Precise onset detection of human motor responses using a whitening filter and the log-likelihood-ratio test

A Velocity Stretch Reflex Threshold Based on Muscle-Tendon Unit Peak Acceleration to Detect Possible Occurrences of Spasticity during Gait in Children with Cerebral Palsy

Spasticity might affect gait in children with cerebral palsy. Quantifying its occurrence during locomotion is challenging. One approach is to determine kinematic stretch reflex thresholds, usually on the velocity, during passive assessment and to search for their exceedance during gait. These thresholds are determined through EMG-Onset detection algorithms, which are variable in performance and sensitive to noisy data, and can therefore lack consistency. This study aimed to evaluate the feasibility of determining the velocity stretch reflex threshold from maximal musculotendon acceleration. Eighteen children with CP were recruited and underwent clinical gait analysis and a full instrumented assessment of their soleus, gastrocnemius lateralis, semitendinosus, and rectus femoris spasticity, with EMG, kinematics, and applied forces being measured simultaneously. Using a subject-scaled musculoskeletal model, the acceleration-based stretch reflex velocity thresholds were determined and compared to those based on EMG-Onset determination. Their consistencies according to physiological criteria, i.e., if the timing of the threshold was between the beginning of the stretch and the spastic catch, were evaluated. Finally, two parameters designed to evaluate the occurrence of spasticity during gait, i.e., the proportion of the gait trial time with a gait velocity above the velocity threshold and the number of times the threshold was exceeded, were compared. The proposed method produces velocity stretch reflex thresholds close to the EMG-based ones. For all muscles, no statistical difference was found between the two parameters designed to evaluate the occurrence of spasticity during gait. Contrarily to the EMG-based methods, the proposed method always provides physiologically consistent values, with median electromechanical delays of between 50 and 130 ms. For all subjects, the semitendinosus velocity during gait usually exceeded its stretch reflex threshold, while it was less frequent for the three other muscles. We conclude that a velocity stretch reflex threshold, based on musculotendon acceleration, is a reliable substitute for EMG-based ones.

Evaluation of force-time curve analysis methods in the isometric mid-thigh pull test

The purpose of this study was to evaluate the manual and automatic analysis methods for force-time curve analysis of the isometric mid-thigh pull (IMTP) test. The visual analysis, first derivative analysis and threshold analysis methods were used to analyse onset time and time-specific forces at 50 ms, 90 ms, 200 ms and 250 ms on the force-time curve. Ninety-three collegiate sports athletes' trials were selected and analysed by each method. The visual analysis method was set as the reference method for paired comparisons with the first derivative analysis method and threshold analysis method. Onset time comparisons revealed that the first derivative analysis method was comparable with the visual analysis method with average difference at about 30 ms. Results from the weighted least products regression analysis and the Bland-Altman analysis showed that large fixed bias confounded by proportional bias existed in the threshold analysis method, and time-specific force variables obtained from the first derivative analysis method were closer to those from the visual analysis method when compared with the threshold analysis method. These findings suggest that the first derivative analysis method could be an effective tool for force-time curve analysis of the IMTP test.

Advances in EMG measurement techniques, analysis procedures, and the impact of muscle mechanics on future requirements for the methodology

Muscular coordination enables locomotion and interaction with the environment. For more than 50 years electromyography (EMG) has provided insights into the central nervous system control of individual muscles or muscle groups, enabling both fine and gross motor functions. This information is available either at individual motor units (Mus) level or on a more global level from the coordination of different muscles or muscle groups. In particular, non-invasive EMG methods such as surface EMG (sEMG) or, more recently, spatial mapping methods (High-Density EMG - HDsEMG) have found their place in research into biomechanics, sport and exercise, ergonomics, rehabilitation, diagnostics, and increasingly for the control of technical devices. With further technical advances and a growing understanding of the relationship between EMG and movement task execution, it is expected that with time, especially non-invasive EMG methods will become increasingly important in movement sciences. However, while the total number of publications per year on non-invasive EMG methods is growing exponentially, the number of publications on this topic in journals with a scope in movement sciences has stagnated in the last decade. This review paper contextualizes non-invasive EMG development over the last 50 years, highlighting methodological progress. Changes in research topics related to non-invasive EMG were identified. Today non-invasive EMG procedures are increasingly used to control technical devices, where muscle mechanics have a minor influence. In movement science, however, the effect of muscle mechanics on the EMG signal cannot be neglected. This explains why non-invasive EMG's relevance in movement sciences has not developed as expected.

