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Gait phase recognition systems based on surface electromyographic signals (EMGs) are crucial for developing advanced myoelectric control schemes that enhance the interaction between humans and lower limb assistive devices. However, machine learning models used in this context, such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), typically experience performance degradation when modeling the gait cycle with more than just stance and swing phases. This study introduces a generalized phasor-based feature extraction approach (PHASOR) that captures spatial myoelectric features to improve the performance of LDA and SVM in gait phase recognition. A publicly available dataset of 40 subjects was used to evaluate PHASOR against state-of-the-art feature sets in a five-phase gait recognition problem. Additionally, fully data-driven deep learning architectures, such as Rocket and Mini-Rocket, were included for comparison. The separability index (SI) and mean semi-principal axis (MSA) analyses showed mean SI and MSA metrics of 7.7 and 0.5, respectively, indicating the proposed approach's ability to effectively decode gait phases through EMG activity. The SVM classifier demonstrated the highest accuracy of 82% using a five-fold leave-one-trial-out testing approach, outperforming Rocket and Mini-Rocket. This study confirms that in gait phase recognition based on EMG signals, novel and efficient muscle synergy information feature extraction schemes, such as PHASOR, can compete with deep learning approaches that require greater processing time for feature extraction and classification. [ABSTRACT FROM AUTHOR]

This study delves into decoding hand gestures using surface electromyography (EMG) signals collected via a precision Myo-armband sensor, leveraging machine learning algorithms. The research entails rigorous data preprocessing to extract features and labels from raw EMG data. Following partitioning into training and testing sets, four traditional machine learning models are scrutinized for their efficacy in classifying finger movements across seven distinct gestures. The analysis includes meticulous parameter optimization and five-fold cross-validation to evaluate model performance. Among the models assessed, the Random Forest emerges as the top performer, consistently delivering superior precision, recall, and F1-score values across gesture classes, with ROC-AUC scores surpassing 99%. These findings underscore the Random Forest model as the optimal classifier for our EMG dataset, promising significant advancements in healthcare rehabilitation engineering and enhancing human–computer interaction technologies. [ABSTRACT FROM AUTHOR]

The objective of the article is to recognize users' emotions by classifying facial electromyographic (EMG) signals. A biomedical signal amplifier, equipped with eight active electrodes positioned in accordance with the Facial Action Coding System, was used to record the EMG signals. These signals were registered during a procedure where users acted out various emotions: joy, sadness, surprise, disgust, anger, fear, and neutral. Recordings were made for 16 users. The mean power of the EMG signals formed the feature set. We utilized these features to train and evaluate various classifiers. In the subject-dependent model, the average classification accuracies were 96.3% for KNN, 94.9% for SVM with a linear kernel, 94.6% for SVM with a cubic kernel, and 93.8% for LDA. In the subject-independent model, the classification results varied depending on the tested user, ranging from 91.4% to 48.6% for the KNN classifier, with an average accuracy of 67.5%. The SVM with a cubic kernel performed slightly worse, achieving an average accuracy of 59.1%, followed by the SVM with a linear kernel at 53.9%, and the LDA classifier at 41.2%. Additionally, the study identified the most effective electrodes for distinguishing between pairs of emotions. [ABSTRACT FROM AUTHOR]

Functional electrical stimulation (FES) is a rehabilitation and assistive technique used for stroke survivors. FES systems mainly consist of sensors, a control algorithm, and a stimulation unit. However, there is a critical need to reassess sensing and control techniques in FES systems to enhance their efficiency. This SLR was carried out following the PRISMA 2020 statement. Four databases (PubMed, Scopus, Web of Science, Wiley Online Library) from 2010 to 2024 were searched using terms related to sensing and control strategies in FES systems. A total of 322 articles were chosen in the first stage, while only 60 of them remained after the final filtering stage. This systematic review mainly focused on sensor techniques and control strategies to deliver FES. The most commonly used sensors reported were inertial measurement units (IMUs), 45% (27); biopotential electrodes, 36.7% (22); vision-based systems, 18.3% (11); and switches, 18.3% (11). The control strategy most reported is closed-loop; however, most of the current commercial FES systems employ open-loop strategies due to their simplicity. Three main factors were identified that should be considered when choosing a sensor for gait-oriented FES systems: wearability, accuracy, and affordability. We believe that the combination of computer vision systems with artificial intelligence-based control algorithms can contribute to the development of minimally invasive and personalized FES systems for the gait rehabilitation of patients with FDS. [ABSTRACT FROM AUTHOR]

Most studies on sprint performance have focused on kinematics and kinetics of the musculoskeletal system for adults, with little research on the central sensorimotor transmission and processes, especially for adolescent sprinters. This study aimed to determine whether differences in the integrity of the central auditory system and audiomotor transmissions between the elite and sub-elite adolescent sprinters may affect their performance in the 100 m time. Twenty-nine adolescent junior high school students, including elite national-class and sub-elite regional-class athletes, were assessed. Visual and auditory evoked potentials (VEP and AEP) as well as electroencephalography (EEG) and electromyography (EMG) were recorded and analyzed during a sprint start. The electrophysiological results clearly reveal differences in central auditory transmission between elite and sub-elite groups, and between sexes. There were significant differences between elite and sub-elite groups, and during a sprint start, the EEG activities for elite female and male athletes showed significant time-dependent differences in peak amplitudes following the three auditory cues (ready, set, and gunshot). These findings can provide coaches with a more comprehensive consideration for sports-specific selection based on the athletes' individual conditions, e.g., sensorimotor neuroplastic training for providing precise and efficient training methods to improve young sprinters' performance. [ABSTRACT FROM AUTHOR]

Most BCI systems used in neurorehabilitation detect EEG features indicating motor intent based on machine learning, focusing on repetitive movements, such as limb flexion and extension. These machine learning methods require large datasets and are time consuming, making them unsuitable for same-day rehabilitation training following EEG measurements. Therefore, we propose a BMI system based on fuzzy inference that bypasses the need for specific EEG features, introducing an algorithm that allows patients to progress from measurement to training within a few hours. Additionally, we explored the integration of electromyography (EMG) with conventional EEG-based motor intention estimation to capture continuous movements, which is essential for advanced motor function training, such as skill improvement. In this study, we developed an algorithm that detects the initial movement via EEG and switches to EMG for subsequent movements. This approach ensures real-time responsiveness and effective handling of continuous movements. Herein, we report the results of this study. [ABSTRACT FROM AUTHOR]

This study examines pedaling asymmetry using the electromyogram (EMG) complexity of six bilateral lower limb muscles for chronic stroke survivors. Fifteen unilateral chronic stroke and twelve healthy participants joined passive and volitional recumbent pedaling tasks using a self-modified stationary bike with a constant speed of 25 revolutions per minute. The fuzzy approximate entropy (fApEn) was adopted in EMG complexity estimation. EMG complexity values of stroke participants during pedaling were smaller than those of healthy participants (p = 0.002). For chronic stroke participants, the complexity of paretic limbs was smaller than that of non-paretic limbs during the passive pedaling task (p = 0.005). Additionally, there was a significant correlation between clinical scores and the paretic EMG complexity during passive pedaling (p = 0.022, p = 0.028), indicating that the paretic EMG complexity during passive movement might serve as an indicator of stroke motor function status. This study suggests that EMG complexity is an appropriate quantitative tool for measuring neuromuscular characteristics in lower limb dynamic movement tasks for chronic stroke survivors. [ABSTRACT FROM AUTHOR]

