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• Development of a Wearable Hip Joint Power-Assisted Exoskeleton. • A new auxiliary force model based on the biomechanical characteristics of the hip joint is proposed. • An evaluation system for muscle assistance effectiveness and joint synergy is established. • A novel joint synergy evaluation method based on Non-negative Matrix Factorization (NMF) is proposed. With a demand for exoskeletons with high human–machine compatibility, superior assistive performance, and excellent wearability, wearable exoskeletons have experienced exponential growth in recent years. This paper develops a wearable hip joint power-assisted exoskeleton based on human lower limb parameters and hip joint physiological characteristics. A model for expected assistance force was designed considering ergonomics. To evaluate the exoskeleton's performance, this study presents a Non-negative Matrix Factorization (NMF) method for analyzing joint synergy, and combined with the surface electromyography (sEMG) analysis of thigh muscle groups, the assisted exoskeleton's performance was assessed in terms of muscle assistance and joint synergy. Results from independent sample t-tests indicate no significant difference in joint synergy similarity before and after exoskeleton use, but a substantial reduction in muscle activation levels, particularly in the rectus femoris. Additionally, the mean power frequency (MPF) and median frequency (MF) slopes of muscle activation showed a notable decline, suggesting the exoskeleton effectively assists lower limb muscles during Power-ON. [ABSTRACT FROM AUTHOR]

• A new metric, synergy index, is introduced to analyze hip, knee, and ankle movements. • Using this metric, we obtained a functional interpretation of lower limb joint movements in five different walking tasks. • This study reveals distinct muscle coordination patterns in Heel and Toe walking tasks. • The k-means method is employed to extract commonalities in joint movements across different walking tasks. • We address synergy weight variations within the common muscle synergy across different walking tasks. The limb is the most common site for sports injuries. The objective of this study is to validate lower limb muscle synergy as a biomarker for movement quality. The proposed method comprises the following steps: (i) extraction of muscle synergies using nonnegative matrix factorization, (ii) estimation of hip, knee, and ankle joint movements from synergy weights, (iii) identification of common muscle synergies across five walking tasks using k-means clustering and (iv) conducting cluster-wise analysis of muscle contraction intensity during lower limb joint movements using synergy weights. In this study, we used surface Electromyography signals from the Lencioni et al. 2019 dataset recorded during normal walking, heel walking, toe walking, stair ascending and stair descending tasks involving fifty heterogeneous subjects. For the first time in the literature, we performed a functional interpretation of lower limb joint movements during these five walking tasks based on synergy weights and compared the results with the multiple muscle co-activation index (p < 0.005). Further, the clustering results revealed similarities in muscle synergies among normal, toe, and stair walking tasks (p < 0.05), while highlighting the distinct synergy pattern of the heel walking task. Notably, this study also explored the variations in muscle contraction intensities across different walking tasks that share common muscle synergies. Additionally, we extensively analysed the impact of age-gender factors on stride length and walking speed, uncovering small but discernible gender-specific differences in muscle synergies during different walking tasks. Hence, muscle synergy shows promise as a potential biomarker for the functional assessment of lower limb movements. [ABSTRACT FROM AUTHOR]

• The paper involved a large number of experimental subjects. • The dimensionality reduction of muscle synergy was based on the physiological knowledge of the human body. • The isometric processing was able to accommodate different walking speeds. • The paper analyzed each subject and provided reference suggestions based on the processing results. Surface Electromyography (sEMG) is an important source for obtaining human motion information, the joint angle of the human body can be predicted. Therefore, this paper proposed a method for predicting the knee joint angle of the lower limb based on sEMG signals. Firstly, the muscles with the strongest correlation during the walking state were selected by using muscle synergy. Then the root mean square (RMS) of the selected muscles and their gradients were used as features for predicting the knee joint angle. Finally, regression models were used to predict the knee joint angle. We chose to use publicly available dataset from the Exoskeleton and Prosthetic Intelligent Control (EPIC) laboratory to validate the proposed method in this paper, and the predicted angle can provide real-time reference for the control process of the lower limb exoskeleton and other devices. [ABSTRACT FROM AUTHOR]

This study proposes a method to improve the measurement stability of sit-to-stand (STS) motion, one of the most important motions for human mobility. STS motion is complex and uses numerous muscles ranging from the feet to the torso. Electromyography (EMG) is often used to analyze STS motion by precisely measuring the activity of the contributing muscles. However, such measurements depend strongly on the measurement conditions, such as electrode attachment conditions, and variations in the contributing muscles and initial poses. We examined the variability caused by these conditions and explored a method to stabilize STS measurements by EMG. The proposed method adjusts measurements using data aggregation and muscle synergy computation, which leads to better comparisons or integration of motion measurements with two or more motion strategies or motions acquired over multiple EMG electrode reinstallations. The experimental results show that the proposed method improves the similarity of measurements under different conditions. [ABSTRACT FROM AUTHOR]

