sEMG Device Research Articles

A novel human-robot interface based on low-cost sEMG device designed for the collaborative wearable robot

Currently, the acquisition of surface electromyographic (sEMG) signals commonly requires powerful and expensive equipment or systems. The signal analysis also requires additional equipment to process independently, which is time-consuming and the results are difficult to understand and directly apply to wearable robots. Therefore, this paper developed a sEMG signal device with a simple structure and high-performance cost ratio. The hardware architecture consists of a two-stage small-size amplifier with a total gain of 1000 and a band-pass filter from 10 to 150 HZ. Since real measured sEMG signals are usually corrupted by various types of noises, this paper designed an envelope processing to obtain the sEMG envelope signal which has stronger anti-interference ability than the raw signal, and it is also easier to apply to wearable robots. The proposed system is evaluated quantitatively by the experimental test and electrode groups analysis. In addition, the feasibility and effectiveness of the system is performed by measuring the sEMG signal of the forearm muscle group of four volunteers during motion action and extract the time domain feature to control a wearable robot. The sEMG envelope successfully manipulates the wearable robot with four different actions, whose precision is as high as a conventional sEMG device. The proposed system is promising in providing the reliable control interface of a wearable robot based on the sEMG.

Examining the association between speed and myoelectric activity: Time-based differences and muscle group balance.

The purpose of this study is to investigate the relationship between speed and myoelectric activity, measured during an incremental 25m shuttle running test, exploring the time-based variations and assessing muscle group balance within the context of this association. Twelve male young soccer players (n = 12) aged 18±1.2 years, with an average body mass of 68.4±5.8kg and average body height of 1.72±0.08m, from a professional Italian youth team (Italian "Primavera"), volunteered as participants for this study. The speed of each player during testing was measured using GPS technology, sampling at 50Hz. Myoelectrical activities of the gluteus, hamstrings, and quadriceps muscles were recorded through wearable sEMG devices, sampled at 100Hz. To ensure alignment of the sampling frequencies, the sEMG data was resampled to 50Hz, matching the GPS data sampling rate. This allowed for direct comparison and analysis of the data obtained from both measurement systems. The collected data were then analyzed to determine the relationship between the investigated variables and any potential differences associated with different sides of the body. The results revealed a robust correlation (r2≈0.97) between the speed of the participants (m·s-1) and their myoelectrical activity (μV) during the test. Factorial ANOVA 2x11 showed no significant differences between the sides of the analyzed muscles (p>0.05). The interpolation lines generated by the association of speed and sEMG exhibit very similar angular coefficients (0.9 to 0.12) in all six measurements obtained from electromyography of the three investigated muscle groups on each side of the body. In conclusion, the concurrent validity between the two instruments in this study indicates that GPS and sEMG are valid and consistent in estimating external load and internal load during incremental shuttle running.

Does Exerting Grasps Involve a Finite Set of Muscle Patterns? A Study of Intra- and Intersubject Variability of Forearm sEMG Signals in Seven Grasp Types.

Surface Electromyography (sEMG) signals are widely used as input to control robotic devices, prosthetic limbs, exoskeletons, among other devices, and provide information about someone's intention to perform a particular movement. However, the redundant action of 32 muscles in the forearm and hand means that the neuromotor system can select different combinations of muscular activities to perform the same grasp, and these combinations could differ among subjects, and even among the trials done by the same subject. In this work, 22 healthy subjects performed seven representative grasp types (the most commonly used). sEMG signals were recorded from seven representative forearm spots identified in a previous work. Intra- and intersubject variability are presented by using four sEMG characteristics: muscle activity, zero crossing, enhanced wavelength and enhanced mean absolute value. The results confirmed the presence of both intra- and intersubject variability, which evidences the existence of distinct, yet limited, muscle patterns while executing the same grasp. This work underscores the importance of utilizing diverse combinations of sEMG features or characteristics of various natures, such as time-domain or frequency-domain, and it is the first work to observe the effect of considering different muscular patterns during grasps execution. This approach is applicable for fine-tuning the control settings of current sEMG devices.

