A review of non-invasive techniques to detect and predict localised muscle fatigue

Denoising of the gradient artifact present in simultaneous studies of muscle blood oxygen level dependent (BOLD) signal and electromyography (EMG)

The MR-induced gradient artifact affects EMG recordings during simultaneous muscle BOLD/EMG acquisitions. However, no dedicated hardware can remove the gradient artifact easily, and alternative methods are expensive and time-consuming. This study aimed to develop three denoising methods requiring different processing levels and MR-compatible hardware. At two time points, surface EMG was recorded from the lower leg of 6 participants (50:50 sex ratio, age = 26.24.6 yrs., height = 173.59.2 cm, weight = 71.511.4 kg) using a plantar flexion-based block design consisting of 30s of rest followed by 30s of flexion for 5 min, under three conditions: inside the MRI bore, with and without a BOLD sequence (3 T, BOLD sequence, GRE EPI, 10 slices, 64×64 matrix, 2 mm thickness, and TE/TR/flip = 35/3000 ms/70), and outside the MRI environment. Simultaneous BOLD/EMG recordings were denoised using average artifact subtraction with three methods of artifact template creation, each having varying timing and hardware requirements. Method M1 builds the artifact template by recording the scanner triggers coming from the MRI; M2 creates the artifact template with a constant artifact period computed as TR/[number of slices]; M3 estimates the artifact template by looking at the periodicity of the gradient artifact located in the EMG recordings. Following postprocessing, SNR analysis was performed, comparing rest-to-flexion periods, to assess the efficacy of denoising methods and to compare differences between conditions. Linear mixed-effects models showed no significant differences in the mean SNR between denoising methods (p = 0.656). Furthermore, EMG SNR measurements were significantly affected by the magnetic environment (p < 0.05) but not by muscle fatigue over time (p = 0.975). EMG recordings contaminated with gradient artifacts during simultaneous BOLD/EMG can be efficiently denoised using all proposed methods, with two methods requiring no extra hardware. With minimal post-processing, EMG can easily be performed during muscle BOLD MRI studies.

Semitendinosus and biceps femoris long head activity during the single leg bridge test in healthy individuals

INTRODUCTION

The single leg bridge test (SLBT) has been suggested as a clinical test to examine function, screen injury risk, and monitor the effectiveness of rehabilitation programes targeting the hamstring. This study aimed to determine the inter-day reliability and repeatability of both SLBT performance, semitendinosus (ST), and biceps femoris long head (BFlh) surface electromyography (sEMG) responses and characterise the BFlh and ST electrical activity during the SLBT performed until exhaustion in healthy individuals.

METHODS

Twelve physically active young men without previous hamstring injury were tested for the number of repetitions attained, and sEMG signal median frequency and amplitude in both ST and BFlh of each lower limb, randomly in two sessions, with a seven-day interval between sessions.

RESULTS

High reliability [ICC = 0.85] was found for the number of SLBT repetitions attained. Reliability of sEMG outcomes showed better results for ST (ICC = 0.62-0.91) than for BFlh (ICC = 0.39-0.81), and a high to very-high repeatability was found for both ST (ICC = 0.91-0.84) and BFlh (ICC = 0.91-0.85). sEMG median frequency decreased and amplitude increased for both BFlh (p ≤ 0.001) and ST (p ≤ 0.039) at the end of SLBT, suggesting localised fatigue.

CONCLUSIONS

The SLBT performed by healthy individuals until exhaustion proved to be reliable and to induce fatigue in both BFlh and ST, where the sEMG median frequency and amplitude can be measured on different days with acceptable reliability and high repeatability, suggesting its potential future use in both practical and clinical settings.

Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors

Muscle fatigue is defined as a reduced ability to maintain maximal strength during voluntary contraction. It is associated with musculoskeletal disorders that affect workers performing repetitive activities, affecting their performance and well-being. Although electromyography remains the gold standard for measuring muscle fatigue, its limitations in long-term work motivate the use of wearable devices. This article proposes a computational model for estimating muscle fatigue using wearable and non-invasive devices, such as Optical Fiber Sensors (OFSs) and Inertial Measurement Units (IMUs) along the subjective Borg scale. Electromyography (EMG) sensors are used to observe their importance in estimating muscle fatigue and comparing performance in different sensor combinations. This study involves 30 subjects performing a repetitive lifting activity with their dominant arm until reaching muscle fatigue. Muscle activity, elbow angles, and angular and linear velocities, among others, are measured to extract multiple features. Different machine learning algorithms obtain a model that estimates three fatigue states (low, moderate and high). Results showed that between the machine learning classifiers, the LightGBM presented an accuracy of 96.2% in the classification task using all of the sensors with 33 features and 95.4% using only OFS and IMU sensors with 13 features. This demonstrates that elbow angles, wrist velocities, acceleration variations, and compensatory neck movements are essential for estimating muscle fatigue. In conclusion, the resulting model can be used to estimate fatigue during heavy lifting in work environments, having the potential to monitor and prevent muscle fatigue during long working shifts.

A Narrative Review on Multi-Domain Instrumental Approaches to Evaluate Neuromotor Function in Rehabilitation

In clinical scenarios, the use of biomedical sensors, devices and multi-parameter assessments is fundamental to provide a comprehensive portrait of patients' state, in order to adapt and personalize rehabilitation interventions and support clinical decision-making. However, there is a huge gap between the potential of the multidomain techniques available and the limited practical use that is made in the clinical scenario. This paper reviews the current state-of-the-art and provides insights into future directions of multi-domain instrumental approaches in the clinical assessment of patients involved in neuromotor rehabilitation. We also summarize the main achievements and challenges of using multi-domain approaches in the assessment of rehabilitation for various neurological disorders affecting motor functions. Our results showed that multi-domain approaches combine information and measurements from different tools and biological signals, such as kinematics, electromyography (EMG), electroencephalography (EEG), near-infrared spectroscopy (NIRS), and clinical scales, to provide a comprehensive and objective evaluation of patients' state and recovery. This multi-domain approach permits the progress of research in clinical and rehabilitative practice and the understanding of the pathophysiological changes occurring during and after rehabilitation. We discuss the potential benefits and limitations of multi-domain approaches for clinical decision-making, personalized therapy, and prognosis. We conclude by highlighting the need for more standardized methods, validation studies, and the integration of multi-domain approaches in clinical practice and research.

Measuring upper limb active joint position sense: Introducing a new clinical tool - The Upper Limb Proprioception Reaching Test

BACKGROUND

Proprioception is our sense of body awareness, including the sub-category of active joint position sense (AJPS). AJPS is fundamental to joint stability and movement coordination. Despite its importance, there remain few confident ways to measure upper limb AJPS in a clinic.

OBJECTIVE

To assess a new AJPS clinical tool, the Upper Limb Proprioception Reaching Test (PRO-Reach; seven targets), for discriminant validity, intra-rater and absolute reliability.

DESIGN

Cross-sectional measurement study.

METHODS

Seventy-five healthy participants took part in a single session with 2 consecutive evaluations (E1 and E2) (within-day reliability). Twenty participants were randomly selected to perform a dominant shoulder fatigue protocol (discriminant validity), whereafter a third evaluation was repeated (E3). The PRO-Reach was analyzed with paired t tests (discriminant validity), intra-class correlation coefficients (ICCs) and minimal detectable change [MDC]) (intra-rater: within-day and between-trial relative and absolute reliability).

RESULTS

The PRO-Reach supports moderate (mostly superior targets) to excellent (mostly inferior targets) reliability. Between-trial ICCs (T1/T2/T3) varied between 0.72 and 0.90, and within-day (E1/E2) ICCs between 0.45 and 0.72, with associated MDC95 values (3.9-5.0 cm). The overall scores (seven targets) supported the strongest within-day reliability (ICC = 0.77). The inferior targets demonstrated the highest between-trial and within-day reliability (ICCs = 0.90 and 0.72). A fatigue effect was found with the superior and superior-lateral targets (P < .05).

