Features Of Electromyography Signals Research Articles

Dual Stream Long Short-Term Memory Feature Fusion Classifier for Surface Electromyography Gesture Recognition.

Gesture recognition using electromyography (EMG) signals has prevailed recently in the field of human-computer interactions for controlling intelligent prosthetics. Currently, machine learning and deep learning are the two most commonly employed methods for classifying hand gestures. Despite traditional machine learning methods already achieving impressive performance, it is still a huge amount of work to carry out feature extraction manually. The existing deep learning methods utilize complex neural network architectures to achieve higher accuracy, which will suffer from overfitting, insufficient adaptability, and low recognition accuracy. To improve the existing phenomenon, a novel lightweight model named dual stream LSTM feature fusion classifier is proposed based on the concatenation of five time-domain features of EMG signals and raw data, which are both processed with one-dimensional convolutional neural networks and LSTM layers to carry out the classification. The proposed method can effectively capture global features of EMG signals using a simple architecture, which means less computational cost. An experiment is conducted on a public DB1 dataset with 52 gestures, and each of the 27 subjects repeats every gesture 10 times. The accuracy rate achieved by the model is 89.66%, which is comparable to that achieved by more complex deep learning neural networks, and the inference time for each gesture is 87.6 ms, which can also be implied in a real-time control system. The proposed model is validated using a subject-wise experiment on 10 out of the 40 subjects in the DB2 dataset, achieving a mean accuracy of 91.74%. This is illustrated by its ability to fuse time-domain features and raw data to extract more effective information from the sEMG signal and select an appropriate, efficient, lightweight network to enhance the recognition results.

An Exosuit System With Bidirectional Hand Support for Bilateral Assistance Based on Dynamic Gesture Recognition.

Hand motor impairment has seriously affected the daily life of the elderly. We developed an electromyography (EMG) exosuit system with bidirectional hand support for bilateral coordination assistance based on a dynamic gesture recognition model using graph convolutional network (GCN) and long short-term memory network (LSTM). The system included a hardware subsystem and a software subsystem. The hardware subsystem included an exosuit jacket, a backpack module, an EMG recognition module, and a bidirectional support glove. The software subsystem based on the dynamic gesture recognition model was designed to identify dynamic and static gestures by extracting the spatio-temporal features of the patient's EMG signals and to control glove movement. The offline training experiment built the gesture recognition models for each subject and evaluated the feasibility of the recognition model; the online control experiments verified the effectiveness of the exosuit system. The experimental results showed that the proposed model achieve a gesture recognition rate of 96.42% ± 3.26 %, which is higher than the other three traditional recognition models. All subjects successfully completed two daily tasks within a short time and the success rate of bilateral coordination assistance are 88.75% and 86.88%. The exosuit system can effectively help patients by bidirectional hand support strategy for bilateral coordination assistance in daily tasks, and the proposed method can be applied to various limb assistance scenarios.

A Novel Application of Deep Learning (Convolutional Neural Network) for Traumatic Spinal Cord Injury Classification Using Automatically Learned Features of EMG Signal.

In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesion from five Macaca fasicularis monkeys. The proposed classifier is based on a CNN using filtered segmented EMG signals from the pre- and post-lesion periods as inputs, while the kNN is designed using four hand-crafted EMG features. The results suggest that the CNN provides a promising classification technique for TSCI, compared to conventional machine learning classification. The kNN with hand-crafted EMG features classified the pre- and post-lesion EMG data with an F-measure of 89.7% and 92.7% for the left- and right-side muscles, respectively, while the CNN with the EMG segments classified the data with an F-measure of 89.8% and 96.9% for the left- and right-side muscles, respectively. Finally, the proposed deep learning classification model (CNN), with its learning ability of high-level features using EMG segments as inputs, shows high potential and promising results for use as a TSCI classification system. Future studies can confirm this finding by considering more subjects.