Volume estimation of fluid intake using regression models

Monitoring of water intake is critical for managing the health and wellness of individuals with various health conditions, including young children, sick adults, the elderly, and individuals seeking better weight control. The research presented in this paper studies the use of different regression methods to estimate water intake using wireless surface electromyography (sEMG). The advantage of using regression is that it can provide more consistent values for different swallow volumes. In addition, the setup reported in this research employs a less controlled environment, providing stronger evidence of the practical feasibility of the used setup. Neural networks-based regression achieved an R2 of 0.99 and a root-mean-squared error of 0.14 and 0.08 after feature selection. The relative immunity of sEMG as a sensing technique and the accuracy levels achieved with the used mobile sEMG device can provide a robust system for volume estimation of fluid intake in real-world situations.

A Smartphone-Based sEMG Signal Analysis System for Human Action Recognition.

In lower-limb rehabilitation, human action recognition (HAR) technology can be introduced to analyze the surface electromyography (sEMG) signal generated by movements, which can provide an objective and accurate evaluation of the patient's action. To balance the long cycle required for rehabilitation and the inconvenient factors brought by wearing sEMG devices, a portable sEMG signal acquisition device was developed that can be used under daily scenarios. Additionally, a mobile application was developed to meet the demand for real-time monitoring and analysis of sEMG signals. This application can monitor data in real time and has functions such as plotting, filtering, storage, and action capture and recognition. To build the dataset required for the recognition model, six lower-limb motions were developed for rehabilitation (kick, toe off, heel off, toe off and heel up, step back and kick, and full gait). The sEMG segment and action label were combined for training a convolutional neural network (CNN) to achieve high-precision recognition performance for human lower-limb actions (with a maximum accuracy of 97.96% and recognition accuracy for all actions reaching over 97%). The results show that the smartphone-based sEMG analysis system proposed in this paper can provide reliable information for the clinical evaluation of lower-limb rehabilitation.

A Multi-Day Wearable Surface EMG E-Tattoo for Fatigue Monitoring.

Surface electromyography (sEMG) is a commonly used technique for the non-invasive measurement of muscle activity. However, the traditional electrodes used for sEMG often have limitations regarding their long-term wearability. This study explored the feasibility of a wearable platform using a tattoo-like epidermal electrode (e-tattoo) for multi-day sEMG monitoring. Our sEMG e-tattoo provided stable and reliable sEMG signals over three days of application comparable to conventional gel electrodes. In addition, the e-tattoo has great resistance to motion artifacts and, therefore, maintains a high signal-to-noise ratio (SNR) and signal-to-motion ratio (SMR) during dynamic activities such as cycling. This robust wearable platform opens up new avenues for developing future wearable sEMG devices and advancing dynamic muscle fatigue research.Clinical relevance- The proposed wearable sEMG system can provide continuous and non-invasive monitoring of muscle activity, providing insights for improving rehabilitation and EMG-based prosthesis development for patients.

Comparing the Effects of Ergonomic and Standard Office Chairs on Trunk Muscle Activation

Background and Purpose: Musculoskeletal disorders (MSD) are one of the most common health problems faced by individuals who sit for prolonged periods. The sitting design of office chairs has recently become an important aspect in preventing the MSD. The aim of this study is to determine the effect of different chair types on trunk muscle activation.
 Methods: Fifteen participants (age 22.92 ± 3.40 years) were included in the study. Participants’ muscle activations were assessed with the sEMG device while sitting and typing on the computer for one hour. 
 Results: When two different types of chairs were compared, in the first 10 minutes, % MVC difference was observed only in the TES (p

Electromyography Monitoring Systems in Rehabilitation: A Review of Clinical Applications, Wearable Devices and Signal Acquisition Methodologies

Recently, there has been an evolution toward a science-supported medicine, which uses replicable results from comprehensive studies to assist clinical decision-making. Reliable techniques are required to improve the consistency and replicability of studies assessing the effectiveness of clinical guidelines, mostly in muscular and therapeutic healthcare. In scientific research, surface electromyography (sEMG) is prevalent but underutilized as a valuable tool for physical medicine and rehabilitation. Other electrophysiological signals (e.g., from electrocardiogram (ECG), electroencephalogram (EEG), and needle EMG) are regularly monitored by medical specialists; nevertheless, the sEMG technique has not yet been effectively implemented in practical medical settings. However, sEMG has considerable clinical promise in evaluating muscle condition and operation; nevertheless, precise data extraction requires the definition of the procedures for tracking and interpreting sEMG and understanding the fundamental biophysics. This review is centered around the application of sEMG in rehabilitation and health monitoring systems, evaluating their technical specifications, including wearability. At first, this study examines methods and systems for tele-rehabilitation applications (i.e., neuromuscular, post-stroke, and sports) based on detecting EMG signals. Then, the fundamentals of EMG signal processing techniques and architectures commonly used to acquire and elaborate EMG signals are discussed. Afterward, a comprehensive and updated survey of wearable devices for sEMG detection, both reported in the scientific literature and on the market, is provided, mainly applied in rehabilitation training and physiological tracking. Discussions and comparisons about the examined solutions are presented to emphasize how rehabilitation professionals can reap the aid of neurobiological detection systems and identify perspectives in this field. These analyses contribute to identifying the key requirements of the next generation of wearable or portable sEMG devices employed in the healthcare field.