CONCLUSIONS

The inferior targets and overall scores demonstrate the strongest reliability. The use of the PRO-Reach tool may be suitable for clinical use upon further psychometric testing amongst pathological populations.

LEVEL OF EVIDENCE

Level III cross-sectional study.

Progressive Rehabilitation Based on EMG Gesture Classification and an MPC-Driven Exoskeleton

Stroke is a leading cause of disability and death worldwide, with a prevalence of 200 millions of cases worldwide. Motor disability is presented in 80% of patients. In this context, physical rehabilitation plays a fundamental role for gradually recovery of mobility. In this work, we designed a robotic hand exoskeleton to support rehabilitation of patients after a stroke episode. The system acquires electromyographic (EMG) signals in the forearm, and automatically estimates the movement intention for five gestures. Subsequently, we developed a predictive adaptive control of the exoskeleton to compensate for three different levels of muscle fatigue during the rehabilitation therapy exercises. The proposed system could be used to assist the rehabilitation therapy of the patients by providing a repetitive, intense, and adaptive assistance.

Fatigue-Related Change in Surface Electromyographic Activities of the Perilaryngeal Muscles

PURPOSE

This study set out to quantify the fatigue-related changes in surface electromyographic (sEMG) activities of the perilaryngeal muscles following a vocal loading task.

METHOD

Thirty-six young healthy participants (M _age = 22.4 years) with normal voice performed karaoke singing for at least 100 min. Before the singing task, all participants underwent the sEMG measure and completed a Perceived Vocal Fatigue Score (P-VFS) questionnaire. After the singing task, all participants were immediately measured with the P-VFS again. Half of the participants were then measured for their sEMG immediately after their karaoke singing task, and the other half were given 20 min of rest before undertaking the sEMG measure. The P-VFS and the median frequency (MDF) of the sEMG signals collected from the suprahyoid, infrahyoid, and sternocleidomastoid muscles before and after the singing task were compared using a linear mixed-effects model.

RESULTS

All participants reported a perceived vocal fatigue after singing, with a significantly increased P-VFS. Compared with the presinging baseline, the MDF of the sEMG signals in perilaryngeal muscles was significantly lower immediately after the singing task. Such a significant difference was also found after 20 min following the singing task.

CONCLUSIONS

The MDF analysis of the sEMG signals could identify and quantify the performance vocal fatigue contributed by perilaryngeal muscles following a vocal loading task. The findings also showed that such fatigue in perilaryngeal muscles, as far as sEMG activities are concerned, can last for at least 20 min.

Analysis of physiological tremors during different intensities of armcurl exercises using wearable three-axis accelerometers in healthy young men: a pilot study

PURPOSE

The purpose of this pilot study was to determine whether physiological tremors (PTs) assessed using an accelerometer could be used to evaluate resistance exercise intensity.

METHODS

Twenty healthy young men with no prior experience of resistance exercise were recruited. Different intensities (resting, 30%, 50%, and 70% of their predetermined one-repetition maximum (1-RM)) of arm-curl exercise were used to elicit PT. The total work was held equally by varying the number of repetitions, with five sets for each intensity. Sessions of varying intensities were performed randomly with a washout period of at least a week. PT responses were recorded during exercise using accelerometers (3-axis) attached to the wrist and ear. Electromyography (EMG) data were obtained from the biceps brachii muscle during exercise. PT and EMG data were expressed as the average root mean square index.

RESULTS

The EMG amplitude increased significantly as exercise intensity increased. Furthermore, PT amplitude significantly increased as exercise intensity increased. Moreover, the Borg rating of perceived exertion (RPE) and lactic acid levels increased significantly. The wrist PT was related to ear PT, EMG, RPE, and lactic acid levels. Additionally, ear PT was associated with EMG, RPE, and lactic acid levels.

CONCLUSION

This pilot study identified changes in PT during resistance exercise at different intensities. We suggest that the use of PT analyses during these exercises provides a more intuitive delineation of resistance exercise intensity and fatigue.

Use of surface electromyography to evaluate effects of whole-body vibration exercises on neuromuscular activation and muscle strength in the elderly: a systematic review

PURPOSE

Reduction of muscle strength and lean mass, increase in the risk of falls, higher mortality, and morbidity are observed in geriatric syndromes. Physical activity is an effective intervention in reducing signs and symptoms of geriatric syndromes. Whole-body vibration exercise (WBVE) is an intervention with low cost and has been effective.

MATERIALS AND METHODS

The aim of this systematic review aimed to determine the effects of WBVE on neuromuscular activation and muscle strength in the elderly. Searches in PubMed, Embase, Science direct, and Scopus databases were conducted. Six studies, that analyzed the use of surface electromyography evaluating effects of WBVE on neuromuscular activation and muscle strength in the elderly, published in English, were included.

RESULTS

Six studies were included. One hundred forty-six individuals participated in the studies and 24 were males (16.43%), with an average age of 74.20 ± 7.66 years. Five publications were defined as "fair" methodological in the PEDro scale, the risk of bias was high and the risk of bias for non-randomized studies was moderate/high. In general, increased strength muscle was reported in the studies.

CONCLUSION

This systematic review suggests that WBVE might promote desirable neuromuscular responses in healthy elderly. However, it is necessary to perform further studies to reinforce the reported findings.IMPLICATIONS FOR REHABILITATIONThe reduction in lean mass and consequent reduction in muscle strength are present in healthy elderly people and the whole-body vibration exercise can reduce or alleviate these symptoms caused by the geriatric syndrome.Whole-body vibration exercise is a training modality that increases neuromuscular activation and muscle strength.Surface electromyography is a useful tool for the evaluation of the neuromuscular activation of the muscle fibers.

Lower Limb Exoskeleton Sensors: State-of-the-Art

Due to the ever-increasing proportion of older people in the total population and the growing awareness of the importance of protecting workers against physical overload during long-time hard work, the idea of supporting exoskeletons progressed from high-tech fiction to almost commercialized products within the last six decades. Sensors, as part of the perception layer, play a crucial role in enhancing the functionality of exoskeletons by providing as accurate real-time data as possible to generate reliable input data for the control layer. The result of the processed sensor data is the information about current limb position, movement intension, and needed support. With the help of this review article, we want to clarify which criteria for sensors used in exoskeletons are important and how standard sensor types, such as kinematic and kinetic sensors, are used in lower limb exoskeletons. We also want to outline the possibilities and limitations of special medical signal sensors detecting, e.g., brain or muscle signals to improve data perception at the human-machine interface. A topic-based literature and product research was done to gain the best possible overview of the newest developments, research results, and products in the field. The paper provides an extensive overview of sensor criteria that need to be considered for the use of sensors in exoskeletons, as well as a collection of sensors and their placement used in current exoskeleton products. Additionally, the article points out several types of sensors detecting physiological or environmental signals that might be beneficial for future exoskeleton developments.

Effects of Kinesio taping on calf muscle fatigue in college female athletes: A randomized controlled trial

BACKGROUND

Fatigue is a common phenomenon encountered by athletes in ordinary life and sports. Fatigue results in decreased muscle strength, balance, agility, and an increased risk of injury, which together results in hampered sports performance. Several studies have examined the effects of Kinesio Tape (KT) application on muscle fatigue however, contradictory findings are reported. This study aimed to examine the effects of the application of KT on calf muscle fatigability.

METHODS

A three-arm parallel pretest-post-test experimental design was used. Forty-five collegiate female athletes (mean age of 20.57 years) were randomly assigned to three groups. For the experimental group, KT with 50% tension; for the sham group, KT without any tension; and for the placebo group, rigid tape without any tension was applied. The number of heel rises (HRn) was measured before and after taping in the three groups, using Haberometer and Metronome. The tapes were applied in the Y shape to the calf muscle region.

RESULTS

In the experimental group: The HRn significantly increased by 18.76 % (P = .000) after applying KT. In the sham and placebo groups: There was no change in HRn before and after Taping (P > .05).