A stroke represents a significant medical condition characterized by the sudden interruption of blood flow to the brain, leading to cellular damage or death. The impact of stroke on individuals can vary from mild impairments to severe disability. Treatment for stroke often focuses on gait rehabilitation. Notably, assessing muscle activation and kinematics patterns using electromyography (EMG) and stereophotogrammetry, respectively, during walking can provide information regarding pathological gait conditions. The concurrent measurement of EMG and kinematics can help in understanding disfunction in the contribution of specific muscles to different phases of gait. To this aim, complexity metrics (e.g., sample entropy; approximate entropy; spectral entropy) applied to EMG and kinematics have been demonstrated to be effective in identifying abnormal conditions. Moreover, the conditional entropy between EMG and kinematics can identify the relationship between gait data and muscle activation patterns. This study aims to utilize several machine learning classifiers to distinguish individuals with stroke from healthy controls based on kinematics and EMG complexity measures. The cubic support vector machine applied to EMG metrics delivered the best classification results reaching 99.85% of accuracy. This method could assist clinicians in monitoring the recovery of motor impairments for stroke patients. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) is continuing to grow as more affordable technological devices become available. Video games are one of the most profitable applications, while rehabilitation has the most significant social impact. Both applications require a proper user evaluation to provide personalized experiences that avoid boring or stressful situations. Despite the successful applications, there are several opportunities to improve the field of human–machine interactions, one of the most popular ones being the use of affect detection to create personalized experiences. In that sense, this study presents the implementation of two dynamic difficulty adaptation strategies. The person's affective state is estimated through a machine learning classification model, which later serves to adapt the difficulty of the video game online. The results show that it is possible to maintain the user at a given difficulty level, which is analogous to achieving the well-known flow state. Among the two implemented strategies, no statistical differences were found in the workload induced by the users. However, more physical demands and a higher frustration were induced by one of the strategies, validated with the recorded muscular activity. The results obtained contribute to the state of the art of DDA strategies in virtual reality driven by affective data. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to assess the effect of a single session of EMG biofeedback in a group of postmenopausal women on improving technique in pelvic floor muscle (PFM) contractions (exercises). Methods: Sixty-two women aged 60 to 85 years (69 ± 4; mean ± SD) participated in the study. We assessed the technique of PFM exercises via surface electromyography (EMG) using a vaginal probe. A single assessment sequence consisted of 11 exercises involving the conscious contraction of the PFM, during which the order of activation for selected muscles was determined. We then awarded scores for exercise technique on a scale from 0 to 4, where 4 represented the best technique and 0 represented no activation of PFMs. In the second assessment, we used a biofeedback method to teach PFM exercise technique. Results: In total, 32% (n = 20) of the participants were unable to correctly perform the first PFM contraction, scoring 0.9 ± 0.79. After a single EMG biofeedback session, these women received 1.7 ± 1.08 scores (p = 0.003). In the tenth exercise, there was also a statistically significant improvement between the first (baseline) and second assessment (1.7 ± 1.34 and 2.15 ± 1.09, respectively; p = 0.037). For the remaining exercises, the results were not statistically significant, but we observed a positive trend of change. Conclusions: The use of a single EMG biofeedback session is an effective method of improving technique in PFM exercises in a group of women who initially performed them incorrectly. [ABSTRACT FROM AUTHOR]

Introduction: This study analyzes the efficacy of trapezius muscle electromyography (EMG) in discerning mental states, namely stress and meditation. Methods: Fifteen healthy participants were monitored to assess their physiological responses to mental stressors and meditation. Sensors were affixed to both the right and left trapezius muscles to capture EMG signals, while simultaneous electroencephalography (EEG) was conducted to validate cognitive states. Results: Our analysis of various EMG features, considering frequency ranges and sensor positioning, revealed significant changes in trapezius muscle activity during stress and meditation. Notably, low-frequency EMG features facilitated enhanced stress detection. For accurate stress identification, sensor configurations can be limited to the right trapezius muscle. Furthermore, the introduction of a novel method for determining asymmetry in EMG features suggests that applying sensors on bilateral trapezius muscles can improve the detection of mental states. Conclusion: This research presents a promising avenue for efficient cognitive state monitoring through compact and convenient sensing. [ABSTRACT FROM AUTHOR]

Upper-limb exoskeletons for industrial applications can enhance the comfort and productivity of workers by reducing muscle activity and intra-articular forces during overhead work. Current devices typically employ a spring-based mechanism to balance the gravitational torque acting on the shoulder. As an alternative, this paper presents the design of a passive upper-limb exoskeleton based on McKibben artificial muscles. The interaction forces between the exoskeleton and the user, as well as the mechanical resistance of the exoskeleton structure, were investigated to finalize the design of the device prior to its prototyping. Details are provided about the solutions adopted to assemble, wear, and regulate the exoskeleton's structure. The first version of the device weighing about 5.5 kg was manufactured and tested by two users in a motion analysis laboratory. The results of this study highlight that the exoskeleton can effectively reduce the activation level of shoulder muscles without affecting the lumbar strain. [ABSTRACT FROM AUTHOR]

This is a protocol for comprehensive analysis of gait and affecting factors in individuals with incomplete paraplegia due to spinal cord injury (SCI). A SCI is a devastating event affecting both sensory and motor functions. Due to better care, the SCI population is changing, with a greater proportion retaining impaired ambulatory function. Optimizing ambulatory function after SCI remains challenging. To investigate factors influencing optimal ambulation, a multi-professional research project was grounded with expertise from clinical rehabilitation, neurophysiology, and biomechanical engineering from Karolinska Institutet, the Spinalis Unit at Aleris Rehab Station (Sweden's largest center for specialized neurorehabilitation), and the Promobilia MoveAbility Lab at KTH Royal Institute of Technology. Ambulatory adults with paraplegia will be consecutively invited to participate. Muscle strength, sensitivity, and spasticity will be assessed, and energy expenditure, 3D movements, and muscle function (EMG) during gait and submaximal contractions will be analyzed. Innovative computational modeling and data-driven analyses will be performed, including the identification of clusters of similar movement patterns among the heterogeneous population and analyses that study the link between complex sensorimotor function and movement performance. These results may help optimize ambulatory function for persons with SCI and decrease the risk of secondary conditions during gait with a life-long perspective. [ABSTRACT FROM AUTHOR]

In virtual reality (VR), a factor that can maximize user immersion is the development of an intuitive and sensory interaction method. Physical devices such as controllers or data gloves of existing VR devices are used to control the movement intentions of the user, but their shortfall is that grip strength and detailed muscle strength cannot be reflected. Therefore, this study intended to establish a more sensory VR environment compared to existing methods by reflecting the grip strength of the flexor digitorum profundus of the user of the VR content. In this experiment, the muscle activity of the flexor digitorum profundus was obtained from six subjects based on surface electromyography, and four objects with differing intensity were created within a VR program in which the objects were made to be destroyed depending on muscle activity. As a result, satisfaction was improved because the users could sensitively interact with the objects inside the VR environment, and the intended motion control of the user was reflected in the VR content. [ABSTRACT FROM AUTHOR]

This study compares biomechanical and bioelectric electromyography (EMG) normalization techniques across disparate age cohorts during walking to assess the impact of normalization methods on the functional interpretation of EMG data. The biomechanical method involved scaling EMG to a target absolute torque (EMGTS) from a joint-specific task and the chosen bioelectric methods were peak and mean normalization taken from the EMG signal during gait, referred to as dynamic mean and dynamic peak normalization (EMGMean and EMGPeak). The effects of normalization on EMG amplitude, activation pattern, and inter-subject variability were compared between disparate cohorts, including OLD (76.6 yrs N = 12) and YOUNG (26.6 yrs N = 12), in five lower-limb muscles. EMGPeak normalization resulted in differences between YOUNG and OLD cohorts in Biceps Femoris (BF) and Medial Gastrocnemius (MG) that were not observed with EMGMean or EMGTS normalization. EMGPeak and EMGMean normalization also demonstrated interactions between age and the phase of gait in BF that were not seen with EMGTS. Correlations showed that activation patterns across the gait cycle were similar between all methods for both age groups and the coefficient of variation comparisons found that EMGTS produced the greatest inter-subject variability. We have shown that the normalization technique can influence the interpretation of findings when comparing disparate populations, highlighting the need to carefully interpret functional differences in EMG between disparate cohorts. [ABSTRACT FROM AUTHOR]

