Low-cost electromyography – Validation against a commercial system using both manual and automated activation timing thresholds

A Movement Classification of Polymyalgia Rheumatica Patients Using Myoelectric Sensors

Gait disorder is common among people with neurological disease and musculoskeletal disorders. The detection of gait disorders plays an integral role in designing appropriate rehabilitation protocols. This study presents a clinical gait analysis of patients with polymyalgia rheumatica to determine impaired gait patterns using machine learning models. A clinical gait assessment was conducted at KATH hospital between August and September 2022, and the 25 recruited participants comprised 18 patients and 7 control subjects. The demographics of the participants follow: age 56 years ± 7, height 175 cm ± 8, and weight 82 kg ± 10. Electromyography data were collected from four strained hip muscles of patients, which were the rectus femoris, vastus lateralis, biceps femoris, and semitendinosus. Four classification models were used-namely, support vector machine (SVM), rotation forest (RF), k-nearest neighbors (KNN), and decision tree (DT)-to distinguish the gait patterns for the two groups. SVM recorded the highest accuracy of 85% among the classifiers, while KNN had 75%, RF had 80%, and DT had the lowest accuracy of 70%. Furthermore, the SVM classifier had the highest sensitivity of 92%, while RF had 86%, DT had 90%, and KNN had the lowest sensitivity of 84%. The classifiers achieved significant results in discriminating between the impaired gait pattern of patients with polymyalgia rheumatica and control subjects. This information could be useful for clinicians designing therapeutic exercises and may be used for developing a decision support system for diagnostic purposes.

Development of a Low-Cost Portable EMG for Measuring the Muscular Activity of Workers in the Field

This study explores the development and validation of a low-cost electromyography (EMG) device for monitoring muscle activity and muscle fatigue by monitoring the key features in EMG time and frequency domains. The device consists of a Raspberry Pico microcontroller interfacing a Myoware EMG module. The experiment involved 34 volunteers (14 women, 20 men) who performed isometric and isotonic contractions using a hand dynamometer. The low-cost EMG device was compared to a research-grade EMG device, recording EMG signals simultaneously. Key features including root mean square (RMS), median power frequency (MDF), and mean power frequency (MNF) were extracted to evaluate muscle fatigue. During isometric contraction, a strong congruence between the two devices, with similar readings and behavior of the extracted features, was observed, and the Wilcoxon signed rank test confirmed no significant difference in the ability to detect muscle fatigue between the devices. For isotonic contractions, the low-cost device demonstrated behavior similar to the professional EMG device in 70.58% of cases, despite some susceptibility to noise and movement. This suggests the potential viability of the low-cost EMG device as a portable tool for assessing muscle fatigue, enabling accessible and cost-effective management of muscle health in various work scenarios.

Assessing Quadriceps Muscle Contraction Using a Novel Surface Mechanomyography Sensor during Two Neuromuscular Control Screening Tasks

Electromyography (EMG) is the clinical standard for capturing muscle activation data to gain insight into neuromuscular control, yet challenges surrounding data analysis limit its use during dynamic tasks. Surface mechanomyography (sMMG) sensors are novel wearable devices that measure the physical output of muscle excursion during contraction, which may offer potential easy application to assess neuromuscular control. This study aimed to investigate sMMG detection of the timing patterns of muscle contraction compared to EMG. Fifteen healthy participants (mean age = 31.7 ± 9.1 y; eight males and seven females) were donned with EMG and sMMG sensors on their right quadriceps for simultaneous data capture during bilateral deep squats, and a subset performed three sets of repeated unilateral partial squats. No significant difference in the total duration of contraction was detected by EMG and sMMG during bilateral (p = 0.822) and partial (p = 0.246) squats. sMMG and EMG timing did not differ significantly for eccentric (p = 0.414) and concentric (p = 0.462) phases of muscle contraction during bilateral squats. The sMMG magnitude of quadriceps excursion demonstrated excellent intra-session retest reliability for bilateral (ICC_3,1 = 0.962 mm) and partial (ICC_3,1 = 0.936 mm, n = 10) squats. The sMMG sensors accurately and consistently provided key quadriceps muscle performance metrics during two physical activities commonly used to assess neuromuscular control for injury prevention, rehabilitation, and exercise training.

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.