CONCLUSION

Y-shaped application of KT with 50% tension over the calf muscle region is effective in reducing its fatigability.

A Systematic Review of Sensor Fusion Methods Using Peripheral Bio-Signals for Human Intention Decoding

Humans learn about the environment by interacting with it. With an increasing use of computer and virtual applications as well as robotic and prosthetic devices, there is a need for intuitive interfaces that allow the user to have an embodied interaction with the devices they are controlling. Muscle-machine interfaces can provide an intuitive solution by decoding human intentions utilizing myoelectric activations. There are several different methods that can be utilized to develop MuMIs, such as electromyography, ultrasonography, mechanomyography, and near-infrared spectroscopy. In this paper, we analyze the advantages and disadvantages of different myography methods by reviewing myography fusion methods. In a systematic review following the PRISMA guidelines, we identify and analyze studies that employ the fusion of different sensors and myography techniques, while also considering interface wearability. We also explore the properties of different fusion techniques in decoding user intentions. The fusion of electromyography, ultrasonography, mechanomyography, and near-infrared spectroscopy as well as other sensing such as inertial measurement units and optical sensing methods has been of continuous interest over the last decade with the main focus decoding the user intention for the upper limb. From the systematic review, it can be concluded that the fusion of two or more myography methods leads to a better performance for the decoding of a user's intention. Furthermore, promising sensor fusion techniques for different applications were also identified based on the existing literature.

Toward Soft Wearable Strain Sensors for Muscle Activity Monitoring

The force-generating capacity of skeletal muscle is an important metric in the evaluation and diagnosis of musculoskeletal health. Measuring changes in muscle force exertion is essential for tracking the progress of athletes during training, for evaluating patients’ recovery after muscle injury, and also for assisting the diagnosis of conditions such as muscular dystrophy, multiple sclerosis, or Parkinson’s disease. Traditional hardware for strength evaluation requires technical training for operation, generates discrete time points for muscle assessment, and is implemented in controlled settings. The ability to continuously monitor muscle force without restricting the range of motion or adapting the exercise protocol to suit specific hardware would allow for a richer dataset that can help unlock critical features of muscle health and strength evaluation. In this paper, we employ wearable, ultra-sensitive soft strain sensors for tracking changes in muscle deformation during contractions. We demonstrate the sensors’ sensitivity to isometric contractions, as well as the sensors’ capacity to track changes in peak torque over the course of an isokinetic fatiguing protocol for the knee extensors. The wearable soft system was able to efficiently estimate peak joint torque reduction caused by muscle fatigue (mean NRMSE = 0.15±0.03).

Design Validation of a Low-Cost EMG Sensor Compared to a Commercial-Based System for Measuring Muscle Activity and Fatigue

Electromyography (EMG) sensors have been used for measuring muscle signals and for diagnosing neuromuscular disease. Available commercial EMG sensor are expensive and not easily available for individuals. The aim of the study is to validate our designed low-cost sensor against a well-known commercial system for measuring muscle activity and fatigue assessment. The evaluation of the designed system was done through a series of dynamic exercises performed by volunteers. Our low-cost EMG sensor and the commercially available system were placed on the vastus lateralis muscle to concurrently record the signal in a maximum voluntary contraction (MVC). The signal analysis was done using two validation indicators: Spearman's correlation, and intra-class cross correlation on SPSS 26.0 version. For the muscle fatigue assessment, the root mean square (RMS), mean absolute value (MAV) and mean frequency (MNF) indicators were used. The results at the peak and mean level muscle contraction intensity were computed. The relative agreement for the two systems was excellent at peak level muscle contraction range (ICC 0.74-0.92), average 0.83 and mean level muscle contraction intensity range (ICC 0.65-0.85) with an average of 0.74. The Spearman's correlation average was 0.76 with the range of (0.71-0.85) at peak level contraction, whiles the mean level contraction average was 0.71 at a range of (0.62-0.81). In determining muscle fatigue, the RMS and MAV showed increasing values in the time domain, while the MEF decreased in the frequency domain. Overall, the results indicated a good to excellent agreement of the two systems and confirmed the reliability of our design. The low-cost sensor also proved to be suitable for muscle fatigue assessment. Our designed system can therefore be implemented for rehabilitation, sports science, and ergonomics.

Predicting individual muscle fatigue tolerance by resting-state EEG brain network

OBJECTIVE

Exercise-induced muscle fatigue is a complex physiological phenomenon involving the central and peripheral nervous systems, and fatigue tolerance varies across individuals. Various studies have emphasized the close relationships between muscle fatigue and the brain. However, the relationships between the resting-state electroencephalogram (rsEEG) brain network and individual muscle fatigue tolerance remain unexplored.

APPROACH

Eighteen elite water polo athletes took part in our experiment. Five-minute before- and after-fatigue-exercise rsEEG and fatiguing task (i.e., elbow flexion and extension) electromyography (EMG) data were recorded. Based on the graph theory, we constructed the before- and after-task rsEEG coherence network and compared the network differences between them. Then, the correlation between the before-fatigue rsEEG network properties and the EMG fatigue indexes when a subject cannot keep on exercising anymore was profiled. Finally, a prediction model based on the before-fatigue rsEEG network properties was established to predict fatigue tolerance.

MAIN RESULTS

Results of this study revealed the significant differences between the before- and after-exercise rsEEG brain network and found significant high correlations between before-exercise rsEEG network properties in the beta band and individual muscle fatigue tolerance. Finally, an efficient support vector regression (SVR) model based on the before-exercise rsEEG network properties in the beta band was constructed and achieved the accurate prediction of individual fatigue tolerance. Similar results were also revealed on another thirty-subject swimmer data set further demonstrating the reliability of predicting fatigue tolerance based on the rsEEG network.

SIGNIFICANCE

Our study investigates the relationship between the rsEEG brain network and individual muscle fatigue tolerance and provides a potential objective physiological biomarker for tolerance prediction and the regulation of muscle fatigue.

Toward a wearable monitor of local muscle fatigue during electrical muscle stimulation using tissue Doppler imaging

Electrical muscle stimulation (EMS) is widely used in rehabilitation and athletic training to generate involuntary muscle contractions. However, EMS leads to rapid muscle fatigue, limiting the force a muscle can produce during prolonged use. Currently available methods to monitor localized muscle fatigue and recovery are generally not compatible with EMS. The purpose of this study was to examine whether Doppler ultrasound imaging can assess changes in stimulated muscle twitches that are related to muscle fatigue from electrical stimulation. We stimulated five isometric muscle twitches in the medial and lateral gastrocnemius of 13 healthy subjects before and after a fatiguing EMS protocol. Tissue Doppler imaging of the medial gastrocnemius recorded muscle tissue velocities during each twitch. Features of the average muscle tissue velocity waveforms changed immediately after the fatiguing stimulation protocol (peak velocity: -38%, p = .022; time-to-zero velocity: +8%, p = .050). As the fatigued muscle recovered, the features of the average tissue velocity waveforms showed a return towards their baseline values similar to that of the normalized ankle torque. We also found that features of the average tissue velocity waveform could significantly predict the ankle twitch torque for each participant (R2 = 0.255-0.849, p < .001). Our results provide evidence that Doppler ultrasound imaging can detect changes in muscle tissue during isometric muscle twitch that are related to muscle fatigue, fatigue recovery, and the generated joint torque. Tissue Doppler imaging may be a feasible method to monitor localized muscle fatigue during EMS in a wearable device.