APPLICATION OF BACK PROPAGATION NEURAL NETWORK IN SPORTS FATIGUE INDICATORS

ABSTRACT Introduction High-intensity rehabilitation training will produce exercise fatigue. Objective A backpropagation (BP) network neural algorithm is proposed to predict sports fatigue based on electromyography (EMG) signal images. Methods The principal component analysis algorithm is used to reduce the dimension of EMG signal features. The knee joint angle is estimated by the regularized over-limit learning machine algorithm and the BP neural network algorithm. Results The RMSE value of the regularized over-limit learning machine algorithm is lower than that of the BP neural network algorithm. At the same time, the ρ value of the regularized over-limit learning machine algorithm is closer to 1, indicating its higher accuracy. Conclusions The model training time of the regularized over-limit learning machine algorithm has been greatly reduced, which improves efficiency. Level of evidence II; Therapeutic studies - investigation of treatment results.

Improving Gaussianity of EMG Envelope for Myoelectric Robot Arm Control

Several methods have been developed in biomedical signal processing to extract the envelope and features of electromyography (EMG) signals and predict human motion. Also, efforts were made to use this information to improve the interaction of a human body and artificial protheses. The main operations here are envelope acquiring, artifacts filtering, estimate smoothing, EMG value standardizing, feature classifying, and motion recognizing. In this paper, we employ EMG data to extract the envelope with a highest Gaussianity using the rectified signal, where we deal with the absolute EMG signals so that all values become positive. First, we remove artifacts from EMG data by using filters such as the Kalman filter (KF), H1 filter, unbiased finite impulse response (UFIR) filter, and the cKF, cH1 filter, and cUFIR filter modified for colored measurement noise. Next, we standardize the EMG envelope and improve the Gaussianity. Finally, we extract the EMG signal features to provide an accurate prediction.

EMG-Based 3D Hand Motor Intention Prediction for Information Transfer from Human to Robot.

(1) Background: Three-dimensional (3-D) hand position is one of the kinematic parameters that can be inferred from Electromyography (EMG) signals. The inferred parameter is used as a communication channel in human–robot collaboration applications. Although its application from the perspective of rehabilitation and assistive technologies are widely studied, there are few papers on its application involving healthy subjects such as intelligent manufacturing and skill transfer. In this regard, for tasks associated with complex hand trajectories without the consideration of the degree of freedom (DOF), the prediction of 3-D hand position from EMG signal alone has not been addressed. (2) Objective: The primary aim of this study is to propose a model to predict human motor intention that can be used as information from human to robot. Therefore, the prediction of a 3-D hand position directly from the EMG signal for complex trajectories of hand movement, without the direct consideration of joint movements, is studied. In addition, the effects of slow and fast motions on the accuracy of the prediction model are analyzed. (3) Methods: This study used the EMG signal that is collected from the upper limb of healthy subjects, and the position signal of the hand while the subjects manipulate complex trajectories. We considered and analyzed two types of tasks with complex trajectories, each with quick and slow motions. A recurrent fuzzy neural network (RFNN) model was constructed to predict the 3-D position of the hand from the features of EMG signals alone. We used the Pearson correlation coefficient (CC) and normalized root mean square error (NRMSE) as performance metrics. (4) Results: We found that 3-D hand positions of the complex movement can be predicted with the mean performance of CC = 0.85 and NRMSE = 0.105. The 3-D hand position can be predicted well within a future time of 250 ms, from the EMG signal alone. Even though tasks performed under quick motion had a better prediction performance; the statistical difference in the accuracy of prediction between quick and slow motion was insignificant. Concerning the prediction model, we found that RFNN has a good performance in decoding for the time-varying system. (5) Conclusions: In this paper, irrespective of the speed of the motion, the 3-D hand position is predicted from the EMG signal alone. The proposed approach can be used in human–robot collaboration applications to enhance the natural interaction between a human and a robot.

Differentiation of fluctuations in uterine contractions associated with Term pregnancies using adaptive fractal features of electromyography signals

Analysis of uterine contractions using electromyography signals is gaining importance due to its capability to measure the dynamics of uterus. Uterine electromyography (uEMG) provides information on the nature of uterine contractions non-invasively. In this study, the fluctuations in uEMG signals associated with Term pregnancies are analyzed. For this, Term uEMG signals corresponding to second (T1) and third (T2) trimesters are considered. The signals are subjected to Adaptive Fractal Analysis (AFA), wherein a global trend is obtained by using overlapping windows of three orders namely, 25%, 50% and 75%. The signals are detrended and the fluctuation function is estimated. Two Hurst exponent features computed at short range (Hs) and long range (Hl) are extracted and statistically analyzed. Results show that AFA is able to characterize variations in the fluctuations of Term delivery signals. The feature values are observed to vary significantly during different weeks of gestation. It is found that features of T2 signals are higher than that of T1 signals for all the considered overlaps, indicating that T2 signals possess smoother characteristics than T1 signals. Further, coefficient of variation is observed to be low, indicating that these features are able to handle the inter-subject variations in Term signals. Therefore, it appears that the proposed approach could aid in investigation of progressive changes in uterine contractions during Term pregnancies.