Shaping Human Movement via Bimanually-Dependent Haptic Force Feedback

Haptic feedback can enhance training and performance of human operators; however, the design of haptic feedback for bimanual coordination in robot-assisted tasks (e.g., control of surgical robots) remains an open problem. In this study, we present four bimanually-dependent haptic force feedback conditions aimed at shaping bimanual movement according to geometric characteristics: the number of targets, direction, and symmetry. Haptic conditions include a virtual spring, damper, combination spring-damper, and dual springs placed between the hands. We evaluate the effects of these haptic conditions on trajectory shape, smoothness, and speed. We hypothesized that for subjects who perform worse with no haptic feedback (1) a spring will improve the shape of parallel trajectories, (2) a damper will improve the shape of point symmetric trajectories, (3) dual springs will improve the shape of trajectories with one target, and (4) a damper will improve smoothness for all trajectories. Hypotheses (1) and (2) were statistically supported at the p < 0.001 level, but hypotheses (3) and (4) were not supported. Moreover, bimanually-dependent haptic feedback tended to improve shape accuracy for movements that subjects performed worse on under no haptic condition. Thus, bimanual haptic feedback based on geometric trajectory characteristics shows promise to improve performance in robot-assisted motor tasks.

Typical diet and type of economy do not predict food-sharing behaviors in three Tanzanian societies

Food sharing behavior is a widely observed phenomenon, and it draws attention of scholars interested in finding both proximate and ultimate explanations of such practices. In the current study, we focused on possible socio-economic and environmental food-sharing predictors: type of economy (i.e., immediate-return vs. delayed-return) and typical diet composition (i.e., proportion of proteins and carbohydrates in typical daily caloric intake). We investigated whether members of three societies from Tanzania (N = 177), namely hunter-gatherers (Hadza), pastoralists (Datoga), and agriculturalists (Iraqw) differ with regard to food-sharing patterns in the Dictator Game and reactions to violations of the food-sharing norms in the Ultimatum Game. We found that neither the type of economy nor the typical diet influenced our outcomes. The results indicated, however, that food sharing behavior was positively predicted by certain individual-level characteristics: people of higher strength and lower body fat shared more food, and women were more willing to share food than men.

The Effect of Verbal Encouragement on Performance and Muscle Fatigue in Swimming

Background and Objectives: Verbal encouragement (VE) can be used to enhance performance in several sports, even though no studies have been conducted among swimmers and only a few effects have been reported in elite athletes. Besides influencing motor performance, VE is also known to enhance the physical load, thus potentially increasing the probability of developing fatigue. With this in mind, this study aimed to explore the effects of VE in swimmers in order to fill in the knowledge gap concerning the aquatic environment. Materials and Methods: Each athlete swam a maximal 200 m freestyle trial under two different conditions: one trial with VE and the other without VE. The two main outcome measures were: (1) performance velocity (m/s); and (2) muscle fatigue, investigated by means of surface electromyography. Sixty swimmers were recruited, aged 18.63 ± 3.46 years (median 18 years), 28 men (47%), and 32 women (53%), with 7.03 ± 3.9 years of experience. Results: With VE, performance significantly improved in the swim trial (p < 0.001, effect size (ES) −0.95, large). When breaking the results down into the first half (first (0−100 m) vs. the second half (100−200 m)), the ES was large in the first part (−1.11), indicating an improvement in performance. This worsened, however, in the second part of the trial (ES 0.63). In the multivariate analysis, years of experience were found to be a significant predictor of the change in overall performance (p = 0.011). There was a significant increase in muscle fatigue induced by VE, overall, and during the second half, but not during the first half of the trial. Conclusions: The present study indicates that VE during a middle-distance event (200 m) increases performance most in swimmers with little experience. However, it has a negative impact on fatigue.

Machine Learning for Detection of Muscular Activity from Surface EMG Signals

Background: Muscular-activity timing is useful information that is extractable from surface EMG signals (sEMG). However, a reference method is not available yet. The aim of this study is to investigate the reliability of a novel machine-learning-based approach (DEMANN) in detecting the onset/offset timing of muscle activation from sEMG signals. Methods: A dataset of 2880 simulated sEMG signals, stratified for signal-to-noise ratio (SNR) and time support, was generated to train a hidden single-layer fully-connected neural network. DEMANN’s performance was evaluated on simulated sEMG signals and two different datasets of real sEMG signals. DEMANN was validated against different reference algorithms, including the acknowledged double-threshold statistical algorithm (DT). Results: DEMANN provided a reliable prediction of muscle onset/offset in simulated and real sEMG signals, being minimally affected by SNR variability. When directly compared with state-of-the-art algorithms, DEMANN introduced relevant improvements in prediction performances. Conclusions: These outcomes support DEMANN’s reliability in assessing onset/offset events in different motor tasks and the condition of signal quality (different SNR), improving reference-algorithm performances. Unlike other works, DEMANN’s adopts a machine learning approach where a neural network is trained by only simulated sEMG signals, avoiding the possible complications and costs associated with a typical experimental procedure, making this approach suitable to clinical practice.

