Improving detection of muscle activation intervals

Review of electromyography onset detection methods for real-time control of robotic exoskeletons

BACKGROUND

Electromyography (EMG) is a classical technique used to record electrical activity associated with muscle contraction and is widely applied in Biomechanics, Biomedical Engineering, Neuroscience and Rehabilitation Robotics. Determining muscle activation onset timing, which can be used to infer movement intention and trigger prostheses and robotic exoskeletons, is still a big challenge. The main goal of this paper was to perform a review of the state-of-the-art of EMG onset detection methods. Moreover, we compared the performance of the most commonly used methods on experimental EMG data.

METHODS

A total of 156 papers published until March 2022 were included in the review. The papers were analyzed in terms of application domain, pre-processing method and EMG onset detection method. The three most commonly used methods [Single (ST), Double (DT) and Adaptive Threshold (AT)] were applied offline on experimental intramuscular and surface EMG signals obtained during contractions of ankle and knee joint muscles.

RESULTS

Threshold-based methods are still the most commonly used to detect EMG onset. Compared to ST and AT, DT required more processing time and, therefore, increased onset timing detection, when applied on experimental data. The accuracy of these three methods was high (maximum error detection rate of 7.3%), demonstrating their ability to automatically detect the onset of muscle activity. Recently, other studies have tested different methods (especially Machine Learning based) to determine muscle activation onset offline, reporting promising results.

CONCLUSIONS

This study organized and classified the existing EMG onset detection methods to create consensus towards a possible standardized method for EMG onset detection, which would also allow more reproducibility across studies. The three most commonly used methods (ST, DT and AT) proved to be accurate, while ST and AT were faster in terms of EMG onset detection time, especially when applied on intramuscular EMG data. These are important features towards movement intention identification, especially in real-time applications. Machine Learning methods have received increased attention as an alternative to detect muscle activation onset. However, although several methods have shown their capability offline, more research is required to address their full potential towards real-time applications, namely to infer movement intention.

Benchmarking the Effects on Human-Exoskeleton Interaction of Trajectory, Admittance and EMG-Triggered Exoskeleton Movement Control

Nowadays, robotic technology for gait training is becoming a common tool in rehabilitation hospitals. However, its effectiveness is still controversial. Traditional control strategies do not adequately integrate human intention and interaction and little is known regarding the impact of exoskeleton control strategies on muscle coordination, physical effort, and user acceptance. In this article, we benchmarked three types of exoskeleton control strategies in a sample of seven healthy volunteers: trajectory assistance (TC), compliant assistance (AC), and compliant assistance with EMG-Onset stepping control (OC), which allows the user to decide when to take a step during the walking cycle. This exploratory study was conducted within the EUROBENCH project facility. Experimental procedures and data analysis were conducted following EUROBENCH's protocols. Specifically, exoskeleton kinematics, muscle activation, heart and breathing rates, skin conductance, as well as user-perceived effort were analyzed. Our results show that the OC controller showed robust performance in detecting stepping intention even using a corrupt EMG acquisition channel. The AC and OC controllers resulted in similar kinematic alterations compared to the TC controller. Muscle synergies remained similar to the synergies found in the literature, although some changes in muscle contribution were found, as well as an overall increase in agonist-antagonist co-contraction. The OC condition led to the decreased mean duration of activation of synergies. These differences were not reflected in the overall physiological impact of walking or subjective perception. We conclude that, although the AC and OC walking conditions allowed the users to modulate their walking pattern, the application of these two controllers did not translate into significant changes in the overall physiological cost of walking nor the perceived experience of use. Nonetheless, results suggest that both AC and OC controllers are potentially interesting approaches that can be explored as gait rehabilitation tools. Furthermore, the INTENTION project is, to our knowledge, the first study to benchmark the effects on human-exoskeleton interaction of three different exoskeleton controllers, including a new EMG-based controller designed by us and never tested in previous studies, which has made it possible to provide valuable third-party feedback on the use of the EUROBENCH facility and testbed, enriching the apprenticeship of the project consortium and contributing to the scientific community.