Supervised Myoelectrical Hand Gesture Recognition in Post-Acute Stroke Patients with Upper Limb Paresis on Affected and Non-Affected Sides.

In clinical practice, acute post-stroke paresis of the extremities fundamentally complicates timely rehabilitation of motor functions; however, recently, residual and distorted musculoskeletal signals have been used to initiate feedback-driven solutions for establishing motor rehabilitation. Here, we investigate the possibilities of basic hand gesture recognition in acute stroke patients with hand paresis using a novel, acute stroke, four-component multidomain feature set (ASF-4) with feature vector weight additions (ASF-14NP, ASF-24P) and supervised learning algorithms trained only by surface electromyography (sEMG). A total of 19 (65.9 ± 12.4 years old; 12 men, seven women) acute stroke survivors (12.4 ± 6.3 days since onset) with hand paresis (Brunnstrom stage 4 ± 1/4 ± 1, SIAS 3 ± 1/3 ± 2, FMA-UE 40 ± 20) performed 10 repetitive hand movements reflecting basic activities of daily living (ADLs): rest, fist, pinch, wrist flexion, wrist extension, finger spread, and thumb up. Signals were recorded using an eight-channel, portable sEMG device with electrode placement on the forearms and thenar areas of both limbs (four sensors on each extremity). Using data preprocessing, semi-automatic segmentation, and a set of extracted feature vectors, support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbors (k-NN) classifiers for statistical comparison and validity (paired t-tests, p-value < 0.05), we were able to discriminate myoelectrical patterns for each gesture on both paretic and non-paretic sides. Despite any post-stroke conditions, the evaluated total accuracy rate by the 10-fold cross-validation using SVM among four-, five-, six-, and seven-gesture models were 96.62%, 94.20%, 94.45%, and 95.57% for non-paretic and 90.37%, 88.48%, 88.60%, and 89.75% for paretic limbs, respectively. LDA had competitive results using PCA whereas k-NN was a less efficient classifier in gesture prediction. Thus, we demonstrate partial efficacy of the combination of sEMG and supervised learning for upper-limb rehabilitation procedures for early acute stroke motor recovery and various treatment applications.

A Coiled Carbon Nanotube Yarn-Integrated Surface Electromyography System To Monitor Isotonic and Isometric Movements.

A surface electromyogram (sEMG) electrode collects electrical currents generated by neuromuscular activity by a noninvasive technique on the skin. It is particularly attractive for wearable systems for various human activities and health care monitoring. However, it remains challenging to discriminate EMG signals from isotonic (concentric/eccentric) and isometric movements. By applying nanotechnology, we provide a coiled carbon nanotube (CNT) yarn-integrated sEMG device to overcome sEMG-based motion recognition. When the arm was contracted at different angles, the sEMG-derived root mean square amplitude signals were constant regardless of the angle of the moving arm. However, the coiled CNT yarn-derived open circuit voltage (OCV) signals proportionally increased when the arm's angle increased, and presented negative and positive values depending on the moving direction of the arm. Moreover, isometric contraction is characterized by the onset of EMG signals without an OCV signal, and isotonic contraction is determined by both EMG signals and OCV signals. Taken together, the integration of EMG and coiled CNT yarn electrodes provides complementary information, including the strength, direction, and degree of muscle movement. Therefore, we suggest that our system has high potential as a wearable system to monitor human motions in industrial and human system applications.

Towards Zero Retraining for Multiday Motion Recognition via a Fully Unsupervised Adaptive Approach and Fabric Myoelectric Armband.