Long short-term memory (LSTM) recurrent neural network for muscle activity detection

BACKGROUND

The accurate temporal analysis of muscle activation is of great interest in many research areas, spanning from neurorobotic systems to the assessment of altered locomotion patterns in orthopedic and neurological patients and the monitoring of their motor rehabilitation. The performance of the existing muscle activity detectors is strongly affected by both the SNR of the surface electromyography (sEMG) signals and the set of features used to detect the activation intervals. This work aims at introducing and validating a powerful approach to detect muscle activation intervals from sEMG signals, based on long short-term memory (LSTM) recurrent neural networks.

METHODS

First, the applicability of the proposed LSTM-based muscle activity detector (LSTM-MAD) is studied through simulated sEMG signals, comparing the LSTM-MAD performance against other two widely used approaches, i.e., the standard approach based on Teager-Kaiser Energy Operator (TKEO) and the traditional approach, used in clinical gait analysis, based on a double-threshold statistical detector (Stat). Second, the effect of the Signal-to-Noise Ratio (SNR) on the performance of the LSTM-MAD is assessed considering simulated signals with nine different SNR values. Finally, the newly introduced approach is validated on real sEMG signals, acquired during both physiological and pathological gait. Electromyography recordings from a total of 20 subjects (8 healthy individuals, 6 orthopedic patients, and 6 neurological patients) were included in the analysis.

RESULTS

The proposed algorithm overcomes the main limitations of the other tested approaches and it works directly on sEMG signals, without the need for background-noise and SNR estimation (as in Stat). Results demonstrate that LSTM-MAD outperforms the other approaches, revealing higher values of F1-score (F1-score > 0.91) and Jaccard similarity index (Jaccard > 0.85), and lower values of onset/offset bias (average absolute bias < 6 ms), both on simulated and real sEMG signals. Moreover, the advantages of using the LSTM-MAD algorithm are particularly evident for signals featuring a low to medium SNR.

CONCLUSIONS

The presented approach LSTM-MAD revealed excellent performances against TKEO and Stat. The validation carried out both on simulated and real signals, considering normal as well as pathological motor function during locomotion, demonstrated that it can be considered a powerful tool in the accurate and effective recognition/distinction of muscle activity from background noise in sEMG signals.

Onset detection in surface electromyographic signals across isometric explosive and ramped contractions: a comparison of computer-based methods

Objective. Accurate identification of surface electromyography (EMG) muscle onset is vital when examining short temporal parameters such as electromechanical delay. The visual method is considered the 'gold standard' in onset detection. Automatic detection methods are commonly employed to increase objectivity and reduce analysis time, but it is unclear if they are sensitive enough to accurately detect EMG onset when relating them to short-duration motor events.Approach. This study aimed to determine: (1) if automatic detection methods could be used interchangeably with visual methods in detecting EMG onsets (2) if the Teager-Kaiser energy operator (TKEO) as a conditioning step would improve the accuracy of popular EMG onset detection methods. The accuracy of three automatic onset detection methods: approximated generalized likelihood ratio (AGLR), TKEO, and threshold-based method were examined against the visual method. EMG signals from fast, explosive, and slow, ramped isometric plantarflexor contractions were evaluated using each technique.Main results. For fast, explosive contractions, the TKEO was the best-performing automatic detection method, with a low bias level (4.7 ± 5.6 ms) and excellent intraclass correlation coefficient (ICC) of 0.993, however with wide limits of agreement (LoA) (-6.2 to +15.7 ms). For slow, ramped contractions, the AGLR with TKEO conditioning was the best-performing automatic detection method with the smallest bias (11.3 ± 32.9 ms) and excellent ICC (0.983) but produced wide LoA (-53.2 to +75.8 ms). For visual detection, the inclusion of TKEO conditioning improved inter-rater and intra-rater reliability across contraction types compared with visual detection without TKEO conditioning.Significance. In conclusion, the examined automatic detection methods are not sensitive enough to be applied when relating EMG onset to a motor event of short duration. To attain the accuracy needed, visual detection is recommended. The inclusion of TKEO as a conditioning step before visual detection of EMG onsets is recommended to improve visual detection reliability.