Electromyography-based fatigue assessment of an upper body exoskeleton during automotive assembly

The purpose of this study was to assess an upper body exoskeleton during automotive assembly processes that involve elevated arm postures. Sixteen team members at Toyota Motor Manufacturing Canada were fitted with a Levitate Airframe, and each team member performed between one and three processes with and without the exoskeleton. A total of 16 assembly processes were studied. Electromyography (EMG) data were collected on the anterior deltoid, biceps brachii, upper trapezius, and erector spinae. Team members also completed a usability survey. The exoskeleton significantly reduced anterior deltoid mean active EMG amplitude (p = .01, Δ = -3.2 %MVC, d = 0.56 medium effect) and fatigue risk value (p < .01, Δ = -5.1 %MVC, d = 0.62 medium effect) across the assembly processes, with no significant changes for the other muscles tested. A subset of nine assembly processes with a greater amount of time spent in arm elevations at or above 90° (30 vs. 24%) and at or above 135° (18 vs. 9%) appeared to benefit more from exoskeleton usage. For these processes, the exoskeleton significantly reduced anterior deltoid mean active EMG amplitude (p < .01, Δ = -5.1 %MVC, d = 0.95 large effect) and fatigue risk value (p < .01, Δ = -7.4 %MVC, d = 0.96 large effect). Team members responded positively about comfort and fatigue benefits, although there were concerns about the exoskeleton hindering certain job duties. The results support quantitative testing to match exoskeleton usage with specific job tasks and surveying team members for perceived benefits/drawbacks.

Ankle reaction times with tray usage following a slip perturbation between subjects with and without chronic low back pain

BACKGROUND

Abnormal stepping strategies have been associated with handheld tasks in subjects with chronic low back pain (LBP). However, the dominant ankle reactions of subjects with LBP remain unclear following a perturbation during handheld tasks.

RESEARCH QUESTION

Are there differences in the reaction times of the ankle muscles during handheld tasks between subjects with and without LBP following a treadmill-induced slip perturbation?

METHODS

Thirty-seven right limb dominant subjects with LBP and 37 subjects without LBP participated in the study. Each subject was introduced to a slip perturbation (1.37 m/sec velocity for 8.22 cm) with and without a handheld tray in random order. Subjects were allowed to recover by stepping forward for a 0.12 s duration while bilateral tibialis anterior (TA) and gastrocnemius (GA) muscle reaction times were measured by electromyography (EMG).

RESULTS

The EMG results indicated that the groups demonstrated significant interactions on the limb sides and muscles (F = 4.86, p = 0.03). The dominant TA reaction time was significantly faster in the LBP group (t = 2.14, p = 0.03) while holding a tray.

SIGNIFICANCE

The LBP group demonstrated faster reaction times on the dominant TA muscles during perturbations. Clinicians need to consider dominance-dependent compensatory ankle dorsiflexion strategies in LBP patients to help enhance dynamic balance and control.

Comparison between the Original- and a Standardized Version of a Physical Assessment Test for the Dorsal Chain - A Cohort-Based Cross Sectional Study

This cohort-based cross-sectional study compares the original (OV) and a newly developed standardized version (SV) of the Bunkie Test, a physical test used to assess the dorsal chain muscles. Twenty-three participants (13 females, 10 males; median age of 26 ± 3 years) performed the test, a reverse plank, with one foot on a stool and the contralateral leg lifted. In the SV, the position of the pelvis and the foot were predefined. The test performance time (s) and surface electromyography (sEMG) signals of the dorsal chain muscles were recorded. We performed a median power frequency (MPF) analysis, using short-time Fourier transformation, and calculated the MPF/time linear regression slope. We compared the slopes of the linear regression analysis (between legs) and the performance times (between the OV and SV) with the Wilcoxon test. Performance times did not differ between SV and OV for either the dominant (p = 0.28) or non-dominant leg (p = 0.08). Linear regression analysis revealed a negative slope for the muscles of the tested leg and contralateral erector spinae, with a significant difference between the biceps femoris of the tested (-0.91 ± 1.08) and contralateral leg (0.01 ± 1.62) in the SV (p = 0.004). The sEMG showed a clearer pattern in the SV than in the OV. Hence, we recommend using the SV to assess the structures of the dorsal chain of the tested leg and contralateral back.

Thinking out loud, an open-access EEG-based BCI dataset for inner speech recognition

Surface electroencephalography is a standard and noninvasive way to measure electrical brain activity. Recent advances in artificial intelligence led to significant improvements in the automatic detection of brain patterns, allowing increasingly faster, more reliable and accessible Brain-Computer Interfaces. Different paradigms have been used to enable the human-machine interaction and the last few years have broad a mark increase in the interest for interpreting and characterizing the “inner voice” phenomenon. This paradigm, called inner speech, raises the possibility of executing an order just by thinking about it, allowing a “natural” way of controlling external devices. Unfortunately, the lack of publicly available electroencephalography datasets, restricts the development of new techniques for inner speech recognition. A ten-participant dataset acquired under this and two others related paradigms, recorded with an acquisition system of 136 channels, is presented. The main purpose of this work is to provide the scientific community with an open-access multiclass electroencephalography database of inner speech commands that could be used for better understanding of the related brain mechanisms.

