A nonlinearities inverse distance weighting spatial interpolation approach applied to the surface electromyography signal

Spatial interpolation of a surface electromyography (sEMG) signal from a set of signals recorded from a multi-electrode array is a challenge in biomedical signal processing. Consequently, it could be useful to increase the electrodes' density in detecting the skeletal muscles' motor units under detection's vacancy. This paper used two types of spatial interpolation methods for estimation: Inverse distance weighted (IDW) and Kriging. Furthermore, a new technique is proposed using a modified nonlinearity formula based on IDW. A set of EMG signals recorded from the noninvasive multi-electrode grid from different types of subjects, sex, age, and type of muscles have been studied when muscles are under regular tension activity. A goodness of fit measure (R2) is used to evaluate the proposed technique. The interpolated signals are compared with the actual signals; the Goodness of fit measure's value is almost 99%, with a processing time of 100msec. The resulting technique is shown to be of high accuracy and matching of spatial interpolated signals to actual signals compared with IDW and Kriging techniques.

Function, strength, and muscle activation of the shoulder complex in Crossfit practitioners with and without pain: a cross-sectional observational study

Background The shoulder joint is the most commonly injured joint in CrossFit practitioners, because of the high intensity and loads associated with this sport. Despite the large number of clinical cases, there is a shortage of studies that investigate influence of biomechanical aspects of upper limbs' injuries on CrossFit practitioners. This study hypothesized that there would be a difference in function, strength, and muscle activation between Crossfit practitioners with and without shoulder pain. Methods We divided 79 Crossfit practitioners into two groups according to whether they reported pain (n = 29) or no pain (n = 50) in the shoulder during Crossfit training. Muscle function, strength, and activation were assessed using the Disability Arm, Shoulder and Hand function questionnaire, Upper Quarter Y Balance Test and Closed Kinetic Chain Upper Extremity Stability Test shoulder tests, isometric muscle strength assessment by manual dynamometry and muscle activation by surface electromyography and pain report. Results The function based on questionnaire was associated with pain (p = 0.004). We observed a statistically significant difference between the two groups only in the surface electromyography activity of the lower trapezius, and in the variables of shoulder pain and function (p = 0.038). Conclusion Crossfit practitioners with shoulder pain occurring during training showed good function and stability of the shoulder joint, but there was a reduction in the activation of stabilizing muscles, especially the lower trapezius. Trial registration Registro Brasileiro de Ensaios Clinico (Brasilian National Registry) with the ID: RBR-2gycyv.

Light Sleep Detection based on Surface Electromyography Signals for Nap Monitoring

This paper proposes the development of automatic sleep stage detection by using physiological signals. We aim to develop an application to assist drivers after drowsiness or fatigue detection by a commercial driver vigilance system. The proposed method used a low-cost surface electromyography (EMG) device for sleep stage detection. We investigate skeletal muscle location and EMG features from sleep stage 2 to provide an EMG-based nap monitoring system. The results showed that using only one channel of a bipolar EMG signal from an upper trapezius muscle with median power frequency can achieve 84% accuracy. We implement a MyoWare muscle sensor into the proposed nap monitoring device. The results showed that the proposed system is feasible for detecting sleep stages and waking up the napper. A combination of EMG and electroencephalogram (EEG) signals might be yield a high system performance for nap monitoring and alarm system. We will prototype a portable device to connect the application to a smartphone and test with a target group, such as truck drivers and physicians.

Effect of the kinesio taping application method for the activation and strength of gastrocnemius muscles in healthy adults

The objective of this study was to verify the effect of kinesio-taping (KT) application of the origin for muscle insertion (O>I) and insertion for muscle origin (I>O) in healthy participants, through surface electromyography and peak torque of gastrocnemius muscles. A total of 69 participants with an average age of 22.9±5.2 years were evaluated, 41 women with an average age of 22.0±5.1 years, BMI 25.1±4.6 kg/m2, and 28 men with an average age of 22.0±5.1 years, BMI 23.1±3.3 kg/m2, randomized under three conditions: O>I, I>O and no KT (control), repeated three times with five-minute rest between sessions. The peak torque of the gastrocnemius lateral (GML) and medial (GMM) muscles was evaluated at speeds 30º/s 60º/s 120º/s and muscle activity through surface electromyography. Repeated-measurement ANOVA showed effect only on the variable speed (F=767,1; p<0.001) and the variables condition (F=0.010; p=0.990) and interaction (F=0.199; p=0.892) were not significant. In electromyography, Root mean Square (RMS) did not differ in the conditions evaluated, presenting standard behavior without significant differences. The KT application regardless of being O>I or I>O muscular, did not alter the muscle recruitment or contribute to the increase in peak torque performance during the three speeds.

Towards Integration of Domain Knowledge-Guided Feature Engineering and Deep Feature Learning in Surface Electromyography-Based Hand Movement Recognition

As a machine-learning-driven decision-making problem, the surface electromyography (sEMG)-based hand movement recognition is one of the key issues in robust control of noninvasive neural interfaces such as myoelectric prosthesis and rehabilitation robot. Despite the recent success in sEMG-based hand movement recognition using end-to-end deep feature learning technologies based on deep learning models, the performance of today’s sEMG-based hand movement recognition system is still limited by the noisy, random, and nonstationary nature of sEMG signals and researchers have come up with a number of methods that improve sEMG-based hand movement via feature engineering. Aiming at achieving higher sEMG-based hand movement recognition accuracies while enabling a trade-off between performance and computational complexity, this study proposed a progressive fusion network (PFNet) framework, which improves sEMG-based hand movement recognition via integration of domain knowledge-guided feature engineering and deep feature learning. In particular, it learns high-level feature representations from raw sEMG signals and engineered time-frequency domain features via a feature learning network and a domain knowledge network, respectively, and then employs a 3-stage progressive fusion strategy to progressively fuse the two networks together and obtain the final decisions. Extensive experiments were conducted on five sEMG datasets to evaluate our proposed PFNet, and the experimental results showed that the proposed PFNet could achieve the average hand movement recognition accuracies of 87.8%, 85.4%, 68.3%, 71.7%, and 90.3% on the five datasets, respectively, which outperformed those achieved by the state of the arts.