Surface electromyogram pattern recognition (EMG-PR) requires time-consuming training and retraining procedures for long-term use, hindering the usability of myoelectric control. In this paper, we design a fabric myoelectric armband to reduce the electrode shifts. Furthermore, we propose a fully unsupervised adaptive approach called hybrid serial classifier (HSC) to eliminate the burden of retraining over multiply days. We investigated the performance of our approach with a dataset of ten types of forearm motion from ten male subjects over eight weeks (total ten days, including: from day 1 to day 7, day 14, day 28, day 56). The average inter-day classification accuracies of HSC without any new retraining data are 86.61% when trained exclusively with the first day's EMG data, and 94.77% when trained with other nine days' data. We compare our proposed HSC algorithm with linear discriminant analysis (LDA) without recalibration (BLDA) and supervised adaption LDA (ALDA) with just one trial of new retraining data. The inter-day classification accuracy of HSC is significantly higher than that of BLDA and ALDA. These results indicate that our novel armband sEMG device is feasible for long-term use in conjunction with the proposed HSC algorithm.

Reduce Surface Electromyography Channels for Gesture Recognition by Multitask Sparse Representation and Minimum Redundancy Maximum Relevance.

Surface electromyography- (sEMG-) based gesture recognition is widely used in rehabilitation training, artificial prosthesis, and human-computer interaction. The purpose of this study is to simplify the sEMG devices by reducing channels while achieving comparably high gesture recognition accuracy. We propose a compound channel selection scheme by combining the variable selection algorithms based on multitask sparse representation (MTSR) and minimum Redundancy Maximum Relevance (mRMR). Specifically, channelwise features are first extracted to compose channel-feature paired variables, for which variable selection procedures by MTSR and mRMR are carried out, respectively. Then, we rank all the channels according to their occurrences in each variable selection procedure and figure out a certain number of informative channels by fusing these rankings of channels. Finally, the gesture classification performance using the selected channels is evaluated by the support vector machine (SVM) classifier. Experiment results validate the effectiveness of this proposed method.

Functional Gait Recovery after a Combination of Conventional Therapy and Overground Robot-Assisted Gait Training Is Not Associated with Significant Changes in Muscle Activation Pattern: An EMG Preliminary Study on Subjects Subacute Post Stroke.

Background: Overground Robot-Assisted Gait Training (o-RAGT) appears to be a promising stroke rehabilitation in terms of clinical outcomes. The literature on surface ElectroMyoGraphy (sEMG) assessment in o-RAGT is limited. This paper aimed to assess muscle activation patterns with sEMG in subjects subacute post stroke after training with o-RAGT and conventional therapy. Methods: An observational preliminary study was carried out with subjects subacute post stroke who received 15 sessions of o-RAGT (5 sessions/week; 60 min) in combination with conventional therapy. The subjects were assessed with both clinical and instrumental evaluations. Gait kinematics and sEMG data were acquired before (T1) and after (T2) the period of treatment (during ecological gait), and during the first session of o-RAGT (o-RAGT1). An eight-channel wireless sEMG device acquired in sEMG signals. Significant differences in sEMG outcomes were found in the BS of TA between T1 and T2. There were no other significant correlations between the sEMG outcomes and the clinical results between T1 and T2. Conclusions: There were significant functional gains in gait after complex intensive clinical rehabilitation with o-RAGT and conventional therapy. In addition, there was a significant increase in bilateral symmetry of the Tibialis Anterior muscles. At this stage of the signals from the tibialis anterior (TA), gastrocnemius medialis (GM), rectus femoris (RF), and biceps femoris caput longus (BF) muscles of each lower extremity. sEMG data processing extracted the Bilateral Symmetry (BS), the Co-Contraction (CC), and the Root Mean Square (RMS) coefficients. Results: Eight of 22 subjects in the subacute stage post stroke agreed to participate in this sEMG study. This subsample demonstrated a significant improvement in the motricity index of the affected lower limb and functional ambulation. The heterogeneity of the subjects’ characteristics and the small number of subjects was associated with high variability research, functional gait recovery was associated with minimal change in muscle activation patterns.