EMG SIGNAL DENOISING USING ADAPTIVE FILTERS THROUGH HYBRID OPTIMIZATION ALGORITHMS

Electromyography (EMG) signal recording equipment is comparatively modern. Still, there are enough restrictions in detection, recording, and characterization of EMG signals because of nonlinearity in the equipment, which leads to noise components. The most commonly affecting artifacts are Power Line Interference (PLI-Noise), Baseline Wander noise (BW-Noise), and Electrocardiogram noise (ECG-Noise). Adaptive filters are advanced and effective solutions for EMG signal denoising, but the improper tuning of filter coefficients leads to noise components in the denoised EMG signal. This defect in adaptive filters triggers or motivates us to optimize the filter coefficients with existing meta-heuristics optimization algorithms. In this paper, Least Mean Squares (LMS) filter and Recursive Least Squares (RLS) adaptive filter coefficients are optimized with a new Hybrid Firefly–Particle Swarm Optimization (HFPSO) by taking the advantages and disadvantages of both the algorithms. Experiments are conducted with the proposed HFPSO and it proved better in EMG signal denoising in terms of the measured parameters like signal-to-noise ratio (SNR) in dB, maximum error (ME), mean square error (MSE), etc. In the second part of the work, the denoised EMG signal features are extracted for the diagnosis of diseases related to myopathy and neuropathy as EMG signal reflects the neuromuscular function and EMG signal examination may contribute to the diagnosis of muscle disorder linked to myopathy and neuropathy.

Analysis of Fluctuations of Uterine Contractions in Preterm Pregnancies using Adaptive Fractal Features of Electromyography Signals

Analysis of fluctuations of uterine contractions under varied gestational ages is clinically significant. In this work, fluctuations associated with Preterm pregnancies are analyzed. For this, uterine electromyography (uEMG) signals in Preterm condition with varied gestational ages are considered. The signals are subjected to Adaptive Fractal Analysis (AFA) where a global trend is identified using overlapping windows of varying orders. The signal is detrended and fluctuation function is estimated. Hurst exponent features are extracted and analyzed statistically. Results show that AFA is able to characterize the variations in the fluctuations of Preterm uEMG signals. The feature values are distinct and vary with gestational age. Coefficient of variation is observed to be low, indicating that these features could handle inter-subject variability of Preterm signals. As early diagnosis of premature delivery is imperative for timely medical intervention and treatment, it appears that the proposed features could aid in determining the changes in uterine muscle contractions in Preterm condition.

Determining Relevant Features in Activity Recognition Via Wearable Sensors on the MYO Armband

The applications of wearable sensors in daily life are increasing day by day. The wearable sensor technologies provide a wide range of knowledge that can be utilized by human gesture/action systems. Although the electromyography (EMG) sensors, which are one of the most simple and effective wearable sensors, are usually employed in especially medical diagnosis, it holds a great amount of information (e.g., activity descriptors) regarding human activities. This study aims to evaluate the applicability of EMG signals in action recognition by identifying the relevant features of EMG signals alongside their commonly used properties. These (relevant) features are categorized into general basic statistical variables in time/frequency/aLBP/uniform aLBP domains, special statistical features in time and frequency domains, AR coefficients, and features in the wavelet domain. In order to evaluate and validate the applicability of EMG signal, a set of features is extracted from a wide range of datasets established by the authors of this paper in a way of describing distinct human actions (total of 132 distinct features). The study determines the dominant features for human action recognition systems by proposing a new wearable sensor-based methodology that exploits feature selection algorithms. Hence, the determination of the relevancy order of the features is one of the significant outcomes of this study. The results figure out that the most relevant ones are the metrics that detect fluctuations, i.e., a higher amount of entropy, belonging to the signal.