Lower limb muscle performance during a closed chain single leg squat and a squat jump in people with leg weakness after stroke: A comparative study

Objective:

To determine if the intention to perform an exercise at speed leads to beneficial alterations in kinematic and kinetic components of the movement in people with post-stroke hemiplegia.

Design:

Comparative study.

Setting:

Subacute metropolitan rehabilitation hospital.

Participants:

Convenience sample of patients admitted as an inpatient or outpatient with a diagnosis of stroke with lower limb weakness, functional ambulation category score ≥3, and ability to walk ≥14metres.

Methods:

Participants performed a single leg squat exercise on their paretic and nonparetic legs on a leg sled under three conditions: 1) self-selected speed (SS), 2) fast speed (FS), 3) jump squat (JS). Measures of displacement, flight time, peak concentric velocity, and muscle excitation (via electromyography) were compared between legs and conditions.

Results:

Eleven participants (age: 56 ± 17 years; median time since stroke onset: 3.3 [IQR 3,41] months) were tested. All participants achieved a jump during the JS, as measured by displacement and flight time respectively, on both their paretic (0.25 ± 0.16 m and 0.42 ± 0.18 s) and nonparetic (0.49 ± 0.36 m and 0.73 ± 0.28 s) legs; however it was significantly lower on the non-paretic leg (p < 0.05). Peak concentric velocity increased concordantly with intended movement speed (JS-FS paretic: 0.96 m/s, non-paretic: 0.54 m/s; FS-SS paretic 0.69 m/s, nonparetic 0.38 m/s; JS-SS paretic 1.66 m/s, non-paretic 0.92 m/s). Similarly, muscle excitation increased significantly (p < 0.05) with faster speed for the paretic and nonparetic vastus lateralis. For gastrocnemius, the only significant difference was an increase during nonparetic JS vs. SS and FS.

Conclusions:

Speed affects the kinematic and kinetic components of the movement. Performing exercises ballistically may improve training outcomes for people post-stroke.

Design and Evaluation of a Custom-Made Electromyographic Biofeedback System for Facial Rehabilitation

Background

In the rehabilitation of postoperative facial palsy, physical therapy is of paramount importance. However, in the early rehabilitation phase, voluntary movements are often limited, and thus, the motivation of patients is impacted. In these situations, biofeedback of facial electromyographic (EMG) signals enables the visual representation of the rehabilitation progress, even without apparent facial movements. In the present study, we designed and evaluated a custom-made EMG biofeedback system enabling cost-effective facial rehabilitation.

Methods

This prospective study describes a custom-made EMG system, consisting of a microcontroller board and muscle sensors, which was used to record the EMG of frontal and zygomatic facial muscles during frowning and smiling. First, the mean EMG amplitudes and movement onset detection rates (ACC) achieved with the custom-made EMG system were compared with a commercial EMG device in 12 healthy subjects. Subsequently, the custom-made device was applied to 12 patients with and without postoperative facial paresis after neurosurgical intervention. Here, the ratio [laterality index (LI)] between the mean EMG amplitude of the healthy and affected side was calculated and related to the facial function as measured by the House and Brackmann scale (H&B) ranging from 1 (normal) to 6 (total paralysis).

Results

In healthy subjects, a good correlation was measured between the mean EMG amplitudes of the custom-made and commercial EMG device for both frontal (r = 0.84, p = 0.001) and zygomatic muscles (r = 0.8, p = 0.002). In patients, the LI of the frontal and zygomatic muscles correlated significantly with the H&B (r = −0.83, p = 0.001 and r = −0.65, p = 0.023). The ACC of the custom-made EMG system varied between 65 and 79% depending on the recorded muscle and cohort.

Conclusion

The present study demonstrates a good application potential of our custom-made EMG biofeedback device to detect facial EMG activity in healthy subjects as well as patients with facial palsies. There is a correlation between the electrophysiological measurements and the clinical outcome. Such a device might enable cost-efficient home-based facial EMG biofeedback. However, movement detection accuracy should be improved in future studies to reach ranges of commercial devices.

Lower limb muscle performance during a closed chain single leg squat and a squat jump in people with leg weakness after stroke: A comparative study

OBJECTIVE

To determine if the intention to perform an exercise at speed leads to beneficial alterations in kinematic and kinetic components of the movement in people with post-stroke hemiplegia.