Neck Muscle and Head/Neck Kinematic Responses While Bracing Against the Steering Wheel During Front and Rear Impacts

Drivers often react to an impending collision by bracing against the steering wheel. The goal of the present study was to quantify the effect of bracing on neck muscle activity and head/torso kinematics during low-speed front and rear impacts. Eleven seated subjects (3F, 8 M) experienced multiple sled impacts (Δv = 0.77 m/s; a_peak = 19.9 m/s², Δt = 65.5 ms) with their hands on the steering wheel in two conditions: relaxed and braced against the steering wheel. Electromyographic activity in eight neck muscles (sternohyoid, sternocleidomastoid, splenius capitis, semispinalis capitis, semispinalis cervicis, multifidus, levator scapulae, and trapezius) was recorded unilaterally with indwelling electrodes and normalized by maximum voluntary contraction (MVC) levels. Head and torso kinematics (linear acceleration, angular velocity, angular rotation, and retraction) were measured with sensors and motion tracking. Muscle and kinematic variables were compared between the relaxed and braced conditions using linear mixed models. We found that pre-impact bracing generated only small increases in the pre-impact muscle activity (< 5% MVC) when compared to the relaxed condition. Pre-impact bracing did not increase peak neck muscle responses during the impacts; instead it reduced peak trapezius and multifidus muscle activity by about half during front impacts. Bracing led to widespread changes in the peak amplitude and timing of the torso and head kinematics that were not consistent with a simple stiffening of the head/neck/torso system. Instead pre-impact bracing served to couple the torso more rigidly to the seat while not necessarily coupling the head more rigidly to the torso.

A global view on how local muscular fatigue affects human performance

There is a growing interest in scientific literature on identifying how and to what extent interventions applied to a specific body region influence the responses and functions of other seemingly unrelated body regions. To investigate such a construct, it is necessary to have a global multivariate model that considers the interaction among several variables that are involved in a specific task and how a local and acute impairment affects the behavior of the output of such a model. We developed an artificial neural network (ANN)-based multivariate model by using parameters of motor skills obtained from kinematic, postural control, joint torque, and proprioception variables to assess the local fatigue effects of the abductor hip muscles on the functional profile during a single-leg drop landing and a squatting task. Findings suggest that hip abductor muscles' local fatigue produces a significant effect on a general functional profile, built on different control systems. We propose that expanded and global approaches, such as the one used in this study, have great applicability and have the potential to serve as a tool that guarantees ecological validity of future investigations.

Neural muscle activation detection: A deep learning approach using surface electromyography

The timing of muscles activation which is a key parameter in determining plenty of medical conditions can be greatly assessed by the surface EMG signal which inherently carries an immense amount of information. Many techniques for measuring muscle activity detection exist in the literature. However, due to the complex nature of the EMG signal as well as the interference from other muscles that is observed during the measurement of the EMG signal, the accuracy of these techniques is compromised. In this paper, we introduce the neural muscle activation detection (NMAD) framework that detects the muscle activation based on deep learning. The main motivation behind using deep learning is to allow the neural network to detect based on the appropriate signal features instead of depending on certain assumptions. Not only the presented approach significantly improves the accuracy of timing detection, but because of the training nature, it can adapt to operate under different levels of interference and signal-to-noise ratio.

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.

Dopamine and response selection: an Acute Phenylalanine/Tyrosine Depletion study

The role of dopaminergic system in decision-making is well documented, and evidence suggests that it could play a significant role in response selection processes. The N-40 is a fronto-central event-related potential, generated by the supplementary motor areas (SMAs) and a physiological index of response selection processes. The aim of the present study was to determine whether infraclinical effects of dopamine depletion on response selection processes could be evidenced via alterations of the N-40. We obtained a dopamine depletion in healthy volunteers with the acute phenylalanine and tyrosine depletion (APTD) method which consists in decreasing the availability of dopamine precursors. Subjects realized a Simon task in the APTD condition and in the control condition. When the stimulus was presented on the same side as the required response, the stimulus-response association was congruent and when the stimulus was presented on the opposite side of the required response, the stimulus-response association was incongruent. The N-40 was smaller for congruent associations than for incongruent associations. Moreover, the N-40 was sensitive to the level of dopaminergic activity with a decrease in APTD condition compared to control condition. This modulation of the N-40 by dopaminergic level could not be explained by a global decrease of cerebral electrogenesis, since negativities and positivities indexing the recruitment of the primary motor cortex (anatomically adjacent to the SMA) were unaffected by APTD. The specific sensitivity of N-40 to ATPD supports the model of Keeler et al. (Neuroscience 282:156-175, 2014) according to which the dopaminergic system is involved in response selection.

