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Delivering health care at home emerged as a key advancement to reduce healthcare costs and infection risks, as during the SARS-Cov2 pandemic. In particular, in motor training applications, wearable and portable devices can be employed for movement recognition and monitoring of the associated brain signals. This is one of the contexts where it is essential to minimize the monitoring setup and the amount of data to collect, process, and share. In this paper, we address this challenge for a monitoring system that includes high-dimensional EEG and EMG data for the classification of a specific type of hand movement. We fuse EEG and EMG into the magnitude squared coherence (MSC) signal, from which we extracted features using different algorithms (one from the authors) to solve binary classification problems. Finally, we propose a mapping-and-aggregation strategy to increase the interpretability of the machine learning results. The proposed approach provides very low mis-classification errors ( < 0.1 ), with very few and stable MSC features ( < 10 % of the initial set of available features). Furthermore, we identified a common pattern across algorithms and classification problems, i.e., the activation of the centro-parietal brain areas and arm's muscles in 8-80 Hz frequency band, in line with previous literature. Thus, this study represents a step forward to the minimization of a reliable EEG-EMG setup to enable gesture recognition. [ABSTRACT FROM AUTHOR]

Delivering health care at home emerged as a key advancement to reduce healthcare costs and infection risks, as during the SARS-Cov2 pandemic. In particular, in motor training applications, wearable and portable devices can be employed for movement recognition and monitoring of the associated brain signals. This is one of the contexts where it is essential to minimize the monitoring setup and the amount of data to collect, process, and share. In this paper, we address this challenge for a monitoring system that includes high-dimensional EEG and EMG data for the classification of a specific type of hand movement. We fuse EEG and EMG into the magnitude squared coherence (MSC) signal, from which we extracted features using different algorithms (one from the authors) to solve binary classification problems. Finally, we propose a mapping-and-aggregation strategy to increase the interpretability of the machine learning results. The proposed approach provides very low mis-classification errors ( < 0.1 ), with very few and stable MSC features ( < 10 % of the initial set of available features). Furthermore, we identified a common pattern across algorithms and classification problems, i.e., the activation of the centro-parietal brain areas and arm's muscles in 8-80 Hz frequency band, in line with previous literature. Thus, this study represents a step forward to the minimization of a reliable EEG-EMG setup to enable gesture recognition. [ABSTRACT FROM AUTHOR]

Biomimetic robots yearn for compliant actuators that are comparable to biological muscle in both functions and structural properties. For that, electrostatic actuators have been developed to imitate bio-muscle in features of fast response, high power, energy-efficiency, etc. However, those actuators typically lack impact damping performance, making them vulnerable and unstable in real applications. Here, we present auxetic electrostatic actuators that address this issue and demonstrate muscle-like performance by using elastomer-enhanced auxetics and electrostatic zipping mechanism. The proposed actuators contract linearly on applied voltage, producing large actuation strength (15 N) and contraction ratio (59%). Fabricated from readily available materials, our prototypes can quickly attenuate vibrations caused by impacts and absorb shock energy in 0.3 s. Furthermore, leveraging their 2-dimensional working mode and self-locking mechanism, a stiffness-changing muscle for a robotic arm and an active tensegrity device exemplify the potential applications of auxetic electrostatic actuators to a wide range of bionic robots. Electrostatic actuators can imitate muscle features, but they are limited for real applications. Here, the authors show auxetic electrostatic actuators, using elastomer-enhanced auxetics and electrostatic zipping mechanism, that demonstrate muscle-like performance. [ABSTRACT FROM AUTHOR]