The aim of this study was to analyze lower leg muscle forces during strength exercises such as back squats, good mornings and split squats, with a particular emphasis on females. By focusing on females, who are more vulnerable to anterior cruciate ligament injuries, we aimed to better understand muscle engagement and its role in injury prevention. Eight participants were monitored during exercises with a barbell load of 25% of body weight and, during the back squat, an additional 50% load. The analysis was conducted using personalized musculoskeletal models, electromyography (EMG) and Vicon motion capture systems to assess various muscle groups, including the m. gluteus maximus and m. gluteus medius, as well as the hamstring and quadriceps muscles. The back squat produced the highest forces for the quadriceps muscles, particularly the rectus femoris (>25 N/kg), as well as in the back leg during the split squat (>15 N/kg). The gluteal muscles were most active during good mornings and in the front leg of the split squat, especially the m. gluteus maximus medial part (>20 N/kg). The hamstrings generated the highest muscle forces in the front leg of the split squat, with the greatest forces observed in the m. semimembranosus. Our research highlights how musculoskeletal modeling helps us to understand the relationship among muscles, joint angles and anterior cruciate ligament injury risks, especially in strength training females. The results emphasize the need for personalized exercise guidance and customized models to make strength training safer and more effective. [ABSTRACT FROM AUTHOR]

Modular control of the muscle, which is called muscle synergy, simplifies control of the movement by the central nervous system. The purpose of this study was to explore the synergy in both the frequency and movement domains based on the non-negative Tucker decomposition (NTD) method. Surface electromyography (sEMG) data of 8 upper limb muscles in 10 healthy subjects under wrist flexion (WF) and wrist extension (WE) were recorded. NTD was selected for exploring the multi-domain muscle synergy from the sEMG data. The results showed two synergistic flexor pairs, Palmaris longus–Flexor Digitorum Superficialis (PL-FDS) and Extensor Carpi Radialis–Flexor Carpi Radialis (ECR-FCR), in the WF stage. Their spectral components are mainly in the respective bands 0–20 Hz and 25–50 Hz. And the spectral components of two extensor pairs, Extensor Digitorum–Extensor Carpi Ulnar (ED-ECU) and Extensor Carpi Radialis–Brachioradialis (ECR-B), are mainly in the respective bands 0–20 Hz and 7–45 Hz in the WE stage. Additionally, further analysis showed that the Biceps Brachii (BB) muscle was a shared muscle synergy module of the WE and WF stage, while the flexor muscles FCR, PL and FDS were the specific synergy modules of the WF stage, and the extensor muscles ED, ECU, ECR and B were the specific synergy modules of the WE stage. This study showed that NTD is a meaningful method to explore the multi-domain synergistic characteristics of multi-channel sEMG signals. The results can help us to better understand the frequency features of muscle synergy and shared and specific synergies, and expand the study perspective related to motor control in the nervous system. [ABSTRACT FROM AUTHOR]

This paper presents the design, development, and validation of a novel e-textile leg sleeve for non-invasive Surface Electromyography (sEMG) monitoring. This wearable device incorporates e-textile sensors for sEMG signal acquisition from the lower limb muscles, specifically the anterior tibialis and lateral gastrocnemius. Validation was conducted by performing a comparative study with eleven healthy volunteers to evaluate the performance of the e-textile sleeve in acquiring sEMG signals compared to traditional Ag/AgCl electrodes. The results demonstrated strong agreement between the e-textile and conventional methods in measuring descriptive metrics of the signals, including area, power, mean, and root mean square. The paired data t-test did not reveal any statistically significant differences, and the Bland–Altman analysis indicated negligible bias between the measures recorded using the two methods. In addition, this study evaluated the wearability and comfort of the e-textile sleeve using the Comfort Rating Scale (CRS). Overall, the scores confirmed that the proposed device is highly wearable and comfortable, highlighting its suitability for everyday use in patient care. [ABSTRACT FROM AUTHOR]

Recent studies have highlighted the possibility of using surface electromyographic (EMG) signals to develop human–computer interfaces that are also able to recognize complex motor tasks involving the hand as the handwriting of digits. However, the automatic recognition of words from EMG information has not yet been studied. The aim of this study is to investigate the feasibility of using combined forearm and wrist EMG probes for solving the handwriting recognition problem of 30 words with consolidated machine-learning techniques and aggregating state-of-the-art features extracted in the time and frequency domains. Six healthy subjects, three females and three males aged between 25 and 40 years, were recruited for the study. Two tests in pattern recognition were conducted to assess the possibility of classifying fine hand movements through EMG signals. The first test was designed to assess the feasibility of using consolidated myoelectric control technology with shallow machine-learning methods in the field of handwriting detection. The second test was implemented to assess if specific feature extraction schemes can guarantee high performances with limited complexity of the processing pipeline. Among support vector machine, linear discriminant analysis, and K-nearest neighbours (KNN), the last one showed the best classification performances in the 30-word classification problem, with a mean accuracy of 95% and 85% when using all the features and a specific feature set known as TDAR, respectively. The obtained results confirmed the validity of using combined wrist and forearm EMG data for intelligent handwriting recognition through pattern recognition approaches in real scenarios. [ABSTRACT FROM AUTHOR]

In this work, a circuit topology for the implementation of a multi-electrode superficial electromyography (EMG) front-end is presented based on a type II current conveyor (CCII). The presented topology provides a feasible way to implement an amplifier capable of measuring several electrode locations and obtaining the signal of interest for posterior acquisition. In particular, a five-electrode normal double differential (NDD) EMG spatial filter is demonstrated. The signal modes necessary for the analysis of the circuit are derived, the respective rejection ratios are obtained, and the noise characteristic is calculated. A board-level electrode is implemented as a proof of concept, achieving a gain equal to 28 dB, a bandwidth of 17 Hz to 578 Hz, a noise voltage linked to the input of 3.7 μ V rms and a common-mode rejection ratio higher than 95 dB at interference frequencies. The topology was validated after using it as an active electrode in experimental EMG measurements with an NDD dry-contact electrode in a flexible printed circuit board. [ABSTRACT FROM AUTHOR]

Stuttering, affecting approximately 1% of the global population, is a complex speech disorder significantly impacting individuals' quality of life. Prior studies using electromyography (EMG) to examine orofacial muscle activity in stuttering have presented mixed results, highlighting the variability in neuromuscular responses during stuttering episodes. Fifty-five participants with stuttering and 30 individuals without stuttering, aged between 18 and 40, participated in the study. EMG signals from five facial and cervical muscles were recorded during speech tasks and analyzed for mean amplitude and frequency activity in the 5–15 Hz range to identify significant differences. Upon analysis of the 5–15 Hz frequency range, a higher average amplitude was observed in the zygomaticus major muscle for participants while stuttering (p < 0.05). Additionally, when assessing the overall EMG signal amplitude, a higher average amplitude was observed in samples obtained from disfluencies in participants who did not stutter, particularly in the depressor anguli oris muscle (p < 0.05). Significant differences in muscle activity were observed between the two groups, particularly in the depressor anguli oris and zygomaticus major muscles. These results suggest that the underlying neuromuscular mechanisms of stuttering might involve subtle aspects of timing and coordination in muscle activation. Therefore, these findings may contribute to the field of biosensors by providing valuable perspectives on neuromuscular mechanisms and the relevance of electromyography in stuttering research. Further research in this area has the potential to advance the development of biosensor technology for language-related applications and therapeutic interventions in stuttering. [ABSTRACT FROM AUTHOR]

Non-linear and dynamic systems analysis of human movement has recently become increasingly widespread with the intention of better reflecting how complexity affects the adaptability of motor systems, especially after a stroke. The main objective of this scoping review was to summarize the non-linear measures used in the analysis of kinetic, kinematic, and EMG data of human movement after stroke. PRISMA-ScR guidelines were followed, establishing the eligibility criteria, the population, the concept, and the contextual framework. The examined studies were published between 1 January 2013 and 12 April 2023, in English or Portuguese, and were indexed in the databases selected for this research: PubMed®, Web of Science®, Institute of Electrical and Electronics Engineers®, Science Direct® and Google Scholar®. In total, 14 of the 763 articles met the inclusion criteria. The non-linear measures identified included entropy (n = 11), fractal analysis (n = 1), the short-term local divergence exponent (n = 1), the maximum Floquet multiplier (n = 1), and the Lyapunov exponent (n = 1). These studies focused on different motor tasks: reaching to grasp (n = 2), reaching to point (n = 1), arm tracking (n = 2), elbow flexion (n = 5), elbow extension (n = 1), wrist and finger extension upward (lifting) (n = 1), knee extension (n = 1), and walking (n = 4). When studying the complexity of human movement in chronic post-stroke adults, entropy measures, particularly sample entropy, were preferred. Kinematic assessment was mainly performed using motion capture systems, with a focus on joint angles of the upper limbs. [ABSTRACT FROM AUTHOR]