Low-cost stimulation resistant electromyography

Surface Electromyography (sEMG) is the non-invasive measurement of skeletal muscle contraction bio-potentials. Measuring sEMG of a stimulated muscle can prove particularly difficult due to large scale and long lasting stimulation-induced artefacts: if an sEMG device does not account for such artefacts, its measurements can be swamped and components damaged. sEMG has been used in a wide range of clinical and biomedical fields, providing measures such as muscular fatigue and subject intent. The recording of sEMG can prove difficult due to signal contamination such as movement artefact and mains interference.There are very few commercial sEMG devices that contain protection against large stimulation voltages or measures to reduce artefact transient times. Furthermore, most commercial or research level designs are not open source; these designs are effectively an inflexible black box to researchers and developers.This research presents the design, test and validation of an open source sEMG design, able to record muscle bio-potentials concurrently to electrical stimulation. The open source, low-cost nature of the design provides accessibility to researchers without the time and cost associated with design development. The design has been tested on the forearms of four able-bodied subjects during 25 Hz constant current stimulation, and has been shown to record subject volitional sEMG and M-wave without saturation.

Surface Electromyography in Clinical Practice. A Perspective From a Developing Country.

Surface electromyography (sEMG) has long been used in research, health care, and other fields such as ergonomics and brain-machine interfaces. In health care, sEMG has been employed to diagnose as well as to treat musculoskeletal disorders, pelvic floor dysfunction, and post-stroke motor deficits, among others. Despite the extensive literature on sEMG, the clinical community has not widely adopted it. We believe that in developing countries, such as Chile, this phenomenon may be explained by several interacting barriers. First, the socioeconomics of the country creates an environment where only high cost-effective treatments are routinely applied. Second, the majority of the sEMG literature on clinical applications has not extensively translated into decisive outcomes, which interferes with its applicability in low-income contexts. Third, clinical training on rehabilitation provides inadequate instruction on sEMG. And fourth, accessibility to equipment (i.e., affordability, availability, portability) may constitute another barrier, especially among developing countries. Here, we analyze socio-economic indicators of health care in Chile and comment on current literature about the use of sEMG in rehabilitation. Then we analyze the curricula of several physical therapy schools in Chile and report some estimations of the training on sEMG. Finally, we analyze the accessibility of some available sEMG devices and show that several match predefined criteria. We conclude that in developing countries, the insufficient use of sEMG in health might be explained by a shortage of evidence showing a crucial role in specific outcomes and the lack of training in rehabilitation-related careers, which interact with local socioeconomic factors that limit the application of these techniques.

Are armband sEMG devices dense enough for long-term use?—Sensor placement shifts cause significant reduction in recognition accuracy

Myoelectric control systems (MCSs), which recognize motions through surface electromyograms (sEMGs), present potential applicability for clinical, recreational, and motion-assisting purposes. To increase the adoption of armband device-based MCSs, the performance of motion recognition algorithms should be determined over long periods and sensor placement shifts. We prepared an sEMG dataset to assess motion recognition algorithms for practical use over long periods with varying sensor placement. The dataset comprises 30 recording sessions over 40–42 days, in which sensors were placed at three different placements. We used an armband eight-channel sEMG device for capturing 22 types of forearm motions from five healthy male subjects. To consider only motion periods to learn classifiers, we extracted relevant 1.5-s segments via multiscale sample entropy. We evaluated the dataset on a conventional motion recognition algorithm, finding robust intraday performance but significantly deteriorated inter-day performance under varying sensor placement. Hence, the armband sEMG device is dense enough for short-term use but not apt for long-term use regarding the conventional recognition algorithm. Adaptation techniques are required for developing armband device-based MCSs for long-term use. The dataset and sample codes from this study are publicly available at GitHub.

Low-cost active electromyography

Electromyography (sEMG) is the technique applied to measure the bio-potential signal produced by a contracting skeletal muscle. sEMG has been used for subject intention estimation, force estimation, limb angle estimation and determining muscle activation timing. Typically most sEMG devices on the market, and in literature, are not open source hardware. This results in a device that can be considered as a black box, as it is difficult to determine the applied signal processing. This paper presents an open source low-cost active sEMG circuit, allowing an understanding of sEMG and the challenges involved in designing a high quality device. An open source sEMG design reduces the time and cost associated with initial sEMG research, providing a larger number of researchers with the opportunity to utilise sEMG. The device has been tested on three subjects, resulting in a low baseline noise ⩽5.2μVrms and great line noise rejection, resulting in a sensitive device that can capture high quality bio-potential signals.

