]

The interpretability of gait analysis studies in people with rare diseases, such as those with primary hereditary cerebellar ataxia (pwCA), is frequently limited by the small sample sizes and unbalanced datasets. The purpose of this study was to assess the effectiveness of data balancing and generative artificial intelligence (AI) algorithms in generating synthetic data reflecting the actual gait abnormalities of pwCA. Gait data of 30 pwCA (age: 51.6 ± 12.2 years; 13 females, 17 males) and 100 healthy subjects (age: 57.1 ± 10.4; 60 females, 40 males) were collected at the lumbar level with an inertial measurement unit. Subsampling, oversampling, synthetic minority oversampling, generative adversarial networks, and conditional tabular generative adversarial networks (ctGAN) were applied to generate datasets to be input to a random forest classifier. Consistency and explainability metrics were also calculated to assess the coherence of the generated dataset with known gait abnormalities of pwCA. ctGAN significantly improved the classification performance compared with the original dataset and traditional data augmentation methods. ctGAN are effective methods for balancing tabular datasets from populations with rare diseases, owing to their ability to improve diagnostic models with consistent explainability. [ABSTRACT FROM AUTHOR]

This editorial discusses the importance of job integration and reintegration for individuals with neuromuscular disorders and how advancements in technology can aid in this process. It emphasizes the need for rehabilitation, medical and surgical treatments, and ergonomic interventions to enable these individuals to fully benefit from vocational rehabilitation and overcome barriers to employment. The editorial also highlights the European Union's efforts to improve the social and economic status of persons with disabilities and the use of wearable monitoring sensors, feedback devices, exoskeletons, and collaborative robots in movement-related elements. The article proposes the use of a mobile collaborative robot with an interaction-assistive whole-body interface to help individuals who struggle with balance. [Extracted from the article]

The central nervous system (CNS) controls movements and regulates joint stiffness with muscle co-activation, but until now, few studies have examined muscle pairs during running. This study aims to investigate differences in lower limb muscle coactivation during gait at different speeds, from walking to running. Nineteen healthy runners walked and ran at speeds ranging from 0.8 km/h to 9.3 km/h. Twelve lower limb muscles' co-activation was calculated using the time-varying multi-muscle co-activation function (TMCf) with global, flexor–extension, and rostro–caudal approaches. Spatiotemporal and kinematic parameters were also measured. We found that TMCf, spatiotemporal, and kinematic parameters were significantly affected by gait speed for all approaches. Significant differences were observed in the main parameters of each co-activation approach and in the spatiotemporal and kinematic parameters at the transition between walking and running. In particular, significant differences were observed in the global co-activation (CIglob, main effect F(1,17) = 641.04, p < 0.001; at the transition p < 0.001), the stride length (main effect F(1,17) = 253.03, p < 0.001; at the transition p < 0.001), the stride frequency (main effect F(1,17) = 714.22, p < 0.001; at the transition p < 0.001) and the Center of Mass displacement in the vertical (CoMy, main effect F(1,17) = 426.2, p < 0.001; at the transition p < 0.001) and medial–lateral (CoMz, main effect F(1,17) = 120.29 p < 0.001; at the transition p < 0.001) directions. Regarding the correlation analysis, the CoMy was positively correlated with a higher CIglob (r = 0.88, p < 0.001) and negatively correlated with Full Width at Half Maximum (FWHMglob, r = −0.83, p < 0.001), whereas the CoMz was positively correlated with the global Center of Activity (CoAglob, r = 0.97, p < 0.001). Positive and negative strong correlations were found between global co-activation parameters and center of mass displacements, as well as some spatiotemporal parameters, regardless of gait speed. Our findings suggest that walking and running have different co-activation patterns and kinematic characteristics, with the whole-limb stiffness exerted more synchronously and stably during running. The co-activation indexes and kinematic parameters could be the result of global co-activation, which is a sensory-control integration process used by the CNS to deal with more demanding and potentially unstable tasks like running. [ABSTRACT FROM AUTHOR]