The design and control of artificial hands remains a challenge in engineering. Popular prostheses are bio-mechanically simple with restricted manipulation capabilities, as advanced devices are pricy or abandoned due to their difficult communication with the hand. For social robots, the interpretation of human intention is key for their integration in daily life. This can be achieved with machine learning (ML) algorithms, which are barely used for grasping posture recognition. This work proposes an ML approach to recognize nine hand postures, representing 90% of the activities of daily living in real time using an sEMG human–robot interface (HRI). Data from 20 subjects wearing a Myo armband (8 sEMG signals) were gathered from the NinaPro DS5 and from experimental tests with the YCB Object Set, and they were used jointly in the development of a simple multi-layer perceptron in MATLAB, with a global percentage success of 73% using only two features. GPU-based implementations were run to select the best architecture, with generalization capabilities, robustness-versus-electrode shift, low memory expense, and real-time performance. This architecture enables the implementation of grasping posture recognition in low-cost devices, aimed at the development of affordable functional prostheses and HRI for social robots. [ABSTRACT FROM AUTHOR]

Given there are no known studies which have examined multiple lower extremity muscles between different ankle positions during bridging activities, the objective was to assess how employing two different ankle positions (PF versus DF) while performing five common bridging exercises (three bipedal and two unipedal) used in rehabilitation and athletic performance affect core and select lower extremity muscle EMG recruitment. Twenty healthy subjects performed a 5 s isometric hold during five two- and one-leg bridge exercises: (1) on right leg with left knee to chest (1LB-LFlex); (2) on right leg with left knee extended (1LB-LExt); (3) standard two-leg bridge (2LB); (4) two-leg bridge with resistance band around knees (2LB-ABD); and (5) two-leg bridge with ball between knees (2LB-ADD). Surface electromyographic (EMG) data were collected using a Noraxon Telemyo Direct Transmission System from fourteen muscles: (1) three superficial quadriceps (VM, VL, and RF); (2) three hip abductors (TFL, GMED, and GMAX); (3) medial hamstrings (ST) and lateral hamstrings (BF); (4) hip adductors (ADD); (5) erector spinae (ES); (6) latissimus dorsi (LATS); (7) upper rectus abdominis (RA); and (8) external oblique (EO) and internal oblique (IO). EMG data were normalized by maximum voluntary isometric contractions (MVICs). A paired t-test (p < 0.01) was used to assess differences in normalized mean EMG activities between DF and PF for each exercise. EMG activities were significantly greater in DF than PF for the (a) VM, VL, and RF during 1LB-LFlex; (b) ADD during 1LB-LFlex, 1LB-LExt; (c) EO during 1LB-LFlex; and (d) IO during 1LB-LFex. In contrast, EMG activities were significantly greater in PF than DF for ST and BF during all five bridge exercises. Bridging with PF (feet flat) was most effective in recruiting the hamstrings, while bridging with DF (feet up) was most effective in recruiting the quadriceps, hip adductors, and internal and external obliques. [ABSTRACT FROM AUTHOR]

Background and Objectives: Lean body mass loss after bariatric surgery (BS) is remarkable, despite an effective long-term mass reduction and significant declines in comorbidities. A person's functional capacity is adversely affected when their skeletal muscle strength declines by up to 30%. This study aimed to assess the isokinetic trunk muscle strength and fatigue rate in individuals after BS. Materials and Methods: This study included fifty-eight patients, both male and female, ranging in age from 19 to 45. Twenty-seven individuals had BS and twenty-seven healthy people served as the control group. The primary outcomes were the measurement of the concentric and eccentric isokinetic muscle strength of the trunk flexor and extensor muscles. An isokinetic dynamometer (Biodex Rehabilitation and Testing System 3) was used for the assessment of the isokinetic muscle strength. Noraxon EMG was used to determine a secondary outcome, which was the median frequency slop (MF/time) and root mean square slop (RMS/time) of the lumbar erector spinea muscle at 50% of the Maximum Voluntary Isometric Contraction (MVIC). Outcome measures were assessed for both groups. Results: Compared to the control group, the bariatric group showed a lower mean value of both concentric and eccentric isokinetic muscle strength for the flexor and extensor trunk muscles (p < 0.05). In terms of the EMG fatigue rate, the RMS slope increased significantly more than that of the control group, while the MF slope decreased (p > 0.05). Conclusions: The current study found that, in comparison to the healthy subjects, the BS group showed reduced levels of fatigue and isokinetic strength in the trunk muscles. Based on these results, it is recommended that individuals who underwent BS take part in tailored rehabilitation programs to avoid potential musculoskeletal issues in the future. [ABSTRACT FROM AUTHOR]

Introduction: The role of the spinal muscles in scoliogenesis is not fully substantiated. Do they act scoliogenic (inducing scoliosis) or counteract scoliosis in adolescent idiopathic scoliosis (AIS)? In this study, we will examine this by using selectively placed Transcutaneous Electric Stimulation (TES) combined with a cinematic radiographic technique and by performing electromyographic (EMG) evaluations during various motor tasks. Method: This is a cross-sectional study of subjects with small-curve AIS. Using cinematic radiography, they were evaluated dynamically either under electrical stimulation or when performing motor tasks of left and right lateral bending and rotation whilst measuring the muscle activity by EMG. Results: Forty-five patients with AIS were included as subjects. Five subjects volunteered for TES and six subjects performed the motor tasks with EMG. At the initial visual evaluation, and when stimulated with TES, the frontal plane spatial positions of the vertebral bodies showed discrete changes without an apparent pattern. However, analyzing the spatial positions when calibrated, we found that the spinal muscles exert a compressive 'response' with a minor change in the Cobb angle (CA) in small-curve AIS (CA = 10–20°). In larger curves (CA > 20°), TES induced a 'larger deformity' with a relative four-fold change in the CA compared to small-curve AIS with a ratio of 0.6. When evaluating local amplitude (peak) or cumulative (mean) EMG signals, we were unable to find consistent asymmetries. However, one subject had rapid progression and one regressed to a straight spine. When adding the absolute EMG ratios for all four motor tasks, the subject with progression had almost 10-fold less summed EMG ratios, and the subject with regression had more than 3-fold higher summed EMG ratios. Discussion: Based on these findings, we suggest that the spinal muscles in small-curve AIS have a stabilizing function maintaining a straight spine and keeping it in the midline. When deformities are larger (CA > 20°), the spine muscle curve exerts a scoliogenic 'response'. This suggests that the role of the muscles converts from counteracting AIS and stabilizing the spine to being scoliogenic for a CA of more than 20°. Moreover, we interpret higher EMG ratios as heightened asymmetric spinal muscle activity when the spinal muscles try to balance the spine to maintain or correct the deformity. When progression occurs, this is preceded or accompanied by decreased EMG ratios. These findings must be substantiated by larger studies. [ABSTRACT FROM AUTHOR]

(1) Background: Pelvic-floor-muscle (PFM) activation acts synergistically with multiple muscles while performing functional actions in humans. The purpose of this study was to characterize the activity of the PFMs and gluteus medius (GM) while walking and running in physically active nulliparous females. (2) Methods: The peak and average amplitude of maximal voluntary contractions (MVCs) during 60 s of walking (5 and 7 km/h) and running (9 and 11 km/h) were measured with electromyography of the GM and PFMs in 10 healthy female runners. (3) Results: The activation of both muscles increased (p < 0.001) while walking and running. The MVC of the GM was reached when walking and tripled when running, while the PFMs were activated at half their MVC when running. The global ratio of the GM (75.3%) was predominant over that of the PFMs (24.6%) while static and walking. The ratio reached 9/1 (GM/PFM) while running. (4) Conclusion: The GM and PFMs were active while walking and running. The GM's MVC tripled at high speeds, while the PFMs reached only half of their maximum contraction. [ABSTRACT FROM AUTHOR]