Measurement(s)	brain activity • inner speech command
Technology Type(s)	electroencephalography
Sample Characteristic - Organism	Homo sapiens

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.16783987

Onset and Offset Detection of Respiratory EMG Data Based on Energy Operator Signal

Onset and offset detection of electromyography (EMG) data is an important step in respiratory muscle coordination assessment. Impaired respiratory coordination can indicate breathing disorders and lung diseases. In this paper, we present an algorithm for onset and offset timing detection of real-world EMG signals from respiratory muscles, which are contaminated with electrocardiogram (ECG) artifacts. The algorithm is based on the Energy Operator signal, has a low computational cost, and includes a filtering procedure to remove ECG artifacts from EMG. Analysis of EMG signals from 2 respiratory muscles of 5 participants' data shows high agreement between the algorithm and manual method with a mean difference between two methods of 0.0407 seconds.

Wearable Sensors in Sports for Persons with Disability: A Systematic Review

The interest and competitiveness in sports for persons with disabilities has increased significantly in the recent years, creating a demand for technological tools supporting practice. Wearable sensors offer non-invasive, portable and overall convenient ways to monitor sports practice. This systematic review aims at providing current evidence on the application of wearable sensors in sports for persons with disability. A search for articles published in English before May 2020 was performed on Scopus, Web-Of-Science, PubMed and EBSCO databases, searching titles, abstracts and keywords with a search string involving terms regarding wearable sensors, sports and disability. After full paper screening, 39 studies were included. Inertial and EMG sensors were the most commonly adopted wearable technologies, while wheelchair sports were the most investigated. Four main target applications of wearable sensors relevant to sports for people with disability were identified and discussed: athlete classification, injury prevention, performance characterization for training optimization and equipment customization. The collected evidence provides an overview on the application of wearable sensors in sports for persons with disability, providing useful indication for researchers, coaches and trainers. Several gaps in the different target applications are highlighted altogether with recommendation on future directions.

Gait analysis in neurological populations: Progression in the use of wearables

Gait assessment is an essential tool for clinical applications not only to diagnose different neurological conditions but also to monitor disease progression as it contributes to the understanding of underlying deficits. There are established methods and models for data collection and interpretation of gait assessment within different pathologies. This narrative review aims to depict the evolution of gait assessment from observation and rating scales to wearable sensors and laboratory technologies and provide limitations and possible future directions in the field of gait assessment. In this context, we first present an extensive review of current clinical outcomes and gait models. Then, we demonstrate commercially available wearable technologies with their technical capabilities along with their use in gait assessment studies for various neurological conditions. In the next sections, a descriptive knowledge for existing inertial and EMG based algorithms and a sign based guide that shows the outcomes of previous neurological gait assessment studies are presented. Finally, we state a discussion for the use of wearables in gait assessment and speculate the possible research directions by revealing the limitations and knowledge gaps in the literature.

Event Detection of Muscle Activation Using an Electromyogram

In this study, we proposed a precise onset and offset detection algorithm for muscle activation by using an electromyogram (EMG). The adaptive threshold was determined using the constant false alarm rate algorithm. The EMG signal was refined by morphological hole filling, which is used to close up and fill out missing information. By exploiting the EMG amplitude ratio in two channels, we significantly improved the offset detection performance. The proposed method does not require a training process, unlike conventional methods. The experimental results indicated that the estimated errors for both the onset and offset detection are lower than those obtained using two of the conventional methods.

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.

Benefits of pallidal stimulation in dystonia are linked to cerebellar volume and cortical inhibition

Clinical benefits of pallidal deep brain stimulation (GPi DBS) in dystonia increase relatively slowly suggesting slow plastic processes in the motor network. Twenty-two patients with dystonia of various distribution and etiology treated by chronic GPi DBS and 22 healthy subjects were examined for short-latency intracortical inhibition of the motor cortex elicited by paired transcranial magnetic stimulation. The relationships between grey matter volume and intracortical inhibition considering the long-term clinical outcome and states of the GPi DBS were analysed. The acute effects of GPi DBS were associated with a shortening of the motor response whereas the grey matter of chronically treated patients with a better clinical outcome showed hypertrophy of the supplementary motor area and cerebellar vermis. In addition, the volume of the cerebellar hemispheres of patients correlated with the improvement of intracortical inhibition which was generally less effective in patients than in controls regardless of the DBS states. Importantly, good responders to GPi DBS showed a similar level of short-latency intracortical inhibition in the motor cortex as healthy controls whereas non-responders were unable to increase it. All these results support the multilevel impact of effective DBS on the motor networks in dystonia and suggest potential biomarkers of responsiveness to this treatment.