Label Self-Advised Support Vector Machine (LSA-SVM)-Automated Classification of Foot Drop Rehabilitation Case Study.

Stroke represents a major health problem in our society. One of the effects of stroke is foot drop. Foot drop (FD) is a weakness that occurs in specific muscles in the ankle and foot such as the anterior tibialis, gastrocnemius, plantaris and soleus muscles. Foot flexion and extension are normally generated by lower motor neurons (LMN). The affected muscles impact the ankle and foot in both downward and upward motions. One possible solution for FD is to investigate the movement based on the bio signal (myoelectric signal) of the muscles. Bio signal control systems like electromyography (EMG) are used for rehabilitation devices that include foot drop. One of these systems is function electrical stimulation (FES). This paper proposes new methods and algorithms to develop the performance of myoelectric pattern recognition (M-PR), to improve automated rehabilitation devices, to test these methodologies in offline and real-time experimental datasets. Label classifying is a predictive data mining application with multiple applications in the world, including automatic labeling of resources such as videos, music, images and texts. We combine the label classification method with the self-advised support vector machine (SA-SVM) to create an adapted and altered label classification method, named the label self-advised support vector machine (LSA-SVM). For the experimental data, we collected data from foot drop patients using the sEMG device, in the Metro Rehabilitation Hospital in Sydney, Australia using Ethical Approval (UTS HREC NO. ETH15-0152). The experimental results for the EMG dataset and benchmark datasets exhibit its benefits. Furthermore, the experimental results on UCI datasets indicate that LSA-SVM achieves the best performance when working together with SA-SVM and SVM. This paper describes the state-of-the-art procedures for M-PR and studies all the conceivable structures.

Development of an 8 channel sEMG wireless device based on ADS1299 with Virtual Instrumentation

In this paper, a different approach on the use of the ADS1299 (an analog front-end with features for electroencephalogram and electrocardiography signal acquisition) is considered, proposing the development of a surface electromyography (sEMG) device. The main features of the device include simultaneous recordings of eight muscular channels, wireless transmission and virtual instrumentation with the use of LabVIEWTM software. The proposed sEMG device contains a specifically designed protocol to accommodate data transmission by reducing the data size while still delivering adequate resolution (34.33 μV), amplitude range (±17.57 mV) and sampling rate (1000 Hz) for sEMG signals. For the validation methods, a generated sine wave and a known sEMG data were evaluated. Moreover, the muscular recordings for all the eight channels of a human arm were successful and the results expose the isolated contractions of the triceps and the biceps with their amplitude range and frequency spectrum.

Validity and Reliability of the Newly Developed Surface Electromyography Device for Measuring Muscle Activity during Voluntary Isometric Contraction.

Objective The purpose of this study was to establish the validity and reliability of the newly developed surface electromyography (sEMG) device (PSL-EMG-Tr1) compared with a conventional sEMG device (BTS-FREEEMG1000). Methods In total, 20 healthy participants (10 males, age 30.3 ± 2.9 years; 10 females, age 22.3 ± 2.7 years) were recruited. EMG signals were recorded simultaneously on two devices during three different isometric contractions (maximal voluntary isometric contraction (MVIC, 40% MVIC, 80% MVIC)). Two trials were performed, and the same session was repeated after 1 week. EMG amplitude recorded from the dominant biceps brachii (BB) and rectus femoris (RF) muscles was analyzed for reliability using intrasession intraclass correlation coefficient (ICC). Concurrent validity of the two devices was determined using Pearson's correlation coefficient. Results Nonnormalized sEMG data showed moderate to very high reliability for all three contraction levels (ICC = 0.832–0.937 (BB); ICC = 0.814–0.957 (RF)). Normalized sEMG values showed no to high reliability (ICC = 0.030–0.831 (BB); ICC = 0.547–0.828 (RF)). sEMG signals recorded by the PSL-EMG-Tr1 showed good to excellent validity compared with the BTS-FREEEMG1000, at 40% MVIC (r = 0.943 (BB), r = 0.940 (RF)) and 80% MVIC (r = 0.983 (BB); r = 0.763 (RF)). Conclusions The PSL-EMG-Tr1 was performed with acceptable validity. Furthermore, the high accessibility and portability of the device are useful in adjusting the type and intensity of exercise.