To the purpose of this study was to compare muscle oscillation, muscle activation time, and oxygen consumption while wearing compression pants vs. a control garment during running. Methods. Eleven injury-free and recreationally active participants (26.73 ± 12.74 years) were recruited for this study. Participants ran in full-leg compression pants (COMP) and a loose-fitting control garment (CON). Participants ran for 6 min at three submaximal speeds: preferred speed (PS), preferred speed minus 10% (PS − 10%), and preferred speed plus 10% (PS + 10%). The muscle activity of the leg was measured through electromyography (EMG). Muscle oscillation (MO) was measured with accelerometers attached to the thigh and shank. The rate of oxygen consumption ( V. O2) and heart rate (HR) were recorded during each condition. MO was assessed over the 0–60 Hz range by averaging power across 10 Hz bins per leg segment. EMG data was processed to identify the activation time. Following each condition, a belief score was recorded. Dependent variables were each compared between conditions using 2 (garment) × 3 (speed) repeated measure ANOVAs (α = 0.05). The relationship between the belief score and dependent variables (compression-control) was analyzed using Pearson's product-moment correlation (α = 0.05). Results. MO was lower with the full-leg compression pants vs. the control garment (p < 0.05). The muscle activation time for each muscle was shorter while wearing the full-leg compression pants (p < 0.05). Neither the V. O2, RPE, SF, nor the HR were influenced by the garments (p > 0.05). There was no significant correlation between changes in the dependent variables and belief. Conclusion. Wearing compression pants resulted in reduced MO and activation time; however, these changes did not translate into a reduction in V. O2. [ABSTRACT FROM AUTHOR]

Simple Summary: Back pain in horses is a common occurrence and has been considered to be an important factor affecting the well-being of horses. Its occurrence may be related to soft-tissue lesions, osseous injuries, tack-associated problems, or neurological disorders. In addition to conventional diagnostic methods, there are also more sophisticated ones, such as scintigraphy or computed tomography. Advanced diagnostic modalities include algometers, determination of lameness with inertial sensor systems, biometric mats, and geometric morphometrics methods, electromyography, as well as functional electrical stimulation. The purpose of this review article is to summarize the main back diseases in horses, as well as to provide an overview of their traditional, but also modern diagnostic methods. Although traditional diagnostic methods remain the foundation of orthopedic examination and back diseases, the use of new methods will allow for a better understanding of the pathogenesis of these diseases and will also allow for the objective monitoring of the rehabilitation and training process. Back pain is common in ridden horses. Back diseases in horses include Impinging Dorsal Spinous Processes, Ventral Spondylosis, Osteoarthritis of Articular Process, Intervertebral Discs Disease, Vertebral Fractures, Conformational Abnormalities, Desmopathy of the Supraspinous Ligament, Desmopathy of the Intraspinous Ligament, and Longissimus Muscle Strain. Back pain may also develop as a result of lameness (particularly hindlimb lameness). A poorly fitting saddle and an unbalanced rider are also considered important factors influencing the development of back pain in horses. The conventional diagnosis of equine back pain includes a clinical examination and diagnostic imaging examination using ultrasound, radiography, and thermography. Advanced diagnostic modalities of equine back pain involve the objectification of standard procedures through the use of algometers, a lameness locator, biometric mats, and the geometric morphometrics method. In addition to modern diagnostic methods, such as computed tomography and scintigraphy, advances in the diagnosis of equine back pain include the use of electromyography and functional electrical stimulation. The aim of this review article is to familiarize clinicians with the usefulness and capabilities of conventional diagnostic protocols and advanced diagnostic modalities. Although orthopedic examination and traditional diagnostic methods will remain the foundation of the diagnosis of back diseases, modern methods meet the growing expectations towards high-performance horses and allow for deeper diagnostics and objective monitoring of rehabilitation and training progress. [ABSTRACT FROM AUTHOR]

Interest in developing techniques for acquiring and decoding biological signals is on the rise in the research community. This interest spans various applications, with a particular focus on prosthetic control and rehabilitation, where achieving precise hand gesture recognition using surface electromyography signals is crucial due to the complexity and variability of surface electromyography data. Advanced signal processing and data analysis techniques are required to effectively extract meaningful information from these signals. In our study, we utilized three datasets: NinaPro Database 1, CapgMyo Database A, and CapgMyo Database B. These datasets were chosen for their open-source availability and established role in evaluating surface electromyography classifiers. Hand gesture recognition using surface electromyography signals draws inspiration from image classification algorithms, leading to the introduction and development of the Novel Signal Transformer. We systematically investigated two feature extraction techniques for surface electromyography signals: the Fast Fourier Transform and wavelet-based feature extraction. Our study demonstrated significant advancements in surface electromyography signal classification, particularly in the Ninapro database 1 and CapgMyo dataset A, surpassing existing results in the literature. The newly introduced Signal Transformer outperformed traditional Convolutional Neural Networks by excelling in capturing structural details and incorporating global information from image-like signals through robust basis functions. Additionally, the inclusion of an attention mechanism within the Signal Transformer highlighted the significance of electrode readings, improving classification accuracy. These findings underscore the potential of the Signal Transformer as a powerful tool for precise and effective surface electromyography signal classification, promising applications in prosthetic control and rehabilitation. [ABSTRACT FROM AUTHOR]

Background: The main objective of this study was to compare the clinical efficacy of two different devices, the Eptamed elastodontic device and a common bite sold in pharmacies, by assessing a computer aid evaluation of patients' surface electromyography (sEMG) and kinesiography activity of four pairs of masticatory and postural muscles (anterior temporalis, digastricus, masseters and sternocleidomastoids muscles) before and after 6 months of treatment. Materials and Methods: Twelve adult patients with temporomandibular disorders and in need of orthodontic treatment were enrolled in the study and divided into cases and controls. Cases underwent orthodontic treatment with the Eptamed elastodontic device, while controls were treated with a bite sold in pharmacies. Both groups underwent electromyographic and kinesiographic examinations before and after 6 months from the start of treatment. Results: The Eptamed device was found to guarantee an improvement in the electrical activity of the muscles examined. The subjects in the control group, on the other hand, had a general worsening of electrical activity after wearing a splint purchased in a pharmacy. As for the kinesiographic examination, there was no significant improvement in both groups. Conclusions: the use of the Eptamed device in subjects with TMD ensured a greater relaxation of the chewing muscles than a standard bite, effectively reducing the risk of worsening the symptomatology of temporomandibular disorders. [ABSTRACT FROM AUTHOR]

(1) Background: Most daily activities and sport gestures involve asymmetric movement patterns of the upper and lower extremities, transferring asymmetric mechanical loadings to the spine. Therefore, asymmetric lumbar stabilization exercises are frequently prescribed in athletic programs and preventive/rehabilitation interventions. This study analyzed the bilateral activity of the thoracic erector spinae (ES), lumbar multifidus (MF), external oblique (EO), and internal oblique (IO) during asymmetric lumbar stabilization exercises executed in prone, quadruped, and standing-prone positions, rising an upper and/or lower limb in all possible combinations. A limited subset of these data has been previously published in earlier studies. (2) Methods: Surface EMG signals were bilaterally recorded from the selected muscles using wireless EMG sensors. (3) Results: ES, MF, and oblique muscles' activity was significantly higher in prone, standing-prone, and quadruped positions, respectively, and was maximized by specific limb rise combinations (up to 69%, 64%, 34%, and 24% maximum-voluntary-isometric-contraction for ES, MF, EO, and IO). The bilateral difference in muscle activation was significantly higher in the quadruped position and revealed different strategies used to stabilize the body in response to the different exercise conditions. (4) Conclusions: The study results can provide deeper insights into the stabilizing function of the lumbar and oblique muscles and aid in designing optimal progressions for lumbar stabilization exercises. [ABSTRACT FROM AUTHOR]