DESIGN

Comparative study.

SETTING

Subacute metropolitan rehabilitation hospital.

PARTICIPANTS

Convenience sample of patients admitted as an inpatient or outpatient with a diagnosis of stroke with lower limb weakness, functional ambulation category score ≥3, and ability to walk ≥14metres.

METHODS

Participants performed a single leg squat exercise on their paretic and nonparetic legs on a leg sled under three conditions: 1) self-selected speed (SS), 2) fast speed (FS), 3) jump squat (JS). Measures of displacement, flight time, peak concentric velocity, and muscle excitation (via electromyography) were compared between legs and conditions.

RESULTS

Eleven participants (age: 56 ± 17 years; median time since stroke onset: 3.3 [IQR 3,41] months) were tested. All participants achieved a jump during the JS, as measured by displacement and flight time respectively, on both their paretic (0.25 ± 0.16 m and 0.42 ± 0.18 s) and nonparetic (0.49 ± 0.36 m and 0.73 ± 0.28 s) legs; however it was significantly lower on the non-paretic leg (p p Conclusions: Speed affects the kinematic and kinetic components of the movement. Performing exercises ballistically may improve training outcomes for people post-stroke.

Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks

Electromyogram (EMG) signals have been increasingly used for hand and finger gesture recognition. However, most studies have focused on the wrist and whole-hand gestures and not on individual finger (IF) gestures, which are considered more challenging. In this study, we develop EMG-based hand/finger gesture classifiers based on fixed electrode placement using machine learning methods. Ten healthy subjects performed ten hand/finger gestures, including seven IF gestures. EMG signals were measured from three channels, and six time-domain (TD) features were extracted from each channel. A total of 18 features was used to build personalized classifiers for ten gestures with an artificial neural network (ANN), a support vector machine (SVM), a random forest (RF), and a logistic regression (LR). The ANN, SVM, RF, and LR achieved mean accuracies of 0.940, 0.876, 0.831, and 0.539, respectively. One-way analyses of variance and F-tests showed that the ANN achieved the highest mean accuracy and the lowest inter-subject variance in the accuracy, respectively, suggesting that it was the least affected by individual variability in EMG signals. Using only TD features, we achieved a higher ratio of gestures to channels than other similar studies, suggesting that the proposed method can improve the system usability and reduce the computational burden.

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.

Surface Electromyography Study Using a Low-Cost System: Are There Neck Muscles Differences When the Passenger Is Warned during an Emergency Braking Inside an Autonomous Vehicle?

Deaths and serious injuries caused by traffic accidents is a concerning public health problem. However, the problem can be mitigated by the Autonomous Emergency Braking (AEB) system, which can avoid the impact. The market penetration of AEB is exponentially growing, and non-impact situations are expected to become more frequent. Thus, new injury patterns must be analysed, and the neck is particularly sensitive to sudden acceleration changes. Abrupt braking would be enough to be a potential risk for cervical spine injury. There is controversy about whether or not there are differences in cervical behaviour depending on whether passengers are relaxed or contract their muscles before the imminent accident. In the present manuscript, 18 volunteers were subjected to two different levels of awareness during an emergency braking test. Cervical muscles (sternocleidomastoid and trapezius) were analysed by the sEMG signal captured by means of a low-cost system. The differences observed in the muscle response according to gender and age were notable when passengers are warned. Gender differences were more significant in the post-braking phase. When passengers were relaxed, subjects older than 35 registered higher sEMG values. Meanwhile, when passengers contract their muscles, subjects who were younger than or equal to 35 years old experienced an increment in the values of the sEMG signals.

Sustainable Human–Robot Collaboration Based on Human Intention Classification

Sustainable manufacturing plays a role in ensuring products’ economic characteristics and reducing energy and resource consumption by improving the well-being of human workers and communities and maintaining safety. Using robots is one way for manufacturers to increase their sustainable manufacturing practices. Nevertheless, there are limitations to directly replacing humans with robots due to work characteristics and practical conditions. Collaboration between robots and humans should accommodate human capabilities while reducing loads and ineffective human motions to prevent human fatigue and maximize overall performance. Moreover, there is a need to establish early and fast communication between humans and machines in human–robot collaboration to know the status of the human in the activity and make immediate adjustments for maximum performance. This study used a deep learning algorithm to classify muscular signals of human motions with accuracy of 88%. It indicates that the signal could be used as information for the robot to determine the human motion’s intention during the initial stage of the entire motion. This approach can increase not only the communication and efficiency of human–robot collaboration but also reduce human fatigue by the early detection of human motion patterns. To enhance human well-being, it is suggested that a human–robot collaboration assembly line adopt similar technologies for a sustainable human–robot collaboration workplace.