[Research on muscle fatigue recognition model based on improved wavelet denoising and long short-term memory]

The automatic recognition technology of muscle fatigue has widespread application in the field of kinesiology and rehabilitation medicine. In this paper, we used surface electromyography (sEMG) to study the recognition of leg muscle fatigue during circuit resistance training. The purpose of this study was to solve the problem that the sEMG signals have a lot of noise interference and the recognition accuracy of the existing muscle fatigue recognition model is not high enough. First, we proposed an improved wavelet threshold function denoising algorithm to denoise the sEMG signal. Then, we build a muscle fatigue state recognition model based on long short-term memory (LSTM), and used the Holdout method to evaluate the performance of the model. Finally, the denoising effect of the improved wavelet threshold function denoising method proposed in this paper was compared with the denoising effect of the traditional wavelet threshold denoising method. We compared the performance of the proposed muscle fatigue recognition model with that of particle swarm optimization support vector machine (PSO-SVM) and convolutional neural network (CNN). The results showed that the new wavelet threshold function had better denoising performance than hard and soft threshold functions. The accuracy of LSTM network model in identifying muscle fatigue was 4.89% and 2.47% higher than that of PSO-SVM and CNN, respectively. The sEMG signal denoising method and muscle fatigue recognition model proposed in this paper have important implications for monitoring muscle fatigue during rehabilitation training and exercise.

Recent Trends and Practices Toward Assessment and Rehabilitation of Neurodegenerative Disorders: Insights From Human Gait

The study of human movement and biomechanics forms an integral part of various clinical assessments and provides valuable information toward diagnosing neurodegenerative disorders where the motor symptoms predominate. Conventional gait and postural balance analysis techniques like force platforms, motion cameras, etc., are complex, expensive equipment requiring specialist operators, thereby posing a significant challenge toward translation to the clinics. The current manuscript presents an overview and relevant literature summarizing the umbrella of factors associated with neurodegenerative disorder management: from the pathogenesis and motor symptoms of commonly occurring disorders to current alternate practices toward its quantification and mitigation. This article reviews recent advances in technologies and methodologies for managing important neurodegenerative gait and balance disorders, emphasizing assessment and rehabilitation/assistance. The review predominantly focuses on the application of inertial sensors toward various facets of gait analysis, including event detection, spatiotemporal gait parameter measurement, estimation of joint kinematics, and postural balance analysis. In addition, the use of other sensing principles such as foot-force interaction measurement, electromyography techniques, electrogoniometers, force-myography, ultrasonic, piezoelectric, and microphone sensors has also been explored. The review also examined the commercially available wearable gait analysis systems. Additionally, a summary of recent progress in therapeutic approaches, viz., wearables, virtual reality (VR), and phytochemical compounds, has also been presented, explicitly targeting the neuro-motor and functional impairments associated with these disorders. Efforts toward therapeutic and functional rehabilitation through VR, wearables, and different phytochemical compounds are presented using recent examples of research across the commonly occurring neurodegenerative conditions [viz., Parkinson's disease (PD), Alzheimer's disease (AD), multiple sclerosis, Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS)]. Studies exploring the potential role of Phyto compounds in mitigating commonly associated neurodegenerative pathologies such as mitochondrial dysfunction, α-synuclein accumulation, imbalance of free radicals, etc., are also discussed in breadth. Parameters such as joint angles, plantar pressure, and muscle force can be measured using portable and wearable sensors like accelerometers, gyroscopes, footswitches, force sensors, etc. Kinetic foot insoles and inertial measurement tools are widely explored for studying kinematic and kinetic parameters associated with gait. With advanced correlation algorithms and extensive RCTs, such measurement techniques can be an effective clinical and home-based monitoring and rehabilitation tool for neuro-impaired gait. As evident from the present literature, although the vast majority of works reported are not clinically and extensively validated to derive a firm conclusion about the effectiveness of such techniques, wearable sensors present a promising impact toward dealing with neurodegenerative motor disorders.

Wearable Sensor for Forearm Motion Detection Using a Carbon-Based Conductive Layer-Polymer Composite Film

In this study, we developed a fabrication method for a bracelet-type wearable sensor to detect four motions of the forearm by using a carbon-based conductive layer-polymer composite film. The integral material used for the composite film is a polyethylene terephthalate polymer film with a conductive layer composed of a carbon paste. It is capable of detecting the resistance variations corresponding to the flexion changes of the surface of the body due to muscle contraction and relaxation. To effectively detect the surface resistance variations of the film, a small sensor module composed of mechanical parts mounted on the film was designed and fabricated. A subject wore the bracelet sensor, consisting of three such sensor modules, on their forearm. The surface resistance of the film varied corresponding to the flexion change of the contact area between the forearm and the sensor modules. The surface resistance variations of the film were converted to voltage signals and used for motion detection. The results demonstrate that the thin bracelet-type wearable sensor, which is comfortable to wear and easily applicable, successfully detected each motion with high accuracy.

sEMG-Based Natural Control Interface for a Variable Stiffness Transradial Hand Prosthesis

We propose, implement, and evaluate a natural human-machine control interface for a variable stiffness transradial hand prosthesis that achieves tele-impedance control through surface electromyography (sEMG) signals. This interface, together with variable stiffness actuation (VSA), enables an amputee to modulate the impedance of the prosthetic limb to properly match the requirements of a task while performing activities of daily living (ADL). Both the desired position and stiffness references are estimated through sEMG signals and used to control the VSA hand prosthesis. In particular, regulation of hand impedance is managed through the impedance measurements of the intact upper arm; this control takes place naturally and automatically as the amputee interacts with the environment, while the position of the hand prosthesis is regulated intentionally by the amputee through the estimated position of the shoulder. The proposed approach is advantageous since the impedance regulation takes place naturally without requiring amputees' attention and diminishing their functional capability. Consequently, the proposed interface is easy to use, does not require long training periods or interferes with the control of intact body segments. This control approach is evaluated through human subject experiments conducted over able volunteers where adequate estimation of references and independent control of position and stiffness are demonstrated.

Shoulder electromyography-based indicators to assess manifestation of muscle fatigue during laboratory-simulated manual handling task

Muscle fatigue is a risk factor for developing shoulder musculoskeletal disorders. The aim of this study was to identify shoulder electromyographic indicators that are most indicative of muscle fatigue during a laboratory simulated manual handling task. Thirty-two participants were equipped with electromyographic electrodes on 10 shoulder muscles and moved boxes for 45-minutes. The modified rate of perceived exertion (mRPE) was assessed every 5-minutes and multivariate linear regressions were performed between myoelectric manifestation of fatigue (MMF) and the mRPE scores. During a manual handling task representative of industry working conditions, spectral entropy, median frequency, and mobility were the electromyographic indicators that explained the largest percentage of the mRPE. Overall, the deltoids, biceps and upper trapezius were the muscles that most often showed significant changes over time in their electromyographic indicators. The combination of these three indicators may improve the accuracy for the assessment of MMF during manual handling. Practitioner Summary: To date, muscle fatigue has primarily been assessed during tasks done to exhaustion, which are not representative of typical working conditions. During a manual handling task representative of industry working conditions, EMG-derived spectral entropy, and median frequency, both extracted from time-frequency analysis, and mobility extracted from time domain, were the best indicators of the manifestation of muscle fatigue.

An exploratory study comparing three work/rest schedules during simulated repetitive precision work

The pattern of work and rest can influence both physical fatigue and task performance in manual operations. However, there is relatively limited evidence regarding the influences of specific work/rest schedules in tasks requiring high repetitiveness and precision demands, along with relatively low exertion levels. Eighteen participants completed an exploratory study that simulated such tasks, to compare the effects of three distinct work/rest schedules (i.e. short frequent [short] and long infrequent breaks [long], and a self-selected schedule) on muscle fatigue, task performance (in terms of accuracy and speed), and preference. Schedules with long or self-selected breaks generally induced less muscle fatigue, compared with the short break condition. Participants preferred the self-selected condition the most and the long-break condition the least. The different schedules tested did not influence task performance. A self-selected schedule may be beneficial for repetitive precision task, to achieve a balance across muscle fatigue, task performance, and individual preference. Practitioner summary: Influences of three work/rest schedules (i.e. short and long breaks, and a self-selected schedule) on fatigue, performance, and preference were explored during repetitive precision tasks. Schedules with long or self-selected breaks induced less muscle fatigue and none of the three schedules influenced performance. A self-selected schedule was the most preferred.