[Display omitted] • Extracting muscle synergy during the handwritten signature for stressed subjects. • Classification of muscles synergies to identification the extracted data related to the genuine and forged signatures with SVM classification. • A verification system designed that does not depend on the stress of people in signing. Biometric authentication systems, in terms of using the biometric characteristic, and because these indicators are resistant to the passage of time, and environmental, and physical changes of people, in most different situations from the environment or changes in emotional factors of person, perform identity verification with optimal accuracy. In the signature verification systems, people may experience stress caused by environmental or internal factors when registering a signature, this stress can affect the performance of the signature verification system in terms of accuracy and stability. We try to study the performance of a signature verification system based on a biometric characteristic (muscle synergy patterns) for people who are stressed while signing. For this purpose, electromyography (EMG) signals were recorded from hand and arm muscles of people while signing. Then, using Non-Negative Matrix Factorization (NMF) method to extracted muscle synergies from EMG signals after pre-processing. The extracted synergy patterns are classified into genuine and forgery classes by Support Vector Machine (SVM) classifier. Furthermore, the confirmation of the authentication of stressed people was studied using this method. Finally, the results obtained were compared to the results obtained from the signature verification system for unstressed people and using K-means classifier method. [ABSTRACT FROM AUTHOR]

Objectives: When compared to seated, the standing position allows the production of higher power outputs during intense cycling. We hypothesized that muscle coordination could explain this advantage. To test this hypothesis, we assessed muscle activity over a wide range of power outputs for both seated and standing cycling positions.Design: Nine lower limb muscle activities from seventeen untrained volunteers were recorded during cycling sequences performed in the seated and the standing positions at power outputs ranging from ∼100 to 700W at 90±5 revolutions-per-minute (RPM).Methods: Integrated electromyography activity (iEMG), temporal patterns of the EMGs, and muscle synergies were analyzed.Results: Muscle activity was underlain by four muscle synergies in both positions. Muscle synergies were similar in the two positions (Pearson's r=0.929±0.125). The activation patterns of knee and ankle extensor muscles and their associated synergies had different timings in the two positions (differences of ∼2-10% of cycle). No major timing changes were observed with power output (<2% of cycle). Differences in iEMG between the two positions depended strongly on power output in all but the calf muscle (medial gastrocnemius).Conclusions: The number and structure of the muscle synergies play a minor role in the advantage of using the standing position when cycling at high power-outputs. However, the standing position is favorable in terms of iEMG at power outputs ≳500-600W due to position-dependent modulations of muscle activation levels. These data are important for understanding the determinants of the seat-stand transition in cycling. [ABSTRACT FROM AUTHOR]

The Biomedical Signals have been studied for developing human control systems to improving the quality of life. The EMG signal is one of the main types of biomedical signals. It is a convoluted signal. This signal (EMG signal) controlled by the Central nervous system (CNS). It has been a long time expected that the human central nervous system (CNS) uses flexible combinations of some muscles synergy (MS) to solve and control redundant movements. Synergy muscles activities are different in a single muscle. In the concept of Synergy muscle, the CNS does not directly control the activation of a large number of muscles. There are two main movements can help CNS to shape the synergy. The automatic body response and the voluntary actions. These activities remain not too bright. Some studies support the hypothesis that the automatic body responses could be used as a reference to familiarize the voluntary efforts. It has been validating by analyzing the human voluntary movement and the automatic mechanical motions from the muscle synergy. Based on the validation, there was a proposition that the automatic synergy motion may express some features which could support the CNS to shape the voluntary synergy motion using the nonnegative matrix factorization (NMF). Thus the target of the presenting work is to analyses the human movements from the muscle synergy to help CNS shapes the synergy movement by suggestion using the concatenated non-negative matrix factorization (CNMF) method and the pattern recognition method. Then compare the two results and see if that help CNS to shape the synergy movements and which method has more accuracy. [ABSTRACT FROM AUTHOR]