Low-cost electromyography: validity against a commercial system depends on exercise type and intensity

The aim of this study was to assess the validity of a custom-made low cost (LC) and a commercial surface EMG apparatus in controlled experimental conditions and different exercise types: maximal voluntary contractions (MVC) at 105, 90, 75, 60, 45 and 30° knee angle and explosive fix-end contractions of the knee extensors (75°) at an isometric dynamometer. sEMG of vastus lateralis was recorded from the same electrodes simultaneously, then analyzed in the same way; sEMG were finally expressed in percentage of those collected at 75°MVC. LC underestimated the sEMG signal at the more extended knee angles (30-60°), significant difference was observed only at 30°. In the explosive contractions no differences between devices were observed in average and peak sEMG, as well as in the time to peak and the activation time. Bland-Altman tests and correlation parameters indicate the LC device is not sensible enough to detect the time to peak and the peak values of the sEMG signal properly. Results suggest low-cost systems might be a valid alternative to commercial ones, but attention must be paid when analyzing rapid events.

Upper Limb Associated Reactions: The Relationship Between Movement Kinematics and Muscle Activity in Seated Versus Walking Testing

OBJECTIVE

The aims of this study were to (1) evaluate the relationships between stationary and dynamic associated reaction (AR) tests in people with acquired brain injury using surface electromyography (sEMG) muscle activity and three-dimensional motion analysis kinematic measures and (2) assess the test-retest reliability of sEMG and seated tests of ARs.

DESIGN

Forty-two adults with acquired brain injury underwent AR testing with seated contralateral maximal voluntary isometric contraction tests and walking (self-selected and fast speeds). Associated reaction measurements included biceps brachii sEMG, elbow goniometry, and three-dimensional motion analysis kinematics during walking. Pearson correlations evaluated the relationships between seated and dynamic walking AR tests and between muscle activity and kinematic measures. Chronic participants were reassessed 1 wk later for reliability.

RESULTS

A strong (r = 0.65) and moderate (r = 0.53) relationship existed for biceps brachii sEMG during seated and walking tests at self-selected and fast walk, respectively. A weak to moderate relationship existed between biceps brachii sEMG and kinematics during walking and between seated and walking measures of ARs (r = 0.23-0.53). All tests had strong to very strong test-retest reliability (intraclass correlation coefficients, >0.78).

CONCLUSION

Seated contralateral maximal voluntary isometric contraction tests correlate only weakly to moderately with AR walking kinematics and moderately to strongly with biceps brachii activation during walking. Moderate relationships exist between sEMG and kinematics, indicating that they may provide different information for ARs.

A low-cost transradial prosthesis controlled by the intention of muscular contraction

Persons with upper-limb amputations face severe problems due to a reduction in their ability to perform the activities of daily living. The prosthesis controlled by electromyography (EMG) or other signals from sensors, switches, accelerometers, etc., can somewhat regain the lost capability of such individuals. However, there are several issues with these prostheses, such as expensive cost, limited functionality, unnatural control, slow operating speed, complexity, heavyweight, large size, etc. This paper proposes an affordable transradial prosthesis, controlled by the muscular contractions from user intention. A surface EMG sensor was explicitly fabricated for capturing the muscle contraction information from the residual forearm of subjects with amputation. An under actuated 3D printed hand was developed with a prosthetic socket assembly to attach the remaining upper-limb of such subjects. The hand integrates an intuitive closed-loop control system that receives reference input from the designed sensor and feedback input from a force sensor installed at the thumb tip. The performance of the EMG sensor was compared with that of a traditional sensor in detecting muscle contractions from the subjects. The designed sensor showed a good correlation (r > 0.93) and a better signal-to-noise ratio (SNR) feature to the conventional sensor. Further, a successful trial of the developed hand prosthesis was made on five different subjects with transradial amputation. The users wearing the hand prototype were able to perform faster and delicate grasping of various objects. The implemented control system allowed the prosthesis users to control the grasp force of hand fingers with their intention of muscular contractions.