An Ultra-Low Power Surface EMG Sensor for Wearable Biometric and Medical Applications

In recent years, the surface electromyography (EMG) signal has received a lot of attention. EMG signals are used to analyze muscle activity or to evaluate a patient's muscle status. However, commercial surface EMG systems are expensive and have high power consumption. Therefore, the purpose of this paper is to implement a surface EMG acquisition system that supports high sampling and ultra-low power consumption measurement. This work analyzes and optimizes each part of the EMG acquisition circuit and combines an MCU with BLE. Regarding the MCU power saving method, the system uses two different frequency MCU clock sources and we proposed a ping-pong buffer as the memory architecture to achieve the best power saving effect. The measured surface EMG signal samples can be forwarded immediately to the host for further processing and additional application. The results show that the average current of the proposed architecture can be reduced by 92.72% compared with commercial devices, and the battery life is 9.057 times longer. In addition, the correlation coefficients were up to 99.5%, which represents a high relative agreement between the commercial and the proposed system.

The Effect of Fatigue on Wheelchair Users' Upper Limb Muscle Coordination Patterns in Time-Frequency and Principal Component Analysis

An assessment of shoulder muscle coordination patterns is important to gain insight into muscle fatigue during wheelchair propulsion. The objective of the present study was to quantify muscle coordination changes over time during fatiguing wheelchair propulsion, as the muscles go through distinct levels of fatigue, a) non-fatigued, b) transiting to fatigue and c) fatigued to exhaustion. We recorded surface electromyography (sEMG) signals of the anterior deltoid (AD), middle deltoid (MD), posterior deltoid (PD), infraspinatus (IS), upper trapezius (UT), sternal head of the pectoralis major (PM), biceps brachii (BB), and triceps brachii (TB) during a wheelchair incremental exercise test. Nine wheelchair users with a diagnosis of spina bifida or T6-T12 spinal cord injury volunteered for the study. Oxygen uptake and SmartWheel kinetic parameters were also recorded during the test. EMG signals were processed by wavelet and principal component analysis (PCA), allowing for an assessment of how wheelchair users modify their muscle coordination patterns over time. Analyses of covariance (ANCOVA) were conducted to identify the main effect of fatigue levels on muscle coordination patterns by controlling for the effect of increased workload as covariate. A significant effect of fatigue levels on the PC1 and PC3 loading scores was found after controlling for the effect of increasing workloads (with both cases). In addition, PC3 reflects the most dominant fatigue effect on muscle coordination patterns which are not affected by increased ergometer workload. PC3 indicates muscle imbalance when muscles are fully fatigued and muscle co-contraction when muscles are beginning to fatigue. We conclude that fatigue-related changes in neuromuscular activity during wheelchair propulsion contribute to muscle imbalance and reflect a strategy of stiffening the shoulder joint.

Detecting subtle yet fast skeletal muscle contractions with ultrasoft and durable graphene-based cellular materials

Human bodily movements are primarily controlled by the contractions of skeletal muscles. Unlike joint or skeletal movements that are generally performed in the large displacement range, the contractions of the skeletal muscles that underpin these movements are subtle in intensity yet high in frequency. This subtlety of movement makes it a formidable challenge to develop wearable and durable soft materials to electrically monitor such motions with high fidelity for the purpose of, for example, muscle/neuromuscular disease diagnosis. Here we report that an intrinsically fragile ultralow-density graphene-based cellular monolith sandwiched between silicone rubbers can exhibit a highly effective stress and strain transfer mechanism at its interface with the rubber, with a remarkable improvement in stretchability (>100%). In particular, this hybrid also exhibits a highly sensitive, broadband-frequency electrical response (up to 180 Hz) for a wide range of strains. By correlating the mechanical signal of muscle movements obtained from this hybrid material with electromyography, we demonstrate that the strain sensor based on this hybrid material may provide a new, soft and wearable mechanomyography approach for real-time monitoring of complex neuromuscular–skeletal interactions in a broad range of healthcare and human–machine interface applications. This work also provides a new architecture-enabled functional soft material platform for wearable electronics.

A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States

Physical exercise contributes to the success of rehabilitation programs and rehabilitation processes assisted through social robots. However, the amount and intensity of exercise needed to obtain positive results are unknown. Several considerations must be kept in mind for its implementation in rehabilitation, as monitoring of patients' intensity, which is essential to avoid extreme fatigue conditions, may cause physical and physiological complications. The use of machine learning models has been implemented in fatigue management, but is limited in practice due to the lack of understanding of how an individual's performance deteriorates with fatigue; this can vary based on physical exercise, environment, and the individual's characteristics. As a first step, this paper lays the foundation for a data analytic approach to managing fatigue in walking tasks. The proposed framework establishes the criteria for a feature and machine learning algorithm selection for fatigue management, classifying four fatigue diagnoses states. Based on the proposed framework and the classifier implemented, the random forest model presented the best performance with an average accuracy of ≥98% and F-score of ≥93%. This model was comprised of ≤16 features. In addition, the prediction performance was analyzed by limiting the sensors used from four IMUs to two or even one IMU with an overall performance of ≥88%.

Action Recognition of Lower Limbs Based on Surface Electromyography Weighted Feature Method

To improve the recognition rate of lower limb actions based on surface electromyography (sEMG), an effective weighted feature method is proposed, and an improved genetic algorithm support vector machine (IGA-SVM) is designed in this paper. First, for the problem of high feature redundancy and low discrimination in the surface electromyography feature extraction process, the weighted feature method is proposed based on the correlation between muscles and actions. Second, to solve the problem of the genetic algorithm selection operator easily falling into a local optimum solution, the improved genetic algorithm-support vector machine is designed by championship with sorting method. Finally, the proposed method is used to recognize six types of lower limb actions designed, and the average recognition rate reaches 94.75%. Experimental results indicate that the proposed method has definite potentiality in lower limb action recognition.

Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise

Physical exercise (PE) has become an essential tool for different rehabilitation programs. High-intensity exercises (HIEs) have been demonstrated to provide better results in general health conditions, compared with low and moderate-intensity exercises. In this context, monitoring of a patients' condition is essential to avoid extreme fatigue conditions, which may cause physical and physiological complications. Different methods have been proposed for fatigue estimation, such as: monitoring the subject's physiological parameters and subjective scales. However, there is still a need for practical procedures that provide an objective estimation, especially for HIEs. In this work, considering that the sit-to-stand (STS) exercise is one of the most implemented in physical rehabilitation, a computational model for estimating fatigue during this exercise is proposed. A study with 60 healthy volunteers was carried out to obtain a data set to develop and evaluate the proposed model. According to the literature, this model estimates three fatigue conditions (low, moderate, and high) by monitoring 32 STS kinematic features and the heart rate from a set of ambulatory sensors (Kinect and Zephyr sensors). Results show that a random forest model composed of 60 sub-classifiers presented an accuracy of 82.5% in the classification task. Moreover, results suggest that the movement of the upper body part is the most relevant feature for fatigue estimation. Movements of the lower body and the heart rate also contribute to essential information for identifying the fatigue condition. This work presents a promising tool for physical rehabilitation.

Nanomaterials-patterned flexible electrodes for wearable health monitoring: a review

ABSTRACT

Electrodes fabricated on a flexible substrate are a revolutionary development in wearable health monitoring due to their lightweight, breathability, comfort, and flexibility to conform to the curvilinear body shape. Different metallic thin-film and plastic-based substrates lack comfort for long-term monitoring applications. However, the insulating nature of different polymer, fiber, and textile substrates requires the deposition of conductive materials to render interactive functionality to substrates. Besides, the high porosity and flexibility of fiber and textile substrates pose a great challenge for the homogenous deposition of active materials. Printing is an excellent process to produce a flexible conductive textile electrode for wearable health monitoring applications due to its low cost and scalability. This article critically reviews the current state of the art of different textile architectures as a substrate for the deposition of conductive nanomaterials. Furthermore, recent progress in various printing processes of nanomaterials, challenges of printing nanomaterials on textiles, and their health monitoring applications are described systematically.