FEATURE SELECTION OF ELECTROMYOGRAPHY SIGNALS FOR AUTISM SPECTRUM DISORDER CHILDREN DURING GAIT USING MANN-WHITNEY TEST

Autism Spectrum Disorder is a lifelong neurodevelopmental impairment that affects brain growth and individual functional capabilities that associates with unusual movement and gait disturbance. The aim of this study is to investigate the significant features of EMG signals for lower limbs and arms muscle between Autism Spectrum Disorder (ASD) and Typical Development (TD) Children during walking. In this study, 30 ASD and 30 Typical Development (TD) children aged between 6 to 13 years old were asked to walk on the walkway naturally. The Electromyography (EMG) signals of Biceps Femoris (BF), Rectus Femoris (RF), Tibialis Anterior (TA), Gastrocnemius (GAS), Biceps Brachii (BB) and Tricep Brachii (TB) muscles of the ASD and TD children were recorded by using surface EMG sensors. The BF muscle is located at the posterior compartment of the thigh whereas the RF muscle located in the anterior compartment of the thigh. On the other hand, the TA muscle originates within the anterior compartment of the leg, and Gas muscle originates at the posterior compartment of the calf. Meanwhile, the BB muscle is in the front of the upper arm between shoulder and elbow, and TB muscle is a large muscle on the back of the upper arm limb. The data consists of 42 features from 7 walking phases of 6 muscles during one gait cycle were obtained from the data collection. Firstly, the data will be normalized to one gait cycle to standardize the length of EMG signals used for all subjects. Then, the feature selection method using Mann-Whitney Test is applied to find the significant features to differentiate between ASD and TD children from the EMG signals. Out of 42 features, 5 were found to be the most significant features of EMG signals between ASD and TD children, there are TA muscle at 30% of gait cycle, Gas muscle at 50% and 60% of gait cycle, and BB muscle at 10% and 80% of gait cycle with significant values of 0.017, 0.049, 0.034, 0.021 and 0.003, respectively. These findings are useful to both clinicians and parents as the lower limbs and arm muscles can be valuable therapeutic parameter for ASD children’s rehabilitation plan. The findings of this research also suggest that the significant difference of EMG signals obtained can be a parameter to differentiate between ASD and TD children.

Time-frequency EMG features comparison of biceps brachii and erector spinae in evaluating work level categories of RTW patients

Electromyography (EMG) is one of the special tool in assessing human muscle performance. The muscle performance can be evaluated when there exist changes on both the amplitude and frequency simultaneously. However, various time-frequency EMG signal features from literature do not provide easy and direct interpretation of the muscle condition specifically for non-researchers in rehabilitation centers. This study investigates EMG behavior of biceps brachii and erector spinae based on new time-frequency EMG features during functional capacity evaluation (FCE)’s core-lifting task in evaluating work level categories of return to work patients. Surface EMG signals are filtered using band pass filter with cut-off frequency at 5-500 Hz and auto-segmented using spectrogram, while S-transform is used for the features extraction. Result shows that the differences of the proposed EMG features values are significant in indexing muscle performance and are good indicators for the determination of work level categories.

Driver drowsiness detection based on classification of surface electromyography features in a driving simulator.

Driver drowsiness is a significant cause of fatal crashes every year in the world. In this research, driver's drowsiness is detected by classifying surface electromyography signal features. The tests are conducted on 13 healthy subjects in a driving simulator with a monotonous route. The surface electromyography signal from the upper arm and shoulder muscles are measured including mid deltoid, clavicular portion of the pectoralis major, and triceps and biceps long heads. Signals are separated into 30-s epochs. Five features including range, variance, relative spectral power, kurtosis, and shape factor are extracted. The Observer Rating of Drowsiness evaluates the level of drowsiness. A binormal function is fitted for each feature. For classification, six classifiers are applied. The results show that the k-nearest neighbor classifier predicts drowsiness by 90% accuracy, 82% precision, 77% sensitivity, and 92% specificity.