Introduction: The purpose of this study was to investigate neural patterns within the gluteus maximus (Gmax) muscle to identify optimal EMG placement and injection sites for botulinum toxin and other injectable agents. Methods: This study used 10 fixed and 1 non-fixed adult Korean cadavers. Intramuscular arborization patterns were confirmed in the cranial, middle, and caudal segments of 20 Gmax muscles using Sihler staining. Ultrasound images were obtained from one cadaver, and blue dye was injected using ultrasound guidance to confirm the results. Results: The intramuscular innervation pattern of the Gmax was mostly in the middle part of this muscle. The nerve endings of the Gmax are mainly located in the 40–70% range in the cranial segment, the 30–60% range in the middle segment, and the 40–70% range in the caudal segment. Discussion: Addressing the spasticity of the gluteus maximus requires precise, site-specific botulinum toxin injections. The use of EMG and other injection therapies should be guided by the findings of this study. We propose that these specific sites, which correspond to areas with the densest nerve branches, are the safest and most efficient locations for both botulinum toxin injections and EMG procedures. [ABSTRACT FROM AUTHOR]

Accurate classification of electromyographic (EMG) signals is vital in biomedical applications. This study evaluates different architectures of recurrent neural networks for the classification of EMG signals associated with five movements of the right upper extremity. A Butterworth filter was implemented for signal preprocessing, followed by segmentation into 250 ms windows, with an overlap of 190 ms. The resulting dataset was divided into training, validation, and testing subsets. The Grey Wolf Optimization algorithm was applied to the gated recurrent unit (GRU), long short-term memory (LSTM) architectures, and bidirectional recurrent neural networks. In parallel, a performance comparison with support vector machines (SVMs) was performed. The results obtained in the first experimental phase revealed that all the RNN networks evaluated reached a 100% accuracy, standing above the 93% achieved by the SVM. Regarding classification speed, LSTM ranked as the fastest architecture, recording a time of 0.12 ms, followed by GRU with 0.134 ms. Bidirectional recurrent neural networks showed a response time of 0.2 ms, while SVM had the longest time at 2.7 ms. In the second experimental phase, a slight decrease in the accuracy of the RNN models was observed, standing at 98.46% for LSTM, 96.38% for GRU, and 97.63% for the bidirectional network. The findings of this study highlight the effectiveness and speed of recurrent neural networks in the EMG signal classification task. [ABSTRACT FROM AUTHOR]

Enhancing information representation in electromyography (EMG) signals is pivotal for interpreting human movement intentions. Traditional methods often concentrate on specific aspects of EMG signals, such as the time or frequency domains, while overlooking spatial features and hidden human motion information that exist across EMG channels. In response, we introduce an innovative approach that integrates multiple feature domains, including time, frequency, and spatial characteristics. By considering the spatial distribution of surface electromyographic electrodes, our method deciphers human movement intentions from a multidimensional perspective, resulting in significantly enhanced gesture recognition accuracy. Our approach employs a divide-and-conquer strategy to reveal connections between different muscle regions and specific gestures. Initially, we establish a microscopic viewpoint by extracting time-domain and frequency-domain features from individual EMG signal channels. We subsequently introduce a macroscopic perspective and incorporate spatial feature information by constructing an inter-channel electromyographic signal covariance matrix to uncover potential spatial features and human motion information. This dynamic fusion of features from multiple dimensions enables our approach to provide comprehensive insights into movement intentions. Furthermore, we introduce the space-to-space (SPS) framework to extend the myoelectric signal channel space, unleashing potential spatial information within and between channels. To validate our method, we conduct extensive experiments using the Ninapro DB4, Ninapro DB5, BioPatRec DB1, BioPatRec DB2, BioPatRec DB3, and Mendeley Data datasets. We systematically explore different combinations of feature extraction techniques. After combining multi-feature fusion with spatial features, the recognition performance of the ANN classifier on the six datasets improved by 2.53%, 2.15%, 1.15%, 1.77%, 1.24%, and 4.73%, respectively, compared to a single fusion approach in the time and frequency domains. Our results confirm the substantial benefits of our fusion approach, emphasizing the pivotal role of spatial feature information in the feature extraction process. This study provides a new way for surface electromyography-based gesture recognition through the fusion of multi-view features. [ABSTRACT FROM AUTHOR]

Increasingly prevalent sleep disorders worldwide significantly affect the well-being of individuals. Sleep disorder can be detected by dividing sleep into different stages. Hence, the accurate classification of sleep stages is crucial for detecting sleep disorders. The use of machine learning techniques on physiological signals has shown promising results in the automatic classification of sleep stages. The integration of information from multichannel physiological signals has shown to further enhance the accuracy of such classification. Existing literature reviews focus on studies utilising a single channel of EEG signals for sleep stage classification. However, other review studies focus on models developed for sleep stage classification, utilising either a single channel of physiological signals or a combination of various physiological signals. This review focuses on the classification of sleep stages through the integration of combined multichannel physiological signals and machine learning methods. We conducted a comprehensive review spanning from the year 2000 to 2023, aiming to provide a thorough and up-to-date resource for researchers in the field. We analysed approximately 38 papers investigating sleep stage classification employing various machine learning techniques integrated with combined signals. In this study, we describe the models proposed in the existing literature for sleep stage classification, discuss their limitations, and identify potential areas for future research. [ABSTRACT FROM AUTHOR]

This study analyzed muscle activity during the stand-up paddle stroke, considering the paddling side and the adjacent and opposing muscles relative to the position of the arms during paddling. Methods: Fourteen male paddleboarders performed three trials covering 195 m in which surface electromyography of the upper trapezius, biceps brachii, triceps brachii, tibialis anterior, and gastrocnemius medialis were recorded (four-cycle strokes on each side). The data were processed according to percentage of maximum voluntary contraction (% MVC). The MVC activation values (µV) for each muscle were then calculated and presented as percentage MVC (% MVC). Results: The recovery phase accounted for 60% of the paddle cycle, while the pull phase represented 39%. During right-side paddling, higher % MVC was found in the opposite-side upper trapezius (24.35%, p < 0.01) during the pulling phase and in the adjacent biceps brachii (8.36%, p < 0.03) during the recovery phase. In left-side paddling, greater % MVC was found in the opposite-side upper trapezius (27.60%, p < 0.01) during the pulling phase and in the opposite-side triceps brachii (42.25%, p < 0.04) during the recovery phase. Furthermore, the pulling phase exhibited higher MVC in the opposite-side upper trapezius compared to the recovery phase, both in the right-side (24.35%, p < 0.03) and left-side (27.60%, p < 0.01) paddling. Conclusions: these findings help establish the muscular activity of both sides of the paddling technique and the differences between the upper and lower limbs. [ABSTRACT FROM AUTHOR]

Background: Low-Dye tape (LDT) is a short-term treatment for plantar fasciitis, where external stabilization by means of the tape improves kinetics, kinematics, pain level, and electromyography (EMG). Purpose: The purpose of this study was to compare the EMG of the medial gastrocnemius (MG) and changes in arch height (AH) based on the type of foot. Methods: A total of 30 subjects participated in this study; they walked on a treadmill barefoot and when taped, where the average activity and changes in AH were measured over a 30 s period. The statistical intraclass correlation coefficient (ICC) to test for reliability was calculated, and the Wilcoxon test was determined for measures of EMG and AH. Results: The reliability of the values of EMG was almost perfect. The data show that there was an increase in height in the comparison of the moment pre-baseline walking and post-taped walking on neutral feet (5.61 ± 0.46 vs. 5.77 ± 0.39 cm, p < 0.05), on pronated feet (5.67 ± 0.57 vs. 6.01 ± 0.53 cm, p < 0.001) and on supinated feet (5.97 ± 0.36 vs. 6.28 ± 0.27 cm, p < 0.05). In the MG, EMG activity decreased significantly in the taped condition compared to the baseline condition in neutral subjects (0.0081 ± 0.016 vs. 0.076 ± 0.016 mV, p < 0.05) and in pronated subjects (0.081 ± 0.022 vs. 0.068 ± 0.025 mV, p < 0.05). Conclusions: It was demonstrated that with the use of LDT, there was an improvement in the average activity in the MG in pronated and neutral feet. All foot types improved in arch height with the use of tape. [ABSTRACT FROM AUTHOR]