Non-obstructive monitoring of muscle fatigue for low intensity dynamic exercise with infrared thermography technique

Surface electromyography (sEMG) has been widely used in evaluating muscle fatigue among athletes where electrodes are attached on the skin during the activity. Recently, infrared thermography technique (IRT) has gain popularity and shown to be another preferred method in monitoring and predicting muscle fatigue non-obstructively. This paper investigates the correlation between surface temperature and muscle activation parameters obtained using both IRT and sEMG methods simultaneously. Twenty healthy subjects were required to perform a repetitive calf raise exercise with various loads attached around their ankle for 3 min to induce fatigue on the targeted gastrocnemius muscles. Average temperature and temperature difference information were extracted from thermal images, while root mean square (RMS) and median frequency (MF) were extracted from sEMG signals. Spearman statistical analysis performed shows that there is a significant correlation between average temperature with RMS and between temperature difference with MF values at p<0.05. While ANOVA test conducted shows that there is significant impact of loads on RMS and MF where F=12.61 and 3.59, respectively, at p< 0.05. This study suggested that skin surface temperature can be utilized in monitoring and predicting muscle fatigue in low intensity dynamic exercise and can be extended to other dynamic exercises.

Demystifying the spontaneous phenomena of motor hyperexcitability

Possessing a discrete functional repertoire, the anterior horn cell can be in one of two electrophysiological states: on or off. Usually under tight regulatory control by the central nervous system, a hierarchical network of these specialist neurons ensures muscular strength is coordinated, gradated and adaptable. However, spontaneous activation of these cells and their axons can result in abnormal muscular twitching. The muscular twitch is the common building block of several distinct clinical patterns, namely fasciculation, myokymia and neuromyotonia. When attempting to distinguish these entities electromyographically, their unique temporal and morphological profiles must be appreciated. Detection and quantification of burst duration, firing frequency, multiplet patterns and amplitude are informative. A common feature is their persistence during sleep. In this review, we explain the accepted terminology used to describe the spontaneous phenomena of motor hyperexcitability, highlighting potential pitfalls amidst a bemusing and complex collection of overlapping terms. We outline the relevance of these findings within the context of disease, principally amyotrophic lateral sclerosis, Isaacs syndrome and Morvan syndrome. In addition, we highlight the use of high-density surface electromyography, suggesting that more widespread use of this non-invasive technique is likely to provide an enhanced understanding of these motor hyperexcitability syndromes.

The Effects of Inter-Set Recovery Time on Explosive Power, Electromyography Activity, and Tissue Oxygenation during Plyometric Training

Performing continuous sets to failure is fatiguing during the plyometric training. Cluster sets have been used to redistribute total rest time to create short frequent sets so that muscle fatigue can be avoided. The purpose of the study was to investigate the effects of inter-set recovery time on lower extremity explosive power, neuromuscular activity, and tissue oxygenation during plyometric exercise and recovery. An integrated assessment of explosive power, muscle electrical activity, and tissue oxygenation was adopted in the present study to help understand local muscle metabolism and fatigue during plyometric exercise and recovery. Ten university male basketball players participated in this study. Subjects performed 4 groups of exercise, each group comprised of 3 sets of jumps: 1, 2, 3, or 5 min. Surface electromyography (sEMG) signals were collected from 9 lower extremity muscles; near-infrared spectroscopy (NIRS) was recorded on vastus lateralis; mechanical data during plyometric exercise were collected from a force plate. No significant differences among sets and among groups were found regarding explosive power, jump height, EMG intensity, mean power frequency, the rate of tissue saturation index, and HbO2 changes between baseline and recovery. The current study has shown no muscular fatigue induced during the 4 groups of exercise. The results of this study may help inform recommendations concerning the recovery time during plyometric exercises at low loads (30% 1 RM).

Relationship between gait quality measures and modular neuromuscular control parameters in chronic post-stroke individuals

Background

Recent evidence suggests that disinhibition and/or hyperexcitation of the brainstem descending pathways and intraspinal motor network diffuse spastic synergistic activation patterns after stroke. This results in simplified or merged muscle sets (i.e., muscle modules or synergies) compared to non-impaired individuals and this leads to poor walking performance. However, the relations of how these neuromuscular deficits influence gait quality (e.g., symmetry or natural walking patterns) are still unclear. The objective of this exploratory study was to investigate the relations of modular neuromuscular framework and gait quality measures in chronic stroke individuals.

Methods

Sixteen chronic post-stroke individuals participated in this study. Full lower body three-dimensional kinematics and electromyography (EMG) were concurrently measured during overground walking at a comfortable speed. We first examined changes in gait quality measures across the number of muscle modules using linear regression model. Then, a stepwise multiple regression was used to investigate the optimal combination of the neuromuscular parameters that associates with gait quality measures.

Results

We observed that subjects who had a lower number of muscle modules revealed reduced function (i.e., speed) and greater asymmetry in the kinematic parameters including limb length, footpath area, knee flexion/extension, and hip abduction/adduction (all p < 0.05). We also found that the combination of input variables from the modular neuromuscular control framework significantly associated with gait quality measures (average \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}^{2}=42.5\mathrm{\%}$$\end{document}R2=42.5%). Those variables included variability accounted for (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$VAF$$\end{document}VAF) information from the muscle modules and area under the EMG envelope curves of the quadriceps (i.e., rectus femoris and vastus lateralis) and tibialis anterior muscles.

Conclusions

The results suggest that there exists a significant correlation between the neuromuscular control framework and the gait quality measures. This study helps to understand the underlying mechanism of disturbances in gait quality and provides insight for a more comprehensive outcome measure to assess gait impairment after stroke.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-021-00860-0.

Overuse-Related Injuries of the Musculoskeletal System: Systematic Review and Quantitative Synthesis of Injuries, Locations, Risk Factors and Assessment Techniques

Overuse-related musculoskeletal injuries mostly affect athletes, especially if involved in preseason conditioning, and military populations; they may also occur, however, when pathological or biological conditions render the musculoskeletal system inadequate to cope with a mechanical load, even if moderate. Within the MOVIDA (Motor function and Vitamin D: toolkit for risk Assessment and prediction) Project, funded by the Italian Ministry of Defence, a systematic review of the literature was conducted to support the development of a transportable toolkit (instrumentation, protocols and reference/risk thresholds) to help characterize the risk of overuse-related musculoskeletal injury. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach was used to analyze Review papers indexed in PubMed and published in the period 2010 to 2020. The search focused on stress (overuse) fracture or injuries, and muscle fatigue in the lower limbs in association with functional (biomechanical) or biological biomarkers. A total of 225 Review papers were retrieved: 115 were found eligible for full text analysis and led to another 141 research papers derived from a second-level search. A total of 183 papers were finally chosen for analysis: 74 were classified as introductory to the topics, 109 were analyzed in depth. Qualitative and, wherever possible, quantitative syntheses were carried out with respect to the literature review process and quality, injury epidemiology (type and location of injuries, and investigated populations), risk factors, assessment techniques and assessment protocols.

Fatigue effect on cross-talk in mechanomyography signals of extensor and flexor forearm muscles during maximal voluntary isometric contractions

OBJECTIVE

This paper presents the analyses of the fatigue effect on the cross-talk in mechanomyography (MMG) signals of extensor and flexor forearm muscles during pre- and post-fatigue maximum voluntary isometric contraction (MVIC).

METHODS

Twenty male participants performed repetitive submaximal (60% MVIC) grip muscle contractions to induce muscle fatigue and the results were analyzed during the pre- and post-fatigue MVIC. MMG signals were recorded on the extensor digitorum (ED), extensor carpi radialis longus (ECRL), flexor digitorum superficialis (FDS) and flexor carpi radialis (FCR) muscles. The cross-correlation coefficient was used to quantify the cross-talk values in forearm muscle pairs (MP1, MP2, MP3, MP4, MP5 and MP6). In addition, the MMG RMS and MMG MPF were calculated to determine force production and muscle fatigue level, respectively.