Simultaneous scalp recorded EEG and local field potentials from monkey ventral premotor cortex during action observation and execution reveals the contribution of mirror and motor neurons to the mu-rhythm

The desynchronization of alpha and beta oscillations (mu rhythm) in the central scalp EEG during action observation and action execution is thought to reflect neural mirroring processes. However, the extent to which mirror neurons (MNs) or other populations of neurons contribute to such EEG desynchronization is still unknown. Here, we provide the first evidence that, in the monkey, the neuronal activity recorded from the ventral premotor cortex (PMv) strongly contributes to the EEG changes occurring in the beta band over central scalp electrodes, during executed and observed actions. We simultaneously recorded scalp EEG and extracellular activity, Multi Unit Activity (MUA) and Local Field Potentials (LFP), from area F5 of two macaques executing and observing grasping actions. We found that MUA highly correlates with an increase in high gamma LFP power and, interestingly, such LFP power increase also correlates to EEG beta - and in part also to alpha - desynchronization. In terms of timing of signal changes, the increase in high gamma LFP power precedes the EEG desynchronization, during both action observation and execution, thus suggesting a causal role of PMv neuronal activity in the modulation of the alpha and beta mu-rhythm. Lastly, neuronal signals from deeper layers of PMv exert a greater contribution than superficial layers to the EEG beta rhythm modulation, especially during the motor task. Our findings have clear implications for EEG studies in that they demonstrate that the activity of different populations of neurons in PMv contribute to the generation of the mu-rhythm.

Evidence of splinting in low back pain? A systematic review of perturbation studies

PURPOSE

The purpose of this systematic review was to assess whether LBP patients demonstrate signs of splinting by evaluating the reactions to unexpected mechanical perturbations in terms of (1) trunk muscle activity, (2) kinetic and (3) kinematic trunk responses and (4) estimated mechanical properties of the trunk.

METHODS

The literature was systematically reviewed to identify studies that compared responses to mechanical trunk perturbations between LBP patients and healthy controls in terms of muscle activation, kinematics, kinetics, and/or mechanical properties. If more than four studies reported an outcome, the results of these studies were pooled.

RESULTS

Nineteen studies were included, of which sixteen reported muscle activation, five kinematic responses, two kinetic responses, and two estimated mechanical trunk properties. We found evidence of a longer response time of muscle activation, which would be in line with splinting behaviour in LBP. No signs of splinting behaviour were found in any of the other outcome measures.

CONCLUSIONS

We conclude that there is currently no convincing evidence for the presence of splinting behaviour in LBP patients, because we found no indications for splinting in terms of kinetic and kinematic responses to perturbation and derived mechanical properties of the trunk. Consistent evidence on delayed onsets of muscle activation in response to perturbations was found, but this may have other causes than splinting behaviour.

The Identification and Tracking of Uterine Contractions Using Template Based Cross-Correlation

The purpose of this paper is to outline a novel method of using template based cross-correlation to identify and track uterine contractions during labour. A purpose built six-channel Electromyography (EMG) device was used to collect data from consenting women during labour and birth. A range of templates were constructed for the purpose of identifying and tracking uterine activity when cross-correlated with the EMG signal. Peak finding techniques were applied on the cross-correlated result to simplify and automate the identification and tracking of contractions. The EMG data showed a unique pattern when a woman was contracting with key features of the contraction signal remaining consistent and identifiable across subjects. Contraction profiles across subjects were automatically identified using template based cross-correlation. Synthetic templates from a rectangular function with a duration of between 5 and 10 s performed best at identifying and tracking uterine activity across subjects. The successful application of this technique provides opportunity for both simple and accurate real-time analysis of contraction data while enabling investigations into the application of techniques such as machine learning which could enable automated learning from contraction data as part of real-time monitoring and post analysis.

