A compact description of coordinated muscle activity is provided by the factorization of electromyographic (EMG) signals. With the use of this approach, it has consistently been shown that multimuscle activity during human locomotion can be accounted for by four to five modules, each one comprised of a basic pattern timed at a different phase of gait cycle and the weighting coefficients of synergistic muscle activations. These modules are flexible, in so far as the timing of patterns and the amplitude of weightings can change as a function of gait speed and mode. Here we consider the adjustments of the locomotor modules related to unstable walking conditions. We compared three different conditions, i.e., locomotion of healthy subjects on slippery ground (SL) and on narrow beam (NB) and of cerebellar ataxic (CA) patients on normal ground. Motor modules were computed from the EMG signals of 12 muscles of the right lower limb using non-negative matrix factorization. The unstable gait of SL, NB, and CA showed significant changes compared with controls in the stride length, stride width, range of angular motion, and trunk oscillations. In most subjects of all three unstable conditions, >70% of the overall variation of EMG waveforms was accounted for by four modules that were characterized by a widening of muscle activity patterns. This suggests that the nervous system adopts the strategy of prolonging the duration of basic muscle activity patterns to cope with unstable conditions resulting from either slippery ground, reduced support surface, or pathology.

The aim of this study was to determine which supervised machine learning (ML) algorithm can most accurately classify people with Parkinson’s disease (pwPD) from speed-matched healthy subjects (HS) based on a selected minimum set of IMU-derived gait features. Twenty-two gait features were extrapolated from the trunk acceleration patterns of 81 pwPD and 80 HS, including spatiotemporal, pelvic kinematics, and acceleration-derived gait stability indexes. After a three-level feature selection procedure, seven gait features were considered for implementing five ML algorithms: support vector machine (SVM), artificial neural network, decision trees (DT), random forest (RF), and K-nearest neighbors. Accuracy, precision, recall, and F1 score were calculated. SVM, DT, and RF showed the best classification performances, with prediction accuracy higher than 80% on the test set. The conceptual model of approaching ML that we proposed could reduce the risk of overrepresenting multicollinear gait features in the model, reducing the risk of overfitting in the test performances while fostering the explainability of the results.

Background: In order to reduce the risk of work-related musculoskeletal disorders (WMSDs) several methods have been developed, accepted by the international literature and used in the workplace. The purpose of this systematic review was to describe recent implementations of wearable sensors for quantitative instrumental-based biomechanical risk assessments in prevention of WMSDs. Methods: Articles written until 7 May 2018 were selected from PubMed, Scopus, Google Scholar and Web of Science using specific keywords. Results: Instrumental approaches based on inertial measurement units and sEMG sensors have been used for direct evaluations to classify lifting tasks into low and high risk categories. Wearable sensors have also been used for direct instrumental evaluations in handling of low loads at high frequency activities by using the local myoelectric manifestation of muscle fatigue estimation. In the field of the rating of standard methods, on-body wireless sensors network-based approaches for real-time ergonomic assessment in industrial manufacturing have been proposed. Conclusions: Few studies foresee the use of wearable technologies for biomechanical risk assessment although the requirement to obtain increasingly quantitative evaluations, the recent miniaturization process and the need to follow a constantly evolving manual handling scenario is prompting their use.

Several studies have demonstrated how cerebellar ataxia (CA) affects gait, resulting in deficits in multijoint coordination and stability. Nevertheless, how lesions of cerebellum influence the locomotor muscle pattern generation is still unclear. To better understand the effects of CA on locomotor output, here we investigated the idiosyncratic features of the spatiotemporal structure of leg muscle activity and impairments in the biomechanics of CA gait. To this end, we recorded the electromyographic (EMG) activity of 12 unilateral lower limb muscles and analyzed kinematic and kinetic parameters of 19 ataxic patients and 20 age-matched healthy subjects during overground walking. Neuromuscular control of gait in CA was characterized by a considerable widening of EMG bursts and significant temporal shifts in the center of activity due to overall enhanced muscle activation between late swing and mid-stance. Patients also demonstrated significant changes in the intersegmental coordination, an abnormal transient in the vertical ground reaction force and instability of limb loading at heel strike. The observed abnormalities in EMG patterns and foot loading correlated with the severity of pathology [International Cooperative Ataxia Rating Scale (ICARS), a clinical ataxia scale] and the changes in the biomechanical output. The findings provide new insights into the physiological role of cerebellum in optimizing the duration of muscle activity bursts and the control of appropriate foot loading during locomotion.

Background and Purpose. Radial shock-wave therapy (RSWT) is a pneumatically generated, low- to medium-energy type of shock-wave therapy. This single-blind, randomized, “less active similar therapy”-controlled study was performed to evaluate the effectiveness of RSWT for the management of calcific tendinitis of the shoulder. Subjects. Ninety patients with radiographically verified calcific tendinitis of the shoulder were tested. Methods. Subjects were randomly assigned to either a treatment group (n=45) or a control group (n=45). Pain and functional level were evaluated before and after treatment and at a 6-month follow-up. Radiographic modifications in calcifications were evaluated before and after treatment. Results. The treatment group displayed improvement in all of the parameters analyzed after treatment and at the 6-month follow-up. Calcifications disappeared completely in 86.6% of the subjects in the treatment group and partially in 13.4% of subjects; only 8.8% of the subjects in the control group displayed partially reduced calcifications, and none displayed a total disappearance. Discussion and Conclusion. The results suggest that the use of RSWT for the management of calcific tendinitis of the shoulder is safe and effective, leading to a significant reduction in pain and improvement of shoulder function after 4 weeks, without adverse effects.