RESULTS

The fatigue effect significantly increased the cross-talk values in forearm muscle pairs except for MP2 and MP6. While the MMG RMS and MMG MPF significantly decreased (p<0.05) based on the examination of the mean differences from pre- and post-fatigue MVIC.

CONCLUSION

The presented results can be used as a reference for further investigation of cross-talk on the fatigue assessment of extensor and flexor muscles' mechanic.

Sustained fatigue assessment during isometric exercises with time-domain near infrared spectroscopy and surface electromyography signals

The effect of sustained fatigue during an upper limb isometric exercise is presented to investigate a group of healthy subjects with simultaneous time-domain (TD) NIRS and surface electromyography (sEMG) recordings on the deltoid lateralis muscle. The aim of the work was to understand which TD-NIRS parameters can be used as descriptors for sustained muscular fatigue, focusing on the slow phase of this process and using median frequency (MF) computed from sEMG as gold standard measure. It was found that oxygen saturation and deoxy-hemoglobin are slightly better descriptors of sustained fatigue, than oxy-hemoglobin, since they showed a higher correlation with MF, while total-hemoglobin correlation with MF was lower.

Wheelchair propulsion fatigue thresholds in electromyographic and ventilatory testing

STUDY DESIGN

Qualitative study.

OBJECTIVE

The objective of the present study are physiological processes occurring when the intensity of manual wheelchair propulsion approaches levels causing muscular fatigue. In particular, we set out to (1) detect the electromyographic (EMG) and ventilatory fatigue threshold during a single wheelchair incremental test, (2) examine the relationship between EMG threshold (EMGT) and ventilatory threshold (VT), and (3) detect the EMG threshold differences between the propulsive and recovery muscle synergies.

SETTING

Biomechanics laboratory at the University of Alberta, Canada.

METHODS

Oxygen uptake and EMG signals from ten wheelchair users (seven males and three females) were recorded as they were each performing an incremental propulsion bout in their own wheelchairs on a wheelchair ergometer. The V-slope method was used to identify the VT, and the EMGT of each of the eight muscles (anterior deltoid, middle deltoid, posterior deltoid, infraspinatus, upper trapezius, sternal head of pectoralis major, biceps brachii, and triceps brachii) was determined using the bisegmental linear regression method.

RESULTS

For each participant, we were able to determine the EMGT and VT from a single incremental wheelchair propulsion bout. EMGT stands in good agreement with VT, and there was a high similarity in EMGT between push and recovery muscles (intraclass correlation coefficient = 0.91).

CONCLUSION

The EMG fatigue threshold method can serve as a valid and reliable tool for identifying the onset of muscular fatigue during wheelchair propulsion, thus providing a foundation for automated muscle fatigue detection/prediction in wearable technology.

Surface EMG in Clinical Assessment and Neurorehabilitation: Barriers Limiting Its Use

This article addresses the potential clinical value of techniques based on surface electromyography (sEMG) in rehabilitation medicine with specific focus on neurorehabilitation. Applications in exercise and sport pathophysiology, in movement analysis, in ergonomics and occupational medicine, and in a number of related fields are also considered. The contrast between the extensive scientific literature in these fields and the limited clinical applications is discussed. The "barriers" between research findings and their application are very broad, and are longstanding, cultural, educational, and technical. Cultural barriers relate to the general acceptance and use of the concept of objective measurement in a clinical setting and its role in promoting Evidence Based Medicine. Wide differences between countries exist in appropriate training in the use of such quantitative measurements in general, and in electrical measurements in particular. These differences are manifest in training programs, in degrees granted, and in academic/research career opportunities. Educational barriers are related to the background in mathematics and physics for rehabilitation clinicians, leading to insufficient basic concepts of signal interpretation, as well as to the lack of a common language with rehabilitation engineers. Technical barriers are being overcome progressively, but progress is still impacted by the lack of user-friendly equipment, insufficient market demand, gadget-like devices, relatively high equipment price and a pervasive lack of interest by manufacturers. Despite the recommendations provided by the 20-year old EU project on "Surface EMG for Non-Invasive Assessment of Muscles (SENIAM)," real international standards are still missing and there is minimal international pressure for developing and applying such standards. The need for change in training and teaching is increasingly felt in the academic world, but is much less perceived in the health delivery system and clinical environments. The rapid technological progress in the fields of sensor and measurement technology (including sEMG), assistive devices, and robotic rehabilitation, has not been driven by clinical demands. Our assertion is that the most important and urgent interventions concern enhanced education, more effective technology transfer, and increased academic opportunities for physiotherapists, occupational therapists, and kinesiologists.

Real-Time Forecasting of sEMG Features for Trunk Muscle Fatigue Using Machine Learning

OBJECTIVE

Several features of the surface electromyography (sEMG) signal are related to muscle activity and fatigue. However, the time-evolution of these features are non-stationary and vary between subjects. The aim of this study is to investigate the use of adaptive algorithms to forecast sEMG feature of the trunk muscles.

METHODS

Shallow models and a deep convolutional neural network (CNN) were used to simultaneously learn and forecast 5 common sEMG features in real-time to provide tailored predictions. This was investigated for: up to a 25 second horizon; for 14 different muscles in the trunk; across 13 healthy subjects; while they were performing various exercises.

RESULTS

The CNN was able to forecast 25 seconds ahead of time, with 6.88% mean absolute percentage error and 3.72% standard deviation of absolute percentage error, across all the features. Moreover, the CNN outperforms the best shallow model in terms of a figure of merit combining accuracy and precision by at least 30% for all the 5 features.

CONCLUSION

Even though the sEMG features are non-stationary and vary between subjects, adaptive learning and forecasting, especially using CNNs, can provide accurate and precise forecasts across a range of physical activities.

SIGNIFICANCE

The proposed models provide the groundwork for a wearable device which can forecast muscle fatigue in the trunk, so as to potentially prevent low back pain. Additionally, the explicit real-time forecasting of sEMG features provides a general model which can be applied to many applications of muscle activity monitoring, which helps practitioners and physiotherapists improve therapy.

Central and Peripheral Shoulder Fatigue Pre-screening Using the Sigma-Lognormal Model: A Proof of Concept

Background

Clinical tests for detecting central and peripheral shoulder fatigue are limited. The discrimination of these two types of fatigue is necessary to better adapt recovery intervention. The Kinematic Theory of Rapid Human Movements describes the neuromotor impulse response using lognormal functions and has many applications in pathology detection. The ideal motor control is modeled and a change in the neuromuscular system is reflected in parameters extracted according to this theory.

Objective

The objective of this study was to assess whether a shoulder neuromuscular fatigue could be detected through parameters describing the theory, if there is the possibility to discriminate central from peripheral fatigue, and which handwriting test gives the most relevant information on fatigue.

Methods

Twenty healthy participants performed two sessions of fast stroke handwriting on a tablet, before and after a shoulder fatigue. The fatigue was in internal rotation for one session and in external rotation during the other session. The drawings consisted of simple strokes, triangles, horizontal, and vertical oscillations. Parameters of these strokes were extracted according to the Sigma–Lognormal model of the Kinematic Theory. The evolution of each participant was analyzed through a U-Mann–Whitney test for individual comparisons. A Hotelling’s T²-test and a U-Mann–Whitney test were also performed on all participants to assess the group evolution after fatigue. Moreover, a correlation among parameters was calculated through Spearman coefficients to assess intrinsic parameters properties of each handwriting test.

Results

Central and peripheral parameters were statistically different before and after fatigue with a possibility to discriminate them. Participants had various responses to fatigue. However, when considering the group, parameters related to the motor program execution showed significant increase in the handwriting tests after shoulder fatigue. The test of simple strokes permits to know more specifically where the fatigue comes from, whereas the oscillations tests were the most sensitive to fatigue.

Conclusion

The results of this study suggest that the Sigma–Lognormal model of the Kinematic Theory is an innovative approach for fatigue detection with discrimination between the central and peripheral systems. Overall, there is a possibility to implement the setting for clinics and sports personalized follow-up.