Trunk Postural Muscle Timing Is Not Compromised In Low Back Pain Patients Clinically Diagnosed With Movement Coordination Impairments

Trunk muscle timing impairment has been associated with nonspecific low back pain (NSLBP), but this finding has not been consistent. This study investigated trunk muscle timing in a subgroup of patients with NSLBP attributed to movement coordination impairment (MCI) and matched asymptomatic controls in response to a rapid arm-raising task. Twenty-one NSLBP subjects and 21 matched controls had arm motion and surface EMG data collected from seven bilateral trunk muscles. Muscle onset and offset relative to deltoid muscle activation and arm motion, duration of muscle burst and abdominal-extensor co-contraction time were derived. Trunk muscle onset and offset latencies, and burst and co-contraction durations were not different (p > .05) between groups. Patterns of trunk muscle activation and deactivation relative to arm motion were not different. Task performance was similar between groups. Trunk muscle timing does not appear to be an underlying impairment in the subgroup of NSLBP with MCI.

The concurrent validity of three computerized methods of muscle activity onset detection

Although the visual (VIS) method for muscle activation onset detection has been the gold standard, this method has been criticized because of its moderate reproducibility and for being laborious. The simple threshold (STH), approximated generalized likelihood-step (AGL-step), and k-means (KM) algorithms are more repeatable and less laborious but require validation for gait speeds encountered in clinical research. We, therefore, assessed the intra-rater reliability of the VIS method and the concurrent validity of the algorithms against the VIS for 3 gait speeds. We recruited 10 healthy young adults (4 male, 6 female; mean age=28.5±4.2). Participants completed 10 walking trials each at 3 speeds. Electromyographic data from 1 gait cycle (GC) were collected from 6 right lower extremity muscles during each trial. We used custom Labview programs to determine muscle activity onset for all 4 methods. Repeatability coefficients for the VIS method ranged from 12.51% to 45.08% of the GC, depending on the muscle. The AGL-step algorithm agreed best with the VIS method (root mean squared error (RMSE) 0.86-6.95% of GC) followed by the STH (1.19-15.6% of GC) and KM (4.6-16.9% of GC) methods. A single rater demonstrated large errors (RMSE 8-23% of GC) between VIS assessments. Based on this study's parameters, the AGL-step agreed best with the VIS method and may be an alternative to the VIS.

Robust muscle activity onset detection using an unsupervised electromyogram learning framework

Accurate muscle activity onset detection is an essential prerequisite for many applications of surface electromyogram (EMG). This study presents an unsupervised EMG learning framework based on a sequential Gaussian mixture model (GMM) to detect muscle activity onsets. The distribution of the logarithmic power of EMG signal was characterized by a two-component GMM in each frequency band, in which the two components respectively correspond to the posterior distribution of EMG burst and non-burst logarithmic powers. The parameter set of the GMM was sequentially estimated based on maximum likelihood, subject to constraints derived from the relationship between EMG burst and non-burst distributions. An optimal threshold for EMG burst/non-burst classification was determined using the GMM at each frequency band, and the final decision was obtained by a voting procedure. The proposed novel framework was applied to simulated and experimental surface EMG signals for muscle activity onset detection. Compared with conventional approaches, it demonstrated robust performance for low and changing signal to noise ratios in a dynamic environment. The framework is applicable for real-time implementation, and does not require the assumption of non EMG burst in the initial stage. Such features facilitate its practical application.

Dopa therapy and action impulsivity: subthreshold error activation and suppression in Parkinson's disease

RATIONALE

Impulsive actions entail (1) capture of the motor system by an action impulse, which is an urge to act and (2) failed suppression of that impulse in order to prevent a response error. Several studies indicate that dopaminergic treatment can induce action impulsivity in patients diagnosed with Parkinson's disease (PD). Whether this effect is due to increased impulse expression or to decreased impulse suppression remains to be deciphered.

METHOD

We used a novel approach based on electromyographic (EMG) analyses to decipher the effects of the patient's usual dopaminergic therapy on the expression and suppression of subliminal erroneous impulses. To this end, we used a within-subject design and took advantage of the Simon task, that elicits prepotent response tendencies. The patients (N = 15) performed the task on their usual dopaminergic medication and after complete medication withdrawal (for at least 12 h).

RESULTS

The correction rate that measures the ability to suppress subthreshold impulsive muscle activity was lower when the patients were on medication as compared to their off medication state (p < 0.05). The incorrect activation rate that measures the capture of the motor system by action impulses was unaffected by medication.

CONCLUSIONS

Dopa therapy affected action impulsivity. Although medication did not influence the incidence of fast action impulses, it significantly reduced patients' ability to abort and suppress muscle activation related to the incorrect response alternative.

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.