Verification of Computed Muscle Control and Static Optimization for Isokinetic, Isometric and Isotonic Exercise of Upper Limb

To measure muscle activity during motion is a significant topic in biomechanics. Even though EMG (electromyography) is utilized for this, musculoskeletal simulations are potential alternatives. In this paper, the accuracy of muscle activity calculation of two different algorithms are verified by comparing with EMG during three types of muscle contractions (isokinetic, isotonic and isometric).

Unsupervised Stochastic Strategies for Robust Detection of Muscle Activation Onsets in Surface Electromyogram

Surface electromyographic (sEMG) data impart valuable information concerning muscle function and neuromuscular diseases especially under human movement conditions. However, they are subject to trial-wise and subject-wise variations, which would pose challenges for investigators engaged in precisely estimating the onset of muscle activation. To this end, we posited two unsupervised statistical approaches- scree-plot elbow detection (SPE) heavily relying on the threshold choice and the more robust profile likelihood maximization (PLM) that obviates parameter tuning-for accurately detecting muscle activation onsets (MAOs). The performance of these algorithms was evaluated using the sEMG dataset provided in the article by Tenan et al. and the simulated sEMG created as explained therein. These sEMG signals are reported to have been collected from the biceps brachii and vastus lateralis of 18 participants while performing a biceps curl or knee extension, respectively. The acquired sEMG signals were first preconditioned with the Teager-Kaiser energy operator, and then, either supplied to the SPE or to the PLM or to a state-of-the-art algorithm. The mean and median errors, between the MAO time in milliseconds estimated by each of the algorithms and the gold standard onset time, were computed. The outcome of a PLM variant, namely, PLM-Laplacian, has been found to have good agreement with the gold standard, i.e., an absolute median error of 9 and 21 ms in the simulated and the actual sEMG data, respectively; whereas, the errors produced by the other algorithms are statistically significantly larger than that incurred by the PLM-Laplacian according to Wilcoxon rank-sum test. In addition, the advocated approach does not necessitate parameter settings, lending itself to be flexible and adaptable to any application, which is a unique advantage over several other methods. Research is underway to further validate this technique by imposing various experimental conditions.

Mechanisms of head stability during gait initiation in young and older women: A neuro-mechanical analysis

Decreased head stability has been reported in older women during locomotor transitions such as the initiation of gait. The aim of the study was to investigate the neuro-mechanical mechanisms underpinning head stabilisation in young and older women during gait initiation. Eleven young (23.1 ± 1.1 yrs) and 12 older (73.9 ± 2.4 yrs) women initiated walking at comfortable speed while focussing on a fixed visual target at eye level. A stereophotogrammetric system was used to assess variability of angular displacement and RMS acceleration of the pelvis, trunk and head, and dynamic stability in the anteroposterior and mediolateral directions. Latency of muscle activation in the sternocleidomastoid, and upper and lower trunk muscles were determined by surface electromyography. Older displayed higher variability of head angular displacement, and a decreased ability to attenuate accelerations from trunk to head, compared to young in the anteroposterior but not mediolateral direction. Moreover, older displayed a delayed onset of sternocleidomastoid activation than young. In conclusion, the age-related decrease in head stability could be attributed to an impaired ability to attenuate accelerations from trunk to head along with delayed onset of neck muscles activation.

An adapted double threshold protocol for spastic muscles

The onset of muscle contraction has been an important element in the understanding of human motor control system as well as in the development of medical devices. This task is problematic in the study of spasticity using surface Electromyography (sEMG). In fact, spasticity is characterized by involuntary muscle contractions that can be seen as both, a non-stationary background if they are weak or a severe non-stationary EMG signal if they are strong. In other hand, these sEMG signals present a very low signal to noise ratio, beyond the added noise that contaminates this signal. The double threshold protocol presumes non-stationary muscle activation under a stationary environment which does not accommodate non-stationary background conditions. Apart from that the Shewhart protocol which makes part of the Double Threshold Protocol (DTP) presumes an initial segment containing only noise which can't be guaranteed under spastic conditions. These are the main causes of failures of state of the art approaches when applied to sEMG spastic muscles. This paper proposes dealing with these constraints by adapting the first threshold to the noise conditions via Signal to Noise Ratio (SNR) estimation, which depends on the severity of the disease. The main idea is tuning the first threshold to low SNR conditions since it is where the DTP most degrades. This tuning is done in sEMG artificially contaminated at different SNRs where the multiple of standard deviation is heuristically determined based on experimentation. Noise is estimated in low energy segments instead of in an initial segment that can be contaminated by involuntary muscle contractions. The proposed algorithm was tested in sEMG signals from the Biceps Braquialis of 13 healthy individuals and in 9315 signals recorded in 23 subjects with spasticity. Improvements of more than 23% were obtained when compared with the classical DTP in moderate to severe spasticity.