The emergence of modern prosthetics controlled by bio-signals has been facilitated by AI and microchip technology innovations. AI algorithms are trained using sEMG produced by muscles during contractions. The data acquisition procedure may result in discomfort and fatigue, particularly for amputees. Furthermore, prosthetic companies restrict sEMG signal exchange, limiting data-driven research and reproducibility. GANs present a viable solution to the aforementioned concerns. GANs can generate high-quality sEMG, which can be utilised for data augmentation, decrease the training time required by prosthetic users, enhance classification accuracy and ensure research reproducibility. This research proposes the utilisation of a one-dimensional deep convolutional GAN (1DDCGAN) to generate the sEMG of hand gestures. This approach involves the incorporation of dynamic time wrapping, fast Fourier transform and wavelets as discriminator inputs. Two datasets were utilised to validate the methodology, where five windows and increments were utilised to extract features to evaluate the synthesised sEMG quality. In addition to the traditional classification and augmentation metrics, two novel metrics—the Mantel test and the classifier two-sample test—were used for evaluation. The 1DDCGAN preserved the inter-feature correlations and generated high-quality signals, which resembled the original data. Additionally, the classification accuracy improved by an average of 1.21–5%. [ABSTRACT FROM AUTHOR]

Insomnia, often associated with anxiety and depression, is a prevalent sleep disorder. Biofeedback (BFB) treatment can help patients gain voluntary control over physiological events such as by utilizing electroencephalography (EEG) and electromyography (EMG) power. Previous studies have rarely predicted biofeedback efficacy by measuring the changes in relative EEG power; therefore, we investigated the clinical efficacy of biofeedback for insomnia and its potential neural mechanisms. We administered biofeedback to 82 patients with insomnia, of whom 68 completed 10 sessions and 14 completed 20 sessions. The average age of the participants was 49.38 ± 12.78 years, with 26 men and 56 women. Each biofeedback session consisted of 5 min of EMG and 30 min of EEG feedback, with 2 min of data recorded before and after the session. Sessions were conducted every other day, and four scale measures were taken before the first, fifth, and tenth sessions and after the twentieth session. After 20 sessions of biofeedback treatment, scores on the Pittsburgh Sleep Quality Index (PSQI) were significantly reduced compared with those before treatment (−5.5 ± 1.43,t = −3.85, p = 0.006), and scores on the Beck Depression Inventory (BDI-II) (−7.15 ± 2.43, t = −2.94, p = 0.012) and the State-Trait Anxiety Inventory (STAI) (STAI-S: −12.36 ± 3.40, t = −3.63, p = 0.003; and STAI-T: −9.86 ± 2.38, t = −4.41, p = 0.001) were significantly lower after treatment than before treatment. Beta and theta power were significantly reduced after treatment, compared with before treatment (F = 6.25, p = 0.014; and F = 11.91, p = 0.001). Alpha power was increased after treatment, compared with before treatment, but the difference was not prominently significant (p > 0.05). EMG activity was significantly decreased after treatment, compared with before treatment (F = 2.11, p = 0.015). Our findings suggest that BFB treatment based on alpha power and prefrontal EMG relieves insomnia as well as anxiety and depression and may be associated with increased alpha power, decreased beta and theta power, and decreased EMG power. [ABSTRACT FROM AUTHOR]

Orthogonal generalized Laguerre moments of fractional orders (FrGLMs) are signal and image descriptors. The utilization of the FrGLMs in the analysis of big-size signals encounters three challenges. First, calculating the high-order moments is a time-consuming process. Second, accumulating numerical errors leads to numerical instability and degrades the reconstructed signals' quality. Third, the QR decomposition technique is needed to preserve the orthogonality of the higher-order moments. In this paper, the authors derived a new recurrence formula for calculating the FrGLMs, significantly reducing the computational CPU times. We used the Schwarz–Rutishauser algorithm as an alternative to the QR decomposition technique. The proposed method for computing FrGLMs for big-size signals is accurate, simple, and fast. The proposed algorithm has been tested using the MIT-BIH arrhythmia benchmark dataset. The results show the proposed method's superiority over existing methods in terms of processing time and reconstruction capability. Concerning the reconstructed capability, it has achieved superiority with average values of 25.3233 and 15.6507 with the two metrics PSNR and MSE, respectively. Concerning the elapsed reconstruction time, it also achieved high superiority with an efficiency gain of 0.8. The proposed method is suitable for utilization in the Internet of Healthcare Things. [ABSTRACT FROM AUTHOR]

Exercise therapy as part of the clinical management of patients with neuromuscular diseases (NMDs) is complicated by the limited insights into its efficacy. There is an urgent need for sensitive and non-invasive quantitative muscle biomarkers to monitor the effects of exercise training. Therefore, the objective of this systematic review was to critically appraise and summarize the current evidence for the sensitivity of quantitative, non-invasive biomarkers, based on imaging and electrophysiological techniques, for measuring the effects of physical exercise training. We identified a wide variety of biomarkers, including imaging techniques, i.e., magnetic resonance imaging (MRI) and ultrasound, surface electromyography (sEMG), magnetic resonance spectroscopy (MRS), and near-infrared spectroscopy (NIRS). Imaging biomarkers, such as muscle maximum area and muscle thickness, and EMG biomarkers, such as compound muscle action potential (CMAP) amplitude, detected significant changes in muscle morphology and neural adaptations following resistance training. MRS and NIRS biomarkers, such as initial phosphocreatine recovery rate (V), mitochondrial capacity (Qmax), adenosine phosphate recovery half-time (ADP t1/2), and micromolar changes in deoxygenated hemoglobin and myoglobin concentrations (Δ[deoxy(Hb + Mb)]), detected significant adaptations in oxidative metabolism after endurance training. We also identified biomarkers whose clinical relevance has not yet been assessed due to lack of sufficient study. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the muscle activations of the dominant arm during the forehand stroke of wheelchair tennis. Five players participated in the present study (age: 32.6 ± 9.9 years; body mass: 63.8 ± 3.12 kg; height: 164.4 ± 1.7 cm). The electrical muscle activity of six dominant arm muscles was recorded using an sEMG system. A significant effect of the muscle's activity was observed, and it was shown that the muscle activation was significantly higher in the execution phase compared to the preparation phase in the anterior deltoid and biceps brachii (34.98 ± 10.23% and 29.13 ± 8.27%, p < 0.001); the posterior deltoid, triceps brachii, flexor carpi radialis, and extensor carpi radialis were higher in the follow-through phase than in the execution phase (16.43 ± 11.72%, 16.96 ± 12.19%, 36.23 ± 21.47% and 19.13 ± 12.55%, p < 0.01). In conclusion, it was determined that the muscle activations of the dominant arm muscles demonstrate variances throughout the phases of the forehand stroke. Furthermore, the application of electromyographic analysis to the primary arm muscles has been beneficial in understanding the muscular activity of the shoulder, elbow, and wrist throughout the various phases of the forehand stroke in wheelchair tennis. [ABSTRACT FROM AUTHOR]

Featured Application: When using the prone plank exercise as part of core stabilisation training, clinicians, physiotherapists, and fitness professionals should consider the demonstrated increase in abdominal activation during head flexion and no change in core muscle activity during head extension, when compared to neutral. Additionally, they should consider that the cranio-cervical segment modulates participants' exertion rating and intensify the perceived neuromuscular fatigue when the plank differs from neutral performance. These findings may provide physical therapists and fitness professionals with new guidelines that should be considered when using this exercise as part of core stabilisation training in rehabilitation and sports programmes. This study aimed to investigate the influence of head position on the electromyographical activity of the core muscles during the prone plank exercise. Twenty healthy participants were enrolled in this study. Muscle activation was registered using surface electromyography in the rectus abdominis, external oblique, internal oblique, and the lumbar portion of erector spinae. Three plank conditions were randomly evaluated, varying the position of the cranio-cervical segment during the plank performance (neutral, flexion and extension). The activation of each individual examined muscle and the overall core muscle activity (Total Intensity), as well as the ratings of perceived exertion were analysed with statistical significance set at p < 0.05. Flexion revealed significantly higher Total Intensity values compared to neutral and extension (p < 0.001; effect size (ES) > 0.90). The rectus abdominis presented larger activation in flexion compared to the other variations (p < 0.05; ES > 0.70). Flexion elicited a greater response for both sides of external oblique when compared to neutral and extension, and also for both sides of internal oblique compared to the other conditions (p < 0.05; ES > 0.70). Both flexion and extension reported higher exertion rating values compared to neutral (p < 0.05; ES > 0.90). Head position influenced the electromyographical activation of core musculature, showing highest values when performing the plank in a head flexion. [ABSTRACT FROM AUTHOR]