Dopamine precursors depletion impairs impulse control in healthy volunteers

The aim of the present study was to decipher the role of the dopamine system in impulse control. Impulsive actions entail (i) activation of the motor system by an impulse, which is an urge to act and (ii) a failure to suppress that impulse, when inappropriate, in order to prevent an error. These two aspects of action impulsivity can be experimentally disentangled in conflict reaction time tasks such as the Simon task, which measures susceptibility to acting on spontaneous impulses (as well as the proficiency of suppressing these impulses). In 12 healthy volunteers performing a Simon task, dopamine availability was reduced with an amino acid drink deficient in the dopamine precursors, phenylalanine and tyrosine. Classic behavioral measures were augmented with an analysis of the electromyographic activity of the response effectors. Electromyography allows one to detect covert activations undetectable with strictly behavioral measures and further reveals the participants' ability to quickly suppress covert activations before they result in an overt movement. Following dopamine depletion, compared with a placebo condition, participants displayed comparable impulse activation but were less proficient at suppressing the interference from this activation. These results provide evidence that the dopamine system is directly involved in the suppression of maladaptive response impulses.

Muscle-tendon unit stiffness does not independently affect voluntary explosive force production or muscle intrinsic contractile properties

This study examined the relationship of muscle-tendon unit (MTU) stiffness and explosive force production during voluntary and evoked contractions of the knee extensors. Thirty-four untrained participants performed a series of explosive voluntary and electrically evoked (octets (8 pulses, 300 Hz) via femoral nerve stimulation) isometric contractions. Maximum voluntary force (MVF) was assessed during maximum voluntary contractions. Explosive force production was assessed as the time taken, from force onset (0 N), to achieve specific levels of absolute (25–300 N) and relative force (5%–75% MVF) during the explosive contractions. Ultrasonic images of the vastus lateralis were recorded during 10-s ramp contractions to assess MTU stiffness, which was expressed in absolute (N·mm−1) and relative (to MVF and resting tendon-aponeurosis length) terms. Bivariate correlations suggested that absolute MTU stiffness was associated with voluntary explosive force (time to achieve 150–300 N: r = –0.35 to –0.54, P < 0.05). However, no relationships between stiffness and voluntary explosive force were observed when the influence of MVF was removed, either via partial correlations of absolute values (P ≥ 0.49) or considering relative values (P ≥ 0.14). Similarly, absolute MTU stiffness was related to explosive force during evoked octet contractions (r = –0.41 to –0.64, P < 0.05), but these correlations were no longer present when accounting for the influence of MVF (P ≥ 0.15). Therefore, once maximum strength was considered, MTU stiffness had no independent relationship with voluntary explosive force production or the evoked capacity for explosive force.

Wiener filtering of surface EMG with a priori SNR estimation toward myoelectric control for neurological injury patients

Voluntary surface electromyogram (EMG) signals from neurological injury patients are often corrupted by involuntary background interference or spikes, imposing difficulties for myoelectric control. We present a novel framework to suppress involuntary background spikes during voluntary surface EMG recordings. The framework applies a Wiener filter to restore voluntary surface EMG signals based on tracking a priori signal to noise ratio (SNR) by using the decision-directed method. Semi-synthetic surface EMG signals contaminated by different levels of involuntary background spikes were constructed from a database of surface EMG recordings in a group of spinal cord injury subjects. After the processing, the onset detection of voluntary muscle activity was significantly improved against involuntary background spikes. The magnitude of voluntary surface EMG signals can also be reliably estimated for myoelectric control purpose. Compared with the previous sample entropy analysis for suppressing involuntary background spikes, the proposed framework is characterized by quick and simple implementation, making it more suitable for application in a myoelectric control system toward neurological injury rehabilitation.

A novel technique for muscle onset detection using surface EMG signals without removal of ECG artifacts

Surface electromyography (EMG) signal from trunk muscles is often contaminated by electrocardiography (ECG) artifacts. This study presents a novel method for muscle activity onset detection by processing surface EMG against ECG artifacts. The method does not require removal of ECG artifacts from raw surface EMG signals. Instead, it applies the sample entropy (SampEn) analysis to highlight EMG activity and suppress ECG artifacts in the signal complexity domain. A SampEn threshold can then be determined for detection of muscle activity. The performance of the proposed method was examined with different SampEn analysis window lengths, using a series of combinations of 'clean' experimental EMG and ECG recordings over a wide range of signal to noise ratios (SNRs) from -10 to 10 dB. For all the examined SNRs, the window length of 128 ms yielded the best performance among all the tested lengths. Compared with the conventional amplitude thresholding and integrated profile methods, the SampEn analysis based method achieved significantly better performance, demonstrated as the shortest average latency or error among the three methods (p < 0.001 for any of the examined SNRs except 10 dB).

