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Temporomandibular disorders (TMD) have multifactorial and complex etiology. Regardless of their etiology, all those conditions may result in centrally mediated chronic muscle pain, myalgia, myofascial pain, myofibrotic contracture, myosistis, myospasm, headache and a variety of neck, shoulder, upper back and lower back pain. Biofeedback (BF) is one of methods that has been used for more than 50 years in rehabilitation to facilitate normal movement patterns after injuries. Some studies suggest that biofeedback may be an effective treatment option for patients with different muscle disorders. The aim of this study was to evaluate the efficiency of biofeedback in masticatory muscle activity management in the light of current medical literature. The authors followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines for this systematic review. The authors searched the MEDLINE, Scopus, Web of Science, CINAHL databases to identify relevant publications. Finally 10 papers were included. Most of the selected studies showed a significant correlation between biofeedback usage and reduction of masticatory muscle activity. By analyzing qualified studies, it can be concluded that biofeedback can be an effective tool in masticatory muscle activity management.

Objective This study aimed to evaluate if sustained trapezius muscle activity predicts neck and shoulder pain over a 2.5-year period. Methods Forty young adults (15 hairdressers, 14 electricians, 5 students and 6 with various work) were followed during their first years of working life. Self-reported neck and shoulder pain during the last four weeks was assessed seven times over the observational period. Upper-trapezius muscle activity was measured during a full working day by bilateral surface electromyography (EMG) at baseline (winter 2006/7). Sustained trapezius muscle activity was defined as continuous muscle activity with amplitude >0.5% EMG max lasting >4 minutes. The relative time of sustained muscle activity during the working day was calculated and further classified into low (0—29%), moderate (30—49%) and high (50—100%) level groups. Results Generalized estimating equations (GEE), adjusted for time, gender, mechanical workload, control-over-work intensity, physical activity, tobacco use, and prior neck and shoulder pain, showed that participants with a high level of sustained muscle activity had a rate of neck and shoulder pain three times higher than the low level group during a 2.5-year period. The association was strongest at the same time and shortly after the EMG measurement, indicating a time-lag of ≤6 months. Conclusion The results support the hypothesis that sustained trapezius muscle activity is associated with neck and shoulder pain. This association was strongest analyzing cross-sectional and short-term effects.

(1) Background: In alpine skiing, senior athletes and especially women have a high risk of knee injury. This may also be related to muscular fatigue (MF) of the knee-stabilizing thigh muscles. This study investigates both the evolution of muscle activity (MA) and of MF of the thighs throughout an entire skiing day. (2) Methods: n = 38 female recreational skiers over 40 years of age performed four specific skiing tasks (plough turns, V-steps uphill, turns with short, and middle radii) at specific times, while freely skiing the rest of the day. Surface EMG of the thigh muscle groups (quadriceps and hamstrings) was measured using special wearables (EMG pants). Apart from standard muscle activity parameters, the EMG data were also processed in the frequency domain to calculate the mean frequency and its shift over the day as a metric of muscle fatigue. (3) Results: The EMG pants showed reliable signal quality over the entire day, with BMI not impacting this. MF increased during skiing before and for both muscle groups significantly (p < 0.006) during lunch. MF, however, was not reflected in the quadriceps–hamstrings ratio. The plough manoeuvre seems to require significantly (p < 0.003) more muscle dynamics than the three other tasks. (4) Conclusion: MF may be quantified over an entire skiing day and thus fatigue information could be given to the skier. This is of major importance for skiers at the beginner level dominantly performing plough turns. Crucial for all skiers: There is no regenerative effect of a 45-min lunch break.

Motion recognition (MR)‐based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human‐computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non‐intrusive muscle‐sensing wearable device, that in conjunction with machine learning, enables motion‐control‐based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16‐channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower‐limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle‐sensing‐based somatosensory interaction, using the proposed wearable device, for enabling the real‐time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography‐based methods for achieving accurate human motion capture, which can further broaden the applications of motion‐interactive wearable devices for the coming metaverse age. A non‐invasive wearable device has been developed, incorporating a soft pressure sensor array that enables simultaneous detection of muscle deformation and mechanomyography. By leveraging machine learning techniques, this device demonstrates its ability to recognize a minimum of ten distinct lower limb motions, showcasing significant potential for future metaverse applications.