Inertial Sensors Embedded in Smartphones as a Tool for Fatigue Assessment Based on Acceleration in Survivors of Breast Cancer

BACKGROUND

Cancer-related fatigue is a symptom commonly reported in survivors of breast cancer and is the most variable symptom. Besides questionnaires like PIPER to assess cancer-related fatigue, there is a need to objectively measure fatigue.

OBJECTIVE

The aim of this study was to assess the physiological dimension of fatigue based on acceleration during a 30-second maximal sit-to-stand test.

DESIGN

This was a cross-sectional study.

METHODS

Linear acceleration from a smartphone placed on the sternum was recorded in 70 survivors of breast cancer. Fourth-degree polynomial adjustment from the acceleration signal to the vertical and anterior-posterior axis was calculated. The fatigue temporal cut-off point was detected as a change in the curve slope of the first maximum point of acceleration.

RESULTS

Women were aged 51.8 (8.9) years with a body mass index of 25.4 (5.1) Kg/m2. They performed 23.6 (6.57) number of repetitions. The mean fatigue cut-off point from the total sample was 10.2 (3.1) seconds.

LIMITATIONS

Further research should employ time-prolonged tests to study acceleration behavior beyond 30 seconds as well as include a physiological criterion that justifies the nonlinear saturation of the acceleration-based criterion.

CONCLUSIONS

This study assessed fatigue through a low-cost and easy-to-use methodology during a functional and widely used test such as 30-second maximal sit-to-stand. This would allow clinicians to assess fatigue in a short-effort exercise to individualize exercise prescription dose, measure changes during intervention, and track fatigue objectively throughout survivorship.

Monitoring the training dose and acute fatigue response during elbow flexor resistance training using a custom-made resistance band

BACKGROUND

Home-based resistance training offers an alternative to traditional, hospital-based or rehabilitation center-based resistance training and has attracted much attention recently. However, without the supervision of a therapist or the assistance of an exercise monitoring system, one of the biggest challenges of home-based resistance training is that the therapist may not know if the patient has performed the exercise as prescribed. A lack of objective measurements limits the ability of researchers to evaluate the outcome of exercise interventions and choose suitable training doses.

OBJECTIVE

To create an automated and objective method for segmenting resistance force data into contraction phase-specific segments and calculate the repetition number and time-under-tension (TUT) during elbow flexor resistance training. A pilot study was conducted to evaluate the performance of the segmentation algorithm and to show the capability of the system in monitoring the compliance of patients to a prescribed training program in a practical resistance training setting.

METHODS

Six subjects (three male and three female) volunteered to participate in a fatigue and recovery experiment (5 min intermittent submaximal contraction (ISC); 1 min rest; 2 min ISC). A custom-made resistance band was used to help subjects perform biceps curl resistance exercises and the resistance was recorded through a load cell. The maximum and minimum values of the force-derivative were obtained as distinguishing features and a segmentation algorithm was proposed to divide the biceps curl cycle into concentric, eccentric and isometric contraction, and rest phases. Two assessors, who were unfamiliar with the study, were recruited to manually pick the visually observed cut-off point between two contraction phases and the TUT was calculated and compared to evaluate performance of the segmentation algorithm.

RESULTS

The segmentation algorithm was programmatically implemented and the repetition number and contraction-phase specific TUT were calculated. During isometric, the average TUT (3.75 ± 0.62 s) was longer than the prescribed 3 s, indicating that most subjects did not perform the exercise as prescribed. There was a good TUT agreement and contraction segment agreement between the proposed algorithm and the assessors.

CONCLUSION

The good agreement in TUT between the proposed algorithm and the assessors indicates that the proposed algorithm can correctly segment the contraction into contraction phase-specific parts, thereby providing clinicians and researchers with an automated and objective method for quantifying home-based elbow flexor resistance training. The instrument is easy to use and cheap, and the segmentation algorithm is programmatically implemented, indicating good application prospect of the method in a practical setting.

Neurophysiological markers predicting recovery of standing in humans with chronic motor complete spinal cord injury

The appropriate selection of individual-specific spinal cord epidural stimulation (scES) parameters is crucial to re-enable independent standing with self-assistance for balance in individuals with chronic, motor complete spinal cord injury, which is a key achievement toward the recovery of functional mobility. To date, there are no available algorithms that contribute to the selection of scES parameters for facilitating standing in this population. Here, we introduce a novel framework for EMG data processing that implements spectral analysis by continuous wavelet transform and machine learning methods for characterizing epidural stimulation-promoted EMG activity resulting in independent standing. Analysis of standing data collected from eleven motor complete research participants revealed that independent standing was promoted by EMG activity characterized by lower median frequency, lower variability of median frequency, lower variability of activation pattern, lower variability of instantaneous maximum power, and higher total power. Additionally, the high classification accuracy of assisted and independent standing allowed the development of a prediction algorithm that can provide feedback on the effectiveness of muscle-specific activation for standing promoted by the tested scES parameters. This framework can support researchers and clinicians during the process of selection of epidural stimulation parameters for standing motor rehabilitation.

Fatigue Detection during Sit-To-Stand Test Based on Surface Electromyography and Acceleration: A Case Study

The latest studies of the 30-second sit-to-stand (30-STS) test aim to describe it by employing kinematic variables, muscular activity, or fatigue through electromyography (EMG) instead of a number of repetitions. The aim of the present study was to develop a detection system based on acceleration measured using a smartphone to analyze fatigue during the 30-STS test with surface electromyography as the criterion. This case study was carried out on one woman, who performed eight trials. EMG data from the lower limbs and trunk muscles, as well as trunk acceleration were recorded. Both signals from eight trials were preprocessed, being averaged and temporarily aligned. The EMG signal was processed, calculating the spectral centroid (SC) by Discrete Fourier Transform, while the acceleration signal was processed by Discrete Wavelet Transform to calculate its energy percentage. Regarding EMG, fatigue in the vastus medialis of the quadriceps appeared as a decrease in SC, with a descending slope of 12% at second 12, indicating fatigue. However, acceleration analysis showed an increase in the percentage of relative energy, acting like fatigue firing at second 19. This assessed fatigue according to two variables of a different nature. The results will help clinicians to obtain information about fatigue using an accessible and inexpensive device, i.e., as a smartphone.

The influence of muscle length on gastrocnemius and vastus lateralis muscle oxygen saturation and endurance

Increasing muscle length (passive stretch) has been shown to reduce muscle oxygen levels by increasing intramuscular pressure.

PURPOSE

To measure the effect of passive stretch on muscle-specific endurance and oxygen saturation in the vastus lateralis and medial gastrocnemius muscle groups.

METHODS

Muscle Endurance (EI), Muscle blood flow (MBF), and Muscle oxygen saturation (MVO₂) were measured on the vastus lateralis and medial gastrocnemius muscles in a passive stretched (lengthened) and relaxed (shortened) positions in 10 healthy individuals (21 ± 1 yrs.). Muscle endurance was measured with tri-axial accelerometer. Muscle oxygen saturation and blood flow were measured using a continuous wavelength Near Infrared Spectroscopy (NIRS).

RESULTS

Muscle at stretched position showed a lower endurance index in the gastrocnemius (51 ± 9.6% versus 77 ± 9.1%, p = 0.008) and vastus lateralis (54 ± 8.9% versus 75 ± 9.6%, p < 0.001). The time to half recovery of oxygen levels during reactive hyperemia was slower in the stretched positions for the gastrocnemius (11.4 ± 1.0 s versus 8.2 ± 1.1 s, p < 0.001) and the vastus lateralis (9.8 ± 1.9 s versus 6.3 ± 0.7 s, p < 0.001). However, oxygen saturation during the endurance tests were not different between stretched and relaxed conditions in both muscle (p > 0.05 for all comparisons).

CONCLUSIONS

Studies of muscle endurance need to control for muscle length as changes in muscle length can influence muscle endurance.