Movement Quality: A Novel Biomarker Based on Principles of Neuroscience

A major problem in neurorehabilitation is the lack of objective outcomes to measure movement quality. Movement quality features, such as coordination and stability, are essential for everyday motor actions. These features allow reacting to continuously changing environment or to resist external perturbations. Neurological disorders affect movement quality, leading to functionally impaired movements. Recent findings suggest that the central nervous system organizes motor elements (eg, muscles, joints, fingers) into task-specific ensembles to stabilize motor tasks performance. A method to quantify this feature has been previously developed based on the uncontrolled manifold (UCM) hypothesis. UCM quantifies movement quality in a spatial-temporal domain using intertrial analysis of covariation between motor elements. In this point-of-view article, we first describe major obstacles (eg, the need for group analysis) that interfere with UCM application in clinical settings. Then, we propose a process of quantifying movement quality for a single individual with a novel use of bootstrapping simulations and UCM analysis. Finally, we reanalyze previously published data from individuals with neurological disorders performing a wide range of motor tasks, that is, multi-digit pressing and postural balance tasks. Our method allows one to assess motor quality impairments in a single individual and to detect clinically important motor behavior changes. Our solution may be incorporated into a clinical setting to assess sensorimotor impairments, evaluate the effects of specific neurological treatments, or track movement quality recovery over time. We also recommended the proposed solution to be used jointly with a typical statistical analysis of UCM parameters in cohort studies.

Study of the Emergency Braking Test with an Autonomous Bus and the sEMG Neck Response by Means of a Low-Cost System

Nowadays, due to the advances and the increasing implementation of the autonomous braking systems in vehicles, the non-collision accident is expected to become more common than a crash when a sudden stop happens. The most common injury in this kind of accident is whiplash or cervical injury since the neck has high sensitivity to sharp deceleration. To date, biomechanical research has usually been developed inside laboratories and does not entirely represent real conditions (e.g., restraint systems or surroundings of the experiment). With the aim of knowing the possible neck effects and consequences of an automatic emergency braking inside an autonomous bus, a surface electromyography (sEMG) system built by low-cost elements and developed by us, in tandem with other devices, such as accelerometers or cameras, were used. Moreover, thanks to the collaboration of 18 participants, it was possible to study the non-collision effects in two different scenarios (braking test in which the passenger is seated and looking ahead while talking with somebody in front of him (BT1) and, a second braking test where the passenger used a smartphone (BT2) and nobody is seated in front of him talking to him). The aim was to assess the sEMG neck response in the most common situations when somebody uses some kind of transport in order to conclude which environments are riskier regarding a possible cervical injury.

Investigating the performance of an amplitude-independent algorithm for detecting the hand muscle activity of stroke survivors

To make robotic hand devices controlled by surface electromyography (sEMG) signals feasible and practical tools for assisting patients with hand impairments, the problems that prevent these devices from being widely used have to be overcome. The most significant problem is the involuntary amplitude variation of the sEMG signals due to the movement of electrodes during forearm motion. Moreover, for patients who have had a stroke or another neurological disease, the muscle activity of the impaired hand is weak and has a low signal-to-noise ratio (SNR). Thus, muscle activity detection methods intended for controlling robotic hand devices should not depend mainly on the amplitude characteristics of the sEMG signal in the detection process, and they need to be more reliable for sEMG signals that have a low SNR. Since amplitude-independent muscle activity detection methods meet these requirements, this paper investigates the performance of such a method on people who have had a stroke in terms of the detection of weak muscle activity and resistance to false alarms caused by the involuntary amplitude variation of sEMG signals; these two parameters are very important for achieving the reliable control of robotic hand devices intended for people with disabilities. A comparison between the performance of an amplitude-independent muscle activity detection algorithm and three amplitude-dependent algorithms was conducted by using sEMG signals recorded from six hemiparesis stroke survivors and from six healthy subjects. The results showed that the amplitude-independent algorithm performed better in terms of detecting weak muscle activity and resisting false alarms.