The increasing density of cities poses a huge threat to public health, so pocket parks with high accessibility and flexibility have become potential resources to promote public health. In this context, the ways in which pocket parks can improve public health have become the focus and challenge of current research. This study selected 10 different types of pocket parks in Fuzhou, China, as the research subjects and collected real-time psychological and physiological data of participants by watching videos of the sample plots. The aim was to explore the impact of the environmental characteristics of pocket parks on the psychological and physiological responses of recreational users. The results of the study showed that: (1) the environmental characteristics of pocket parks significantly affect the psychological and physiological responses of recreationists. Different environmental characteristics can affect recreationists' emotional state, attention recovery, environmental preferences, and the indicators of IBI, HR, SDNN, RMSSD, pNN50, SCL, and EMG to varying degrees. (2) The environment of pocket parks may encourage recreationists to generate positive psychological benefits when the site is larger and has a higher degree of scenic beauty, and when the space is not effectively confined. A pocket park environment with a low paving ratio, open view, cultural elements, topographic changes, special vegetation and distributing space can also have a positive effect on the psychological benefits of recreationists to a certain extent. Low canopy density and high green visibility can also play a role in suppressing negative emotions. (3) Pocket parks with high levels of depression and off-site disturbance are not conducive to positive physiological responses from recreationists, while pocket parks with high levels of green visibility and beauty and specialized vegetation are more likely to provide health benefits to them. [ABSTRACT FROM AUTHOR]

We investigate the distribution of muscle signatures of human hand gestures under Dynamic Time Warping. For this we present a k-Nearest-Neighbors classifier using Dynamic Time Warping for the distance estimate. To understand the resulting classification performance, we investigate the distribution of the recorded samples and derive a method of assessing the separability of a set of gestures. In addition to this, we present and evaluate two approaches with reduced real-time computational cost with regards to their effectiveness and the mechanics behind them. We further investigate the impact of different parameters with regards to practical usability and background rejection, allowing fine-tuning of the induced classification procedure. [ABSTRACT FROM AUTHOR]

The anesthetic dosing procedure is a key element of safe surgical practice, where it is paramount to ensure sufficient dosing of the anesthetic agent to the patient in order to reach the desired depth of sedation for the necessary procedure. One means of monitoring the depth of anesthesia (DoA) involves the use of the bispectral index (BIS), which decodes electroencephalography (EEG) signals acquired from the frontal cortex in a continuous fashion. The shortcomings of this include the complexity of the decoding of EEG signals, insensitivity to certain anesthetic agents, and the costly nature of the technology, which limits its adoption in resource-constrained settings. In this paper, we investigate an alternative source of physiological measurement modalities that can track DoA sufficiently while being much more affordable. Thus, we investigate this notion with the use of the University of Queensland database, which comprises EEG-EMG-ECG physiological data from patients going through a variety of surgical procedures. As part of this, select patient datasets were utilized in addition to a variety of signal decomposition and machine learning models—which totaled around 200 simulations—in order to investigate the most optimal combination of algorithms to track DoA using different physiological measurement modalities. The results showed that under certain algorithmic combinations and modeling processes, the ECG measurement (a ubiquitous monitor in anesthetic practice) can rival and occasionally surpass the accuracy of the EEG for DoA monitoring. In addition to this, we also propose a 2-phase modeling process that involves an algorithmic selection stage followed by a model deployment stage. Subsequent work in this area is advised to involve the acquisition of more physiological data from a broader mix of patients in order to further validate the consistency of the findings made in this study. [ABSTRACT FROM AUTHOR]

Single-leg drop landing (SLDL) and jump landing (SLJL) are frequently used as assessment tools for identifying potential high-risk movement patterns; thus, understanding differences in neuromuscular responses between these types of landings is essential. This study aimed to compare lower extremity neuromuscular responses between the SLDL and SLJL. Thirteen female participants performed an SLDL and SLJL from a 30-cm box height. Vertical ground reaction force (vGRF), time to peak vGRF, and surface electromyography (sEMG) data were collected. Continuous neuromuscular responses, peak vGRF, and time to peak vGRF were compared between the tasks. Statistical parametric mapping (SPM) analysis demonstrated that the SLJL had a significantly higher sEMG activity in the rectus femoris (RF), vastus lateralis (VL) and vastus medialis (VM) within the first 10% of the landing phase compared with SLDL. At 20–30% of the landing phase, sEMGs in the RF and VL during the SLDL were significantly higher compared with SLJL (p < 0.05). A higher peak vGRF and shorter time to peak vGRF was observed during SLJL (p < 0.05). In conclusion, our findings highlight that SLJL exhibited greater RF, VL, and VM activities than SLDL at initial impact (10% landing), coinciding with a higher peak vGRF and shorter time to attain peak vGRF. Our findings support the role of the quadriceps as the primary energy dissipator during the SLJL. [ABSTRACT FROM AUTHOR]

Prosthetics and orthotics research, studies, and technologies have been evolving through the years. According to World Health Organization (WHO) data, it is estimated that, globally, 35–40 million people require prosthetics and orthotics usage in daily life. Prosthetics and orthotics demand is increasing due to certain factors. One of the factors is vascular-related disease, which leads to amputation. Prosthetic usage can increase an amputee's quality of life. Therefore, studies of the ergonomic design of prosthetics are important. The ergonomic factor in design delivers prosthetic products that are comfortable for daily use. One way to incorporate the ergonomic design of prosthetics is by studying the human walking gait. This paper presents a multiclassification of human walking gait based on electromyography (EMG) signals using a machine learning method. An EMG sensor was attached to the bicep femoris longus and gastrocnemius lateral head to acquire the EMG signal. The experiment was conducted by volunteers during normal walking activity at various speeds and the movements were segmented as initial contact, which was labeled as initial gait; loading response to the terminal stance, which was labeled as mid-gait; and pre-swing to terminal swing, which was labeled as final gait. The EMG signal was then characterized using an artificial neural network (ANN) and compared to six training accuracy methods, i.e., the Levenberg–Marquardt backpropagation training algorithm, quasi-Newton training method, Bayesian regulation backpropagation training method, gradient descent backpropagation, gradient descent with adaptive learning rate backpropagation, and one-step secant backpropagation. The machine learning study performed well in the classification of three classes of human walking gait with an overall accuracy (training, testing, and validation) of 96% for Levenberg–Marquardt backpropagation. The gait data will be used to explore the design of lower limb prosthetics in future research. [ABSTRACT FROM AUTHOR]

The harvest phase plays an important role in the whole process of production of tomato fruit. Therefore, it is necessary to ensure a technological process that will not damage biological materials. The harvest phase plays an important role in the whole process. Many growers use special machines for harvesting, but there are fruits and vegetables that should be harvested manually to avoid damaging the surface or parenchyma tissue of the harvested objects. In addition to maintaining the quality of biological materials, work comfort, and ergonomic conditions for pickers should be ensured because inadequate working conditions do not encourage employees to undertake manual work in horticulture. Therefore, there have been shortages of workers on Polish plantations in the past year. Based on manual tomato harvesting, the authors conducted a matched qualitative research study on biological materials and work ergonomics. For this purpose, the Grip System was used to investigate tomato quality by assessing the impact of picking hand pressure (in three different picker's body positions) on the harvested objects. Simultaneously, for the picker's ergonomic analysis, a non-invasive surface electromyography method was used to precisely measure changes in muscle motor unit action in the picker's wrist and lumbar spine while in three characteristic picker's positions. The tests found that the poorest body position was when the body was deeply inclined and simultaneously twisted. No significant effect was shown of the body position of the tomato picker on the deterioration of the picked fruit quality. However, body positions significantly affect the level of physical load and work comfort. [ABSTRACT FROM AUTHOR]