EMG burst presence probability: a joint time-frequency representation of muscle activity and its application to onset detection

The purpose of this study was to quantify muscle activity in the time-frequency domain, therefore providing an alternative tool to measure muscle activity. This paper presents a novel method to measure muscle activity by utilizing EMG burst presence probability (EBPP) in the time-frequency domain. The EMG signal is grouped into several Mel-scale subbands, and the logarithmic power sequence is extracted from each subband. Each log-power sequence can be regarded as a dynamic process that transits between the states of EMG burst and non-burst. The hidden Markov model (HMM) was employed to elaborate this dynamic process since HMM is intrinsically advantageous in modeling the temporal correlation of EMG burst/non-burst presence. The EBPP was eventually yielded by HMM based on the criterion of maximum likelihood. Our approach achieved comparable performance with the Bonato method.

A computational method for the detection of activation/deactivation patterns in biological signals with three levels of electric intensity

In the present work, we develop a computational technique to approximate the changes of phase in temporal series associated to electric signals of muscles which perform activities at three different levels of intensity. We suppose that the temporal series are samples of independent, normally distributed random variables with mean equal to zero, and variance equal to one of three possible values, each of them associated to a certain degree of electric intensity. For example, these intensity levels may represent a leg muscle at rest, or active during a light activity (walking), or active during a highly demanding performance (jogging). The model is presented as a maximum likelihood problem involving discrete variables. In turn, this problem is transformed into a continuous one via the introduction of continuous variables with penalization parameters, and it is solved recursively through an iterative numerical method. An a posteriori treatment of the results is used in order to avoid the detection of relatively short periods of silence or activity. We perform simulations with synthetic data in order to assess the validity of our technique. Our computational results show that the method approximates well the occurrence of the change points in synthetic temporal series, even in the presence of autocorrelated sequences. In the way, we show that a generalization of a computational technique for the change-point detection of electric signals with two phases of activity (Esquivel-Frausto et al., 2010 [40]), may be inapplicable in cases of temporal series with three levels of intensity. In this sense, the method proposed in the present manuscript improves previous efforts of the authors.

Accuracy and learning curves of inexperienced observers for manual segmentation of electromyograms

INTRODUCTION: The shape-varying format of surface electromyograms introduces errors in the detection of contraction events. OBJECTIVE: To investigate the accuracy and learning curves of inexperienced observers to detect the quantity of contraction events in surface electromyograms. MATERIALS AND METHODS: Six observers performed manual segmentation in 1200 shape-varying waveforms simulated using a phenomenological model with variable events, smooth changes in amplitude, marked on-off timing, and variable signal-to-noise ratio (0-39 dB). Segmentation was organized in four sessions with 15 blocks of 20 signals each. Accuracy and learning curves were modeled per block by linear and power regression models and tested for difference among sessions. Cut-off values of signal-to-noise ratio for optimal manual segmentation were also estimated. RESULTS: The accuracy curve showed no significant linear trend throughout blocks and no difference among sessions 1-2-3-4 (87% [85; 89], 87% [85; 89], 87% [85; 89], 87% [81; 88]; p = 0.691). Accuracy was low for detection of 1 event (AUC = 0.40; sensitivity = 44%; specificity = 43%; cut-off = 12.9 dB) but was high and affected by the signal-to-noise ratio for detection of two events (AUC = 0.82; sensitivity = 77%; specificity = 76%; cut-off = 7.0 dB). The learning curve showed a significant power regression (p < 0.001) with decreasing values of learning percentages (time duration to complete the task) among sessions 1-2-3-4 (86.5% [68; 94], 76% [68; 91], 62% [38; 77], and 57% [52; 75]; p = 0.002). CONCLUSION: Inexperienced observers exhibit high, not trainable accuracy and a practice-dependent shortening in the time spent to detect the quantity of contraction events in simulated surface electromyograms.