An upper limb movement estimation from electromyography by using BP neural network

Abstract The body electromyography (EMG) signals contain a large amount of information related to the movement of the human body. Identifying the patients’ movement intention from the EMG signals is the key to controlling the exoskeleton to assist their movement. In order to accurately extract the information about the patients’ movement intention from the EMG signals, we preprocessed the EMG signals including signals amplification, denoising, biasing and normalization. Then we extracted the features of EMG signals from the time domain, frequency domain, and time-frequency domain respectively. Based on the features obtained, we used the Matlab neural network toolbox to train BP neural network and tested the established continuous movement control model. The results suggested that the angles estimated by the continuous movement control model had smaller errors. In addition, instead of the traditional working mode that used the PC to process the EMG signals, we used the STM32 microcontroller to perform real-time control of the upper limb exoskeleton, which greatly reduced the size of the control equipment and provided convenience for the patients’ rehabilitation training.

Analysis of surface electromyography signal features on osteomyoplastic transtibial amputees for pattern recognition control architectures

Abstract This paper presents the characterisation of electromyography signals for the purpose of controlling a powered prosthetic ankle using pattern recognition algorithms. The goal is to identify the specific muscles that can be used to guarantee optimal control of a multichannel powered prosthetic ankle. SENIAM and ISEK protocols were used for signal acquisition, processing and reporting. A set of paired surface electrodes were placed above selected muscles on the residual limb. Participants were instructed to perform normal gait. The signals were recorded, labelled and analysed using the Vicon Nexus Motion Capturing System and Noraxon Myomotion System. Signal processing was performed using MR3 Software and further post processing was performed using Matlab. Time and frequency domain features were analysed. The protocol revealed that the tibialis anterior, medial and lateral gastrocnemius muscles actively generate myoelectric signals on the residual limb. A total of 12 time domain and 4 frequency domain features were successfully extracted and used in the analysis. The tibialis anterior muscle was identified as a candidate for classifying dorsiflexion with a mean amplitude of 35.08 μV. The soleus muscle was inaccessible on the amputated leg and as a result only the medial and lateralis gastrocnemius muscles, with 17.40% signal power and 43.73% mean amplitude as compared to the soleus, were available for plantarflexion. There was significant difference (p 0.05) between signal features from two different participants. Sagittal plane movements were linearly discriminated with 100% accuracy for tibialis anterior and medial gastrocnemius. However, lateralis gastrocnemius exhibited a 0.0769% classification error as a result of the amputation technique.

Comparative evaluation of EMG signal features for myoelectric controlled human arm prosthetics

Myoelectric controlled human arm prosthetics have shown a promising performance with regards to the supplementation of the basic manipulation requirements for amputated people over recent years. However these assistive devices still have important restrictions in enabling amputated people to perform rather sophisticated or functional movements. Surface electromyography (EMG) is used as the control signal to command such prosthetic devices to ensure the amputated people to compensate their fundamental movement patterns. The ability of extraction of clear and certain neural information from EMG signals is a critical issue in fine control of hand prosthesis movements. Various signal processing methods have been employed for feature extraction from EMG signals. In this study, it was aimed to comparatively evaluate the widely used time domain EMG signal features, i.e., integrated EMG (IEMG), root mean square (RMS), and waveform length (WL) in estimation of externally applied forces to human hands. Once the signal features were extracted, classification process was applied to predict the external forces using artificial neural networks (ANN). EMG signals were recorded during two types of muscle contraction: (i) isometric and isotonic, and (ii) anisotonic and anisometric contractions. Experiments were implemented by six healthy subjects from the muscles that are proximal to the upper body, i.e., biceps brachii, triceps brachii, pectorialis major and trapezius. The force prediction results obtained from the ANN were statistically evaluated and, merits and shortcomings of the features were discussed. Findings of the study are expected to provide better insight regarding control structure of the EMG-based motion assistive devices.