Maximal isometric finger dead-hangs are used in rock climbing to strengthen finger flexors. Although various grip positions are often used when performing finger dead-hangs, little is known regarding how these grip positions can affect forearm muscle activity. Understanding how forearm muscles are recruited during dead-hangs could help foreseeing the potential for training of different grip positions. The aim of the present study was to explore the training applications of the various grip positions by comparing the activity of forearm muscles during maximal dead-hangs in rock climbers. Twenty-five climbers performed maximal dead-hangs in three climbing-specific grip positions: CRIMP, SLOPE, and SLOPER. We recorded the maximal loads used and the sEMG of the flexor digitorum profundus (FDP), the flexor digitorum superficialis (FDS), the flexor carpi radialis (FCR), and the extensor digitorum communis (EDC). Individual and global (sum of all muscles) root mean square (RMS) and neuromuscular efficiency (NME) values were computed. Repeated measures analysis were performed to assess grip differences (  < 0.05). SLOPER showed the largest maximal load values among the three grip positions (  < 0.001,  ≥ 2.772). Greater global (  ≤ 0.044,  ≥ 0.268), FDS (  ≤ 0.005,  ≥ 0.277), and FCR (  < 0.001,  ≥ 1.049) activity was observed for the SLOPER compared to CRIMP and SLOPE, while EDC (  ≤ 0.005,  ≥ 0.505) showed lower activity in the SLOPER compared to the other two grip positions. SLOPER presented the highest global (  < 0.001,  ≥ 0.629), FDP (  < 0.001, ≥ 0.777), FDS (only CRIMP SLOPER: < 0.001, = 0.140), and EDC NME ( < 0.001, ≥ 1.194). The CRIMP showed greater FDS activity ( = 0.001, = 0.386) and lower NME ( = 0.003, = 0.125) compared to SLOPE. These results revealed that, under maximum intensity conditions, SLOPER could stimulate the FDS and FCR better than the other grip positions at the expense of using greater loads. Similarly, maximum CRIMP dead-hang could better stimulate the FDS than the SLOPE, even when using similar loads.

This study aimed to demonstrate the usefulness of digital image correlation (DIC) for analysing the activity and heterogeneity of the temporalis muscle based on its displacements. While the standard measure for superficial muscles traditionally relies on muscle force determined via electrical biopotentials and numerical simulations, this paper presents results indicating that a kinematic parameter (muscle displacement) can effectively assess muscle function. The temporalis muscle was selected as the primary object of investigation, with measurements taken on both the working and non-working sides during unilateral chewing of three selected foods. The study group consisted of 8 healthy adult males aged 36.6 ± 9.4 years (BMI: 24.7 ± 0.8 kg/m ). A Dantec Q400 non-contact measurement system based on DIC was utilised. Reference images were captured at the mandible's physiological resting position. The technique enabled continuous full-field mapping across the skin surface, while the final analysis was precisely confined to the area corresponding to the muscle fibres. The study demonstrated that the temporalis muscle's functioning is complex and structurally heterogeneous. Specifically, the analysis revealed two distinct kinematic patterns: an asymmetric cycle (AC) in the anterior and middle parts responsible for force generation, and a fluctuating cycle (FC) in the posterior part serving as a dynamic stabiliser for the temporomandibular joint (TMJ). Furthermore, muscle kinematics were significantly modulated by both the chewing side and food texture. Spatial activation patterns suggest a task-specific biomechanical strategy, where the working side presses the mandible, while the non-working side stabilises it against excessive rotation. Ultimately, the results confirm that skin surface displacement can serve as a reliable, high-resolution measure of temporalis muscle activity.