An adaptive algorithm for the determination of the onset and offset of muscle contraction by EMG signal processing

Estimation of on-off timing of human skeletal muscles during movement is an ongoing issue in surface electromyography (sEMG) signal processing for relevant clinical applications. Widely used single threshold methods still rely on the experience of the operator to manually establish a threshold level. In this paper, a novel approach to address this issue is presented. Based on the generalized likelihood ratio test, the maximum likelihood (ML) method is improved with an adaptive threshold technique based on the signal-to-noise ratio (SNR) estimate in the initial time before accurate sEMG analyses. The dependence of optimal threshold on SNR is determined by minimizing the onset/offset estimate error on a large set of simulated signals with well-known signal parameters. Accuracy and precision of the algorithm were assessed by using a set of simulated signals and real sEMG signals recorded from two healthy subjects during elbow flexion-extension movements with and without workload. Comparison with traditional algorithms shows that with a moderate increase in the computational effort the ML algorithm performs well even for low levels of EMG activity, while the proposed adaptive method is most robust with respect to variations in SNRs. Also, we discuss the results of analyzing the sEMG recordings from the selected proximal muscles of the upper limb in two hemiparetic subjects. The detection algorithm is automatic and user-independent, managing the detection of both onset and offset activation, and is applicable in presence of noise allowing use by skilled and unskilled operators alike.

Sample entropy analysis of surface EMG for improved muscle activity onset detection against spurious background spikes

Voluntary surface electromyogram (EMG) signal is sometimes contaminated by spurious background spikes of both physiological and extrinsic or accidental origins. A novel method of muscle activity onset detection against such spurious spikes was proposed in this study based primarily on the sample entropy (SampEn) analysis of the surface EMG. The method takes advantage of the nonlinear properties of the SampEn analysis to distinguish voluntary surface EMG signals from spurious background spikes in the complexity domain. To facilitate muscle activity onset detection, the SampEn analysis of surface EMG was first performed to highlight voluntary EMG activity while suppressing spurious background spikes. Then, a SampEn threshold was optimized for muscle activity onset detection. The performance of the proposed method was examined using both semi-synthetic and experimental surface EMG signals. The SampEn based methods effectively reduced the detection error induced by spurious background spikes and achieved improved performance over the methods relying on conventional amplitude thresholding or its extended version in the Teager Kaiser Energy domain.

Analysis and classification of compressed EMG signals by wavelet transform via alternative neural networks algorithms

We propose intelligent methods for classifying three different muscle types, i.e. biceps, frontallis and abductor pollicis brevis muscles, with low computational complexity. For this aim, electromyogram (EMG) signals are recorded and modelled by using an auto-regressive (AR) model. As the size of the EMG signals is usually large, the computational complexity of artificial neural network (ANN) systems drastically increases. Therefore, in the proposed scheme EMG signals are pre-processed by using a wavelet transform and then they are modelled by employing an AR approach. The AR coefficients are used to train and test the ANNs. Experimental results show that the highest achieved classification accuracy is more than 95% in the case of EMG signals pre-processed by wavelet transform. The wavelet transform-based pre-processing significantly increases the performance rates compared to standard multilayer perceptron and general regression neural networks algorithms.

Application of singular spectrum-based change-point analysis to EMG-onset detection

While many approaches have been proposed to identify the signal onset in EMG recordings, there is no standardized method for performing this task. Here, we propose to use a change-point detection procedure based on singular spectrum analysis to determine the onset of EMG signals. This method is suitable for automated real-time implementation, can be applied directly to the raw signal, and does not require any prior knowledge of the EMG signal's properties. The algorithm proposed by Moskvina and Zhigljavsky (2003) was applied to EMG segments recorded from wrist and trunk muscles. Wrist EMG data was collected from 9 Parkinson's disease patients with and without tremor, while trunk EMG data was collected from 13 healthy able-bodied individuals. Along with the change-point detection analysis, two threshold-based onset detection methods were applied, as well as visual estimates of the EMG onset by trained practitioners. In the case of wrist EMG data without tremor, the change-point analysis showed comparable or superior frequency and quality of detection results, as compared to other automatic detection methods. In the case of wrist EMG data with tremor and trunk EMG data, performance suffered because other changes occurring in these signals caused larger changes in the detection statistic than the changes caused by the initial muscle activation, suggesting that additional criteria are needed to identify the onset from the detection statistic other than its magnitude alone. Once this issue is resolved, change-point detection should provide an effective EMG-onset detection method suitable for automated real-time implementation.