Muscle Activity Detectors-Surface Electromyography in the Evaluation of Abductor Hallucis Muscle

Despite the high availability of surface electromyography (sEMG), it is not widely used for testing the effectiveness of exercises that activate intrinsic muscles of foot in people with hallux valgus. The aim of this study was to assess the effect of the toe-spread-out (TSO) exercise on the outcomes of sEMG recorded from the abductor hallucis muscle (AbdH). An additional objective was the assessment of nerve conduction in electroneurography. The study involved 21 patients with a diagnosed hallux valgus (research group A) and 20 people without the deformation (research group B) who performed a TSO exercise and were examined twice: before and after therapy. The statistical analysis showed significant differences in the third, most important phase of TSO. After the exercises, the frequency of motor units recruitment increased in both groups. There were no significant differences in electroneurography outcomes between the two examinations in both research groups. The TSO exercise helps in the better activation of the AbdH muscle and contributes to the recruitment of a larger number of motor units of this muscle. The TSO exercises did not cause changes in nerve conduction. The sEMG and ENG are good methods for assessing this exercise but a comprehensive assessment should include other tests as well.

Validation of a Low-Cost Electromyography (EMG) System via a Commercial and Accurate EMG Device: Pilot Study

Electromyography (EMG) devices are well-suited for measuring the behaviour of muscles during an exercise or a task, and are widely used in many different research areas. Their disadvantage is that commercial systems are expensive. We designed a low-cost EMG system with enough accuracy and reliability to be used in a wide range of possible ways. The present article focuses on the validation of the low-cost system we designed, which is compared with a commercially available, accurate device. The evaluation was done by means of a set of experiments, in which volunteers performed isometric and dynamic exercises while EMG signals from the rectus femoris muscle were registered by both the proposed low-cost system and a commercial system simultaneously. Analysis and assessment of three indicators to estimate the similarity between both signals were developed. These indicated a very good result, with spearman’s correlation averaging above 0.60, the energy ratio close to the 80% and the linear correlation coefficient approximating 100%. The agreement between both systems (custom and commercial) is excellent, although there are also some limitations, such as the delay of the signal (<1 s) and noise due to the hardware and assembly in the proposed system.

Design and Validation of Multichannel Wireless Wearable SEMG System for Real-Time Training Performance Monitoring

Monitoring of training performance and physical activity has become indispensable these days for athletes. Wireless technologies have started to be widely used in the monitoring of muscle activation, in the sport performance of athletes, and in the examination of training efficiency. The monitorability of performance simultaneously in the process of training is especially a necessity for athletes at the beginner level to carry out healthy training in sports like weightlifting and bodybuilding. For this purpose, a new system consisting of 4 channel wireless wearable SEMG circuit and analysis software has been proposed to detect dynamic muscle contractions and to be used in real-time training performance monitoring and analysis. The analysis software, the Haar wavelet filter with threshold cutting, can provide performance analysis by using the methods of moving RMS and %MVC. The validity of the data obtained from the system was investigated and compared with a biomedical system. In this comparison, 90.95% ± 3.35 for left biceps brachii (BB) and 90.75% ± 3.75 for right BB were obtained. The output of the power and %MVC analysis of the system was tested during the training of the participants at the gym, and the training efficiency was measured as 96.87% ± 2.74.

A low-cost, wearable sEMG sensor for upper limb prosthetic application

This paper proposes a low-cost and sensitive surface electromyography (sEMG) sensor for the myoelectric prosthesis. The sensor consists of a skin interface, signal conditioning circuitry and power supply unit all encased in a single package. The tuned RC parameters based envelope detection scheme employed in the sensor enables faster as well as reliable recognition of EMG signal patterns regardless of its strength and subject variability. The output performance of the developed sensor was compared with a commercial EMG sensor regarding signal-to-noise ratio (SNR), amplitude sensitivity and response time. EMG signals with both the devices were acquired for 10 subjects (three amputees and seven healthy subjects), to perform this comparative analysis. The results showed 4× greater SNR values and 50% higher sensitivity of the developed sensor than the commercial EMG sensor. Also, the proposed sensor was 57% faster than the commercial sensor in producing the output response. The sensor was successfully tested on amputees for controlling a 3D printed hand prototype utilising a proportional control strategy. The enhanced output parameters of the sensor were responsible for smooth, faster and intuitive actuation of the prosthetic hand fingers.