Sit-to-stand (STS) assistance should not only reduce effort but also preserve or shape neuromuscular activity patterns. We propose a data-driven control strategy for an assistive chair with two degrees of freedom (vertical and horizontal seat motion) to infer desired multi-muscle activation during STS. The chair is parameterized by four binary variables (fast/slow vertical and horizontal velocities, and early/late onset timing for each axis), yielding 16 control combinations. Surface EMG from eight lower-limb muscles was collected from six healthy adult males across all control combinations (10 trials per condition). We extracted hundred-dimensional EMG features by segmenting STS into four phases and computing summary statistics per muscle and phase. Four L 1 -regularized logistic regression classifiers were trained to infer each control variable from EMG features, enabling a classifier-based statistical mapping from target EMG features to chair control parameters. The classifiers achieved F-scores of 0.96 and 0.99 for forward and upward speed, and 0.89 and 0.82 for forward and upward timing, respectively. In an offline evaluation, the estimated control parameters inferred EMG feature patterns significantly closer to the target than non-target parameter combinations. These results suggest that low-DoF seat motion can be used to modulate higher-dimensional muscle activation patterns during STS, providing a basis for future real-time and individualized assistive control.

Muscle functional MRI (mfMRI) is an imaging technique that assess muscles’ activity, exploiting a shift in the T2-relaxation time between resting and active state on muscles. It is accompanied by the use of electromyography (EMG) to have a better understanding of the muscle electrophysiology; however, a technique merging MRI and EMG information has not been defined yet. In this paper, we present an anatomical and quantitative evaluation of a method our group recently introduced to quantify its validity in terms of muscle pattern estimation for four subjects during four isometric tasks. Muscle activation pattern are estimated using a resistive network to model the morphology in the MRI. An inverse problem is solved from sEMG data to assess muscle activation. The results have been validated with a comparison with physiological information and with the fitting on the electrodes space. On average, over 90% of the input sEMG information was able to be explained with the estimated muscle patterns. There is a match with anatomical information, even if a strong subjectivity is observed among subjects. With this paper we want to proof the method’s validity showing its potential in diagnostic and rehabilitation fields.

Today Electromyography (EMG) and accelerometer (MEMS) based signals can be used in the clinical diagnosis of physical states of muscle activities such as fatigue, muscle weakness, pain, and tremors and in external or wearable robotic exoskeletal systems used in rehabilitation areas. During the recording of these signals taken from the skin surface through non-invasive processes, analysis of the signal becomes difficult due to the electrodes attached to the skin not fully contacting, involuntary body movements, and noises from peripheral muscles. In addition, parameters such as age and skin structure of the subjects can also affect the signal. Considering these negative factors, a new adaptive method based on Extended Kalman Filtering (EKF) model for more effective filtering of the muscle signals based on both EMG and MEMS is proposed in this study. Moreover, the accuracy of the parametric values determined by the filter automatically according to the most effective time and frequency features that represent noisy and filtered signals was determined by different machine learning and classification algorithms. It was verified that the filter performs adaptive filtering with 100% effectiveness with Linear Discriminant.

Estimation of muscle activity is very important as it can be a cue to assess a person’s movements and intentions. If muscle activity states can be obtained through non-contact measurement, through visual measurement systems, for example, muscle activity will provide data support and help for various study fields. In the present paper, we propose a method to predict human muscle activity from skin surface strain. This requires us to obtain a 3D reconstruction model with a high relative accuracy. The problem is that reconstruction errors due to noise on raw data generated in a visual measurement system are inevitable. In particular, the independent noise between each frame on the time series makes it difficult to accurately track the motion. In order to obtain more precise information about the human skin surface, we propose a method that introduces a temporal constraint in the non-rigid registration process. We can achieve more accurate tracking of shape and motion by constraining the point cloud motion over the time series. Using surface strain as input, we build a multilayer perceptron artificial neural network for inferring muscle activity. In the present paper, we investigate simple lower limb movements to train the network. As a result, we successfully achieve the estimation of muscle activity via surface strain.