EMG-based and gaze-tracking-based man-machine interfaces

A great demand for brain-machine and, more generally, man-machine interfaces is arising nowadays, pushed by several promising scientific and technological results, which are encouraging the concentration of efforts in this field. The possibility of measuring, processing and decoding brain activity, so as to interpret neural signals, is often looked at as a possibility to bypass lost or damaged neural and/or motor structures. Beyond that, such interfaces currently show a potential for applications in other fields, space science being certainly one of them. At present, the concept of "reading" the brain to detect intended actions and use these to control external devices is being studied with several technical and methodological approaches; among these, interfaces based on electroencephalographic signals play today a prominent role. Within such a context, the aim of this section is to present a brief survey on two types of noninvasive man-machine interfaces based on a different approach. In particular, they rely on the extraction of control signals from the user with techniques that adopt electromyography and gaze tracking. Working principles, implementations, typical features, and applications of these two types of interfaces are reported.

Detection technique of muscle activation intervals for sEMG signals based on the empirical mode decomposition

The best way to detect the onset and offset time of muscle activation is through visual decision making by clinical experts like physical therapists. Humans can recognize muscle activation trends recorded from surface EMG signals. Current computer-based algorithms are being researched toward yielding similar results by clinical experts. A new algorithm in this paper has the ability, like humans, to recognize a trend from noisy input signals. We propose using the Empirical Mode Decomposition (EMD), because it is effectual to recognize trends which are decomposed by Hilbert transform and synthesized of Intrinsic Mode Functions (IMFs). These synthesized functions represent hidden low-frequency trends according to more iterative processes. Iterations will be stopped at the minimum SD of a resting period of EMG signals. The proposed method is very useful and easy implemented, but there are some limitations. The EMD method is only available on an off-line data and requires relatively high computational performances to find the IMFs. To use the proposed method, it is possible to detect muscle activation intervals of sEMG signals.

A novel electromyographic signal simulator for muscle contraction studies

Mathematical simulation has been widely used in biomedical and biological sciences. In the case of the surface electromyographic (SEMG) activity, some models have been proposed aiming to study muscle contraction strategies that are used during different tasks and conditions. Most of SEMG simulators are based on energy modulation of a Gaussian noise. This work proposes a novel simulator in which the user-defined parameters are associated with the motor units (MUs) recruitment and their firing rate. Comparison between the mean spectrum of real SEMG signals collected in isometric contraction of the muscle biceps brachii and the mean spectrum obtained from simulated SEMG signals showed a good agreement, pointing the proposed simulator seems to be capable to generate consistent electromyographic signals in time and frequency domains and that can be used in many studies, in particular in the evaluation of automatic methods aimed to detect muscular contraction.

Digital Butterworth filter for subtracting noise from low magnitude surface electromyogram

This work presents a digital filter designed to delimitate the frequency band of surface electromyograms (EMG) and remove the mains noise and its harmonics, focusing the signal analysis during reduced muscle activity. A Butterworth filter was designed as the frequency-domain product of a second order, high-pass filter with cutoff frequency 10 Hz, an eighth order low-pass filter, with cutoff at 400 Hz and six stop-band filters, second order, centered at the 60 Hz mains noise and its harmonics until 360 Hz. The resulting filter was applied in both direct and reverse directions of the signals to avoid phase distortions. The performance was evaluated with a simulated EMG signal with additive noise in multiples of 60 Hz. A qualitative assessment was made with real EMG data, acquired from 16 subjects, with age from 20 to 32 years. Subjects were positioned in orthostatic position during 21s, being only the last second analyzed to assure stationarity. EMG were collected by Ag/AgCl electrodes on right lateral gastrocnemius, amplified with gain 5000, filtered in the band from 10 Hz to 1 kHz, and thus digitized with 2ksamples/s. The filter effectively removed the mains noise components, with attenuations greater than 96.6%. The attenuation of the simulated signal at frequencies below 15 Hz and at 60 Hz caused only a small reduction of total power, preserving the original spectrum. Thus, the filter resulted suitable to the proposed application.

EMG analysis of the thenar muscles as a model for EMG-triggered larynx stimulation

Paralysis of one or both sides of the larynx musculature compromises breathing and speech function. Currently there is no surgical remedy to restore adequate function of the larynx. A plausible alternative solution is triggered electrical stimulation of the paralysed larynx site using a laryngeal pacemaker. Triggering of the pacemaker succeeds via constant EMG measurement of the muscle activity of the healthy larynx side. The EMG data analysis described in this work is one possible approach for regulating pacemaker triggering. In this study we used EMG data from the thenar muscles as a model to calculate a trigger point.