The effects of IT and R.I.C.E. treatment on arm muscle performance in overhead athletes with elbow pain (EP) have been partially validated. However, there is a lack of research evidence regarding the efficacy of these two methods on arm muscle performance among swimmers with EP. The aim of this study was to investigate the trends and differences in the effects of IT and R.I.C.E. treatment on arm muscle performance among swimmers with EP. The main outcomes were the time effects and group effects of interventions on muscle voluntary contraction (MVC). Sixty elite freestyle swimmers from Tianjin, China, voluntarily participated in the study and completed a 10-week intervention program. Swimmers with EP in the IT group showed a positive trend in MVC, with an approximately 2% increase, whereas the MVC of subjects in the R.I.C.E. treatment group and control group decreased by approximately 4% and 5%, respectively. In comparison, the effects of the IT intervention on the MVC of the triceps and brachioradialis muscles in swimmers with EP were significant (p = 0.042 < 0.05, p = 0.027 < 0.05). The mean MVC value of the IT group (0.60) was greater than that of the other two groups (0.51, 0.50). IT has a beneficial impact on the MVC performance of the triceps and brachioradialis muscles in swimmers with EP. It is recommended that professionals consider incorporating IT into regular training routines to mitigate the risk of EP issues. Future research should examine the effectiveness of both interventions on hand-grip strength and completion time in 50-m freestyle swim drills in order for swimmers with EP to return to this sport. [ABSTRACT FROM AUTHOR]

Functional Electrical Stimulation (FES) has demonstrated potential in clinical applications, but determining the optimal electrical current to stimulate muscles remains challenging due to the intricate coordination of various muscle groups during human movement. In this study, we introduce a novel approach to model and control human arm planar reaching movements. In terms of the model, a comprehensive human upper limb model is developed, taking into account the double-link structure, six muscles, and the connection points between muscles and the skeletal system. Regarding the control, a comprehensive sensorimotor control model emulating neural activities for human arm planar reaching movements is proposed. The control model effectively incorporates the imprecise nature of human visual sensory feedback for arm endpoint positioning and emulates the neural activities to determine appropriate stimulation levels for each of the six constituent muscles, inducing muscle contractions and guiding the skeletal systems to the target positions. The effectiveness of the proposed controller is demonstrated via numerical simulation experiments. Through comparisons with different controllers, it is shown that the proposed controller exhibits superior performance in tracking predefined motion trajectories and robustness in dealing with various skeletal muscle arm parameters. [ABSTRACT FROM AUTHOR]

In cases where mobility and joint function are impaired after implantation of a THA, weakening of hip movement in both extension/flexion and adduction/abduction may play a role due to shortening of the physiological lever arm of the hip muscles. Mechanical factors of influence include the lateral femoral offset, which affects the lever arm, and the antetorsion angle of the hip prosthesis, which affects the anterior femoral offset. This study aimed to investigate the effect of an altered antetorsion angle of the implant on the hip moments and gait patterns of the patient. For this study, 13 patients with a conventional stem on one side and a calcar-guided short stem implanted on the contralateral side were included. To determine the maximum hip moment, tests were performed on a dynamometer in extension/flexion and adduction/abduction in addition to gait analysis. As a control, a comparison was made with data from a reference group of 30 healthy subjects. Both implants showed similar symmetry indices. There was a significant difference between the implants for adduction moments (p < 0.001). The ratios between the directions of moments showed no significant differences. The joint function measured by isokinetic measurements and gait analysis remains comparable to the healthy control group after short stem arthroplasty, but shows slight changes after conventional stem arthroplasty. [ABSTRACT FROM AUTHOR]

Epidural electrical stimulation (EES) of lumbosacral sensorimotor circuits improves leg motor control in animals and humans with spinal cord injury (SCI). Upper-limb motor control involves similar circuits, located in the cervical spinal cord, suggesting that EES could also improve arm and hand movements after quadriplegia. However, the ability of cervical EES to selectively modulate specific upper-limb motor nuclei remains unclear. Here, we combined a computational model of the cervical spinal cord with experiments in macaque monkeys to explore the mechanisms of upper-limb motoneuron recruitment with EES and characterize the selectivity of cervical interfaces. We show that lateral electrodes produce a segmental recruitment of arm motoneurons mediated by the direct activation of sensory afferents, and that muscle responses to EES are modulated during movement. Intraoperative recordings suggested similar properties in humans at rest. These modelling and experimental results can be applied for the development of neurotechnologies designed for the improvement of arm and hand control in humans with quadriplegia. The efficacy of epidural electrical stimulation (EES) to engage arm muscles and improve movement after spinal cord injury is still unclear. Here, the authors investigated how EES can recruit upper-limb motor neurons by combining computational modelling with experiments in non-human primates. [ABSTRACT FROM AUTHOR]