• A muscle synergy-based hierarchical pattern recognition strategy was proposed to classify gestures and force levels. • The muscle synergy pattern varied significantly at different force levels. • The muscle synergy feature showed a great advantage for gesture and force level recognition. Multifunctional intelligent prosthetics (IPs) require precise control of the movement under users' intentions where force interaction between the prosthetics and the environment must be considered. Surface electromyography (sEMG) is a promising input for IP control as it reflects users' motor intention on both movement and force levels. However, simultaneously decoding the movements and their force levels based on sEMG has not yet been deeply studied. This paper proposed a sEMG-based hierarchical pattern recognition strategy that enables decoding of gesture types and force levels simultaneously. The hierarchical strategy consisted of two layers of classification (gesture and force level classification) based on two sEMG features: muscle synergy (MS) and root mean square (RMS). The strategy was tested on 13 healthy participants with 6 wireless sEMG electrodes. Resutls showed that the gesture classification with MS and RMS achieved similar accuracies of 98.78% and 98.12, respectively, while the force level classification obtained a significantly higher accuracy of 94.04% with MS compard to 78.94% with RMS. The result of correlation analysis was that the MS correlation coeficients within group were significantly larger than those between groups, which validated that muscle activities at different force levels varied with not only increasing or decreasing the activation intensity but also changing muscle synergy patterns. Finally, the hierarchical strategy achieved an accuracy of 93.4% with MS as the input of the two-layer classification. It was concluded that the hierarchical stragety was promising for gesture and force level recognition, especially with MS featues, which was sensitive to force level recognition. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this study was to evaluate intramuscular muscle activity from a latent myofascial trigger point (MTP) in a synergistic muscle during isometric muscle contraction. Intramuscular activity was recorded with an intramuscular electromyographic (EMG) needle inserted into a latent MTP or a non-MTP in the upper trapezius at rest and during isometric shoulder abduction at 90° performed at 25% of maximum voluntary contraction in 15 healthy subjects. Surface EMG activities were recorded from the middle deltoid muscle and the upper, middle, and lower parts of the trapezius muscle. Maximal pain intensity and referred pain induced by EMG needle insertion and maximal pain intensity during contraction were recorded on a visual analog scale. The results showed that higher visual analog scale scores were observed following needle insertion and during muscle contraction for latent MTPs than non-MTPs (P < .01). The intramuscular EMG activity in the upper trapezius muscle was significantly higher at rest and during shoulder abduction at latent MTPs compared with non-MTPs (P < .001). This study provides evidence that latent MTPs are associated with increased intramuscular, but not surface, EMG amplitude of synergist activation. The increased amplitude of synergistic muscle activation may result in incoherent muscle activation pattern of synergists inducing spatial development of new MTPs and the progress to active MTPs. Perspective: This article presents evidence of increased intramuscular, but not surface, muscle activity of latent MTPs during synergistic muscle activation. This incoherent muscle activation pattern may overload muscle fibers in synergists during muscle contraction and may contribute to spatial pain propagation. [Copyright &y& Elsevier]

[Display omitted] Intra-personal variability, which measures the difference between people's signatures, may be affected by various signing challenges. Considering the variety of physical conditions, it is almost impossible for persons to write their exact handwritten signature in the same way in several attempts. Also, the authentication system requires less complexity to respond quickly to real-time applications. This study attempts to confirm handwritten signatures by using hand muscle synergy as a biometric characteristic. To design the signature verification system, surface electromyography (EMG) signals from eight (arm and hand) muscles of the volunteers were recorded by surface EMG pads during the signing. Muscle synergy was extracted from EMG signals after preprocessing using the non-negative matrix factorization (NMF) method. Genuine and forgery data are then classified by the K-means classifier. The system achieves an equal error rate (EER) of 2.75 to identify the extracted data related to the genuine and forged signatures. [ABSTRACT FROM AUTHOR]

• Higher-order tensor decomposition can be utilised in synergy-based myoelectric control. • The novel consTD method is more effective than matrix factorisation in synergy extraction from EMG repetitive training dataset. • ConsTD method is comparable with NMF and SNMF in synergy-based myoelectric control. Muscle synergies have recently been utilised in myoelectric control systems. Thus far, all proposed synergy-based systems rely on matrix factorisation methods. However, this is limited in terms of task-dimensionality. Here, the potential application of higher-order tensor decomposition as a framework for proportional myoelectric control is demonstrated. A novel constrained Tucker decomposition (consTD) technique of synergy extraction is proposed for synergy-based myoelectric control model and compared with state-of-the-art matrix factorisation models. The extracted synergies were used to estimate control signals for the wrist's Degree of Freedom (DoF) through direct projection. The consTD model was able to estimate the control signals for each DoF by utilising all data in one 3rd-order tensor. This is contrast with matrix factorisation models where data are segmented for each DoF and then the synergies often have to be realigned. Moreover, the consTD method offers more information by providing additional shared synergies, unlike matrix factorisation methods. The extracted control signals were fed to a ridge regression to estimate the wrist's kinematics based on real glove data. The Coefficient of Determination (R 2) for the reconstructed wrist position showed that the proposed consTD was higher than matrix factorisation methods. In sum, this study provides the first proof of concept for the use of higher-order tensor decomposition in proportional myoelectric control and it highlights the potential of tensors to provide an objective and direct approach to identify synergies. [ABSTRACT FROM AUTHOR]

An abstract of the article "Dimensional reduction in sensorimotor systems: a framework for understanding muscle coordination of posture," by Lena H. Ting is presented.