Techniques of EMG signal analysis: detection, processing, classification and applications

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.

A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems

Considerable scientific and technological efforts have been devoted to develop neuroprostheses and hybrid bionic systems that link the human nervous system with electronic or robotic prostheses, with the main aim of restoring motor and sensory functions in disabled patients. A number of neuroprostheses use interfaces with peripheral nerves or muscles for neuromuscular stimulation and signal recording. Herein, we provide a critical overview of the peripheral interfaces available and trace their use from research to clinical application in controlling artificial and robotic prostheses. The first section reviews the different types of non-invasive and invasive electrodes, which include surface and muscular electrodes that can record EMG signals from and stimulate the underlying or implanted muscles. Extraneural electrodes, such as cuff and epineurial electrodes, provide simultaneous interface with many axons in the nerve, whereas intrafascicular, penetrating, and regenerative electrodes may contact small groups of axons within a nerve fascicle. Biological, technological, and material science issues are also reviewed relative to the problems of electrode design and tissue injury. The last section reviews different strategies for the use of information recorded from peripheral interfaces and the current state of control neuroprostheses and hybrid bionic systems.

A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control

This paper presents a heuristic fuzzy logic approach to multiple electromyogram (EMG) pattern recognition for multifunctional prosthesis control. Basic signal statistics (mean and standard deviation) are used for membership function construction, and fuzzy c-means (FCMs) data clustering is used to automate the construction of a simple amplitude-driven inference rule base. The result is a system that is transparent to, and easily "tweaked" by, the prosthetist/clinician. Other algorithms in current literature assume a longer period of unperceivable delay, while the system we present has an update rate of 45.7 ms with little postprocessing time, making it suitable for real-time application. Five subjects were investigated (three with intact limbs, one with a unilateral transradial amputation, and one with a unilateral transradial limb-deficiency from birth). Four subjects were used for system offline analysis, and the remaining intact-limbed subject was used for system real-time analysis. We discriminated between four EMG patterns for subjects with intact limbs, and between three patterns for limb-deficient subjects. Overall classification rates ranged from 94% to 99%. The fuzzy algorithm also demonstrated success in real-time classification, both during steady state motions and motion state transitioning. This functionality allows for seamless control of multiple degrees-of-freedom in a multifunctional prosthesis.

Optimum electrode configuration for detection of leg movement using bio-impedance

Impedances and joint angles were simultaneously measured during ankle and knee movements. The correlation coefficients of the joint angle and the impedance change from human leg movement were obtained using an electro-goniometer and a four-channel impedance measurement system. Because the impedance changes resulting from ankle and knee movements depended heavily on the electrode placement, we determined the optimum electrode configurations for those movements by searching for high correlation coefficients, large impedance changes and minimum interferences in ten subjects (age: 20+/-4). Our optimum electrode configurations showed strong relationships between the ankle joint angle and lower leg impedance (correlation coefficient=-0.91+/-0.06) and between the knee joint angle and knee impedance (correlation coefficient=0.94+/-0.04). The reproducibilities of the impedance changes of five subjects due to the ankle and knee were 6.3+/-1.9% and 5.1+/-1.7% for the optimum electrode pairs, respectively. We propose that this optimum electrode configuration would be useful for future studies involving the convenient measurement of leg movements by the impedance method.

Correlation-based decomposition of surface electromyograms at low contraction forces

The paper studies a surface electromyogram (SEMG) decomposition technique suitable for identification of complete motor unit (MU) firing patterns and their motor unit action potentials (MUAPs) during low-level isometric voluntary muscle contractions. The algorithm was based on a correlation matrix of measurements, assumed unsynchronised (uncorrelated) MU firings, exhibited a very low computational complexity and resolved the superimposition of MUAPs. A separation index was defined that identified the time instants of an MU's activation and was eventually used for reconstruction of a complete MU innervation pulse train. In contrast with other decomposition techniques, the proposed approach worked well also when the number of active MUs was slightly underestimated, if the MU firing patterns partly overlapped and if the measurements were noisy. The results on synthetic SEMG show 100% accuracy in the detection of innervation pulses down to a signal-to-noise ratio (SNR) of 10 dB, and 93+/-4.6% (mean+/-standard deviation) accuracy with 0 dB additive noise. In the case of real SEMG, recorded with an array of 61 electrodes from biceps brachii of five subjects at 10% maximum voluntary contraction, seven active MUs with a mean firing rate of 14.1 Hz were identified on average.