Hybrid BF–PSO and fuzzy support vector machine for diagnosis of fatigue status using EMG signal features

In this study, a novel BF–PSO–FSVCM model has been proposed to identify the fatigue status of the electromyography (EMG) signal. To improve the classifier accuracy of fuzzy support vector classification machine (FSVCM), a hybrid Bacterial Foraging (BF) and particle swarm optimization (PSO) is proposed to optimize the unknown parameters of the classifier. In the proposed method, the EMG signals are firstly decomposed by discrete wavelet transform (DWT), Fast Fourier Transformation (FFT) and Ensemble Empirical Mode Decomposition (EEMD)–Hilbert transform (HT), and then a set of combined features were extracted from different types of fatigue or normal EMG signals. The optimal fatigue vectors of static, local and dynamic fatigue are also provided in this study. The obtained results obviously indicate that further significant enhancements in terms of classification accuracy can be achieved by the proposed BF–PSO–FSVCM classification system. BF–PSO–FSVCM is developed as an efficient tool so that various support vector classification machines (SVCMs) can be used conveniently as the core of BF–PSO–FSVCM for diagnosis of fatigue status.

A Self-adaptive Threshold Method for Automatic Sleep Stage Classification Using EOG and EMG

Sleep stages are generally divided into three stages including Wake, REM and NRME. The standard sleep monitoring technology is Polysomnography (PSG). The inconvenience for PSG monitoring limits the usage of PSG in some areas. In this study, we developed a new method to classify sleep stage using electrooculogram (EOG) and electromyography (EMG) automatically. We extracted right and left EOG features and EMG feature in time domain, and classified them into strong, weak and none types through calculating self-adaptive threshold. Combination of the time features of EOG and EMG signals, we classified sleep stages into Wake, REM and NREM stages. The time domain features utilized in the method were Integrate Value, variance and energy. The experiment of 30 datasets showed a satisfactory result with the accuracy of 82.93% for Wake, NREM and REM stages classification, and the average accuracy of Wake stage classification was 83.29%, 82.11% for NREM stage and 76.73% for REM stage.

A novel STFT-ranking feature of multi-channel EMG for motion pattern recognition

Electromyography (EMG) is widely applied for neural engineering. For motion pattern recognition, many features of multi-channel EMG signals were investigated, but the relationships between muscles were not considered. In this study, a novel STFT-ranking feature based on short-time Fourier transform (STFT) is proposed. The novelty of STFT-ranking features is considering and covering the relationship information between EMG signals and multiple muscles in a motion pattern. With an exoskeleton robot arm, two series of motion patterns corresponding to the shoulder and elbow in the sagittal plane were investigated. EMG signals from six muscles were acquired in arm motion patterns when participants worn the robot arm. Four types of feature combinations, including seven conventional features, were compared with the STFT-ranking feature. The principal component analysis (PCA) and support vector machine (SVM) were used to build the motion recognition model. With the STFT-ranking feature, the recognition performance (93.9%) is superior to the conventional features (33.3–90.8%). The recognition variation is smaller (SD=4.3%) than the other features tested (SD=5.9–13.8%). These achievements will contribute to the advancement of control method of exoskeleton robots or power orthoses based on multi-channel EMG signals in the future. Based on the principle of STFT-ranking feature, the method also has potential for other multi-channel signal applications, such as electroencephalography (EEG) signal processing, speech recognition, and acoustic analysis.

Estimation of Grasping Force from Features of Intramuscular EMG Signals with Mirrored Bilateral Training

This study investigates the use of features extracted from intramuscular electromyography (EMG) for estimating grasping force in the ipsilateral and contralateral (mirrored) hand, during bilateral grasping tasks. This is relevant since force estimation using mirror tasks is a potentially useful pathway for the clinical training of unilateral amputees. Bilateral grasping force and intramuscular EMG (wire electrodes) of the right forearm were measured in 10 able-bodied subjects. The features extracted from the EMG signal were the root mean square, the global discharge rate, the standard sample entropy, and the constraint sample entropy (CSE). The association between the EMG features and force was modeled using a first-order polynomial model, a second-order exponential model, and an artificial neural network (ANN). The accuracies of estimation of ipsilateral and mirrored grasping force were not significantly different (e.g., R(2) = 0.89 ± 0.02 for ipsilateral and 0.88 ± 0.017 for mirrored, when using CSE and the ANN). It was concluded that it is possible to use just one channel of intramuscular EMG for force estimation. This result suggests that intramuscular EMG signals may be suitable for proportional myoelectric control and that training of the association between intramuscular EMG features and force can be performed using mirror tasks, which is a needed condition for applications in unilateral amputees.