Control strategies to re-establish glenohumeral stability after shoulder injury

BACKGROUND

Muscles are important "sensors of the joint instability". The aim of this study was to identify the neuro-motor control strategies adopted by patients with anterior shoulder instability during overhead shoulder elevation in two planes.

METHODS

The onset, time of peak activation, and peak magnitude of seven shoulder muscles (posterior deltoid, bilateral upper trapezius, biceps brachii, infraspinatus, supraspinatus and teres major) were identified using electromyography as 19 pre-operative patients with anterior shoulder instability (mean 27.95 years, SD = 7.796) and 25 age-matched asymptomatic control subjects (mean 23.07 years, SD = 2.952) elevated their arm above 90 degrees in the sagittal and coronal planes.

RESULTS

Temporal characteristics of time of muscle onsets were significantly different between groups expect for teres major in the coronal plane (t = 1.1220, p = 0.2646) Patients recruited the rotator cuff muscles earlier and delayed the onset of ipsilateral upper trapezius compared with subjects (p<0.001) that control subjects. Furthermore, significant alliances existed between the onsets of infraspinatus and supraspinatus (sagittal: r = 0.720; coronal: r = 0.756 at p<0.001) and ipsilateral upper trapezius and infraspinatus (sagittal: r = -0.760, coronal: r = -0.818 at p<0.001). The peak activation of all seven muscles occurred in the mid-range of elevation among patients with anterior shoulder instability whereas subjects spread peak activation of all 7 muscles throughout range. Peak magnitude of patients' infraspinatus muscle was six times higher (sagittal: t = -8.6428, coronal: t = -54.1578 at p<0.001) but magnitude of their supraspinatus was lower (sagittal: t = 36.2507, coronal: t = 35.9350 at p<0.001) that subjects.

CONCLUSIONS

Patients with anterior shoulder instability adopted a "stability before mobility" neuro-motor control strategy to initiate elevation and a "stability at all cost" strategy to ensure concavity compression in the mid-to-150 degrees of elevation in both sagittal and coronal planes.

Human capacity for explosive force production: neural and contractile determinants

This study assessed the integrative neural and contractile determinants of human knee extension explosive force production. Forty untrained participants performed voluntary and involuntary (supramaximally evoked twitches and octets - eight pulses at 300 Hz that elicit the maximum possible rate of force development) explosive isometric contractions of the knee extensors. Explosive force (F0-150 ms) and sequential rate of force development (RFD, 50-ms epochs) were measured. Surface electromyography (EMG) amplitude was recorded (superficial quadriceps and hamstrings, 50-ms epochs) and normalized (quadriceps to Mmax, hamstrings to EMGmax). Maximum voluntary force (MVF) was also assessed. Multiple linear regressions assessed the significant neural and contractile determinants of absolute and relative (%MVF) explosive force and sequential RFD. Explosive force production exhibited substantial interindividual variability, particularly during the early phase of contraction [F50, 13-fold (absolute); 7.5-fold (relative)]. Multiple regression explained 59-93% (absolute) and 35-60% (relative) of the variance in explosive force production. The primary determinants of explosive force changed during the contraction (F0-50, quadriceps EMG and Twitch F; RFD50-100, Octet RFD0-50; F100-150, MVF). In conclusion, explosive force production was largely explained by predictor neural and contractile variables, but the specific determinants changed during the phase of contraction.

A simple control policy for achieving minimum jerk trajectories

Point-to-point fast hand movements, often referred to as ballistic movements, are a class of movements characterized by straight paths and bell-shaped velocity profiles. In this paper we propose a bang-bang optimal control policy that can achieve such movements. This optimal control policy is accomplished by minimizing the L∞ norm of the jerk profile of ballistic movements with known initial position, final position, and duration of movement. We compare the results of this control policy with human motion data recorded with a manipulandum. We propose that such bang-bang control policies are inherently simple for the central nervous system to implement and also minimize wear and tear on the bio-mechanical system. Physiological experiments support the possibility that some parts of the central nervous system use bang-bang control policies. Furthermore, while many computational neural models of movement control have used a bang-bang control policy without justification, our study shows that the use of such policies is not only convenient, but optimal.

A SNR-independent formulation of a double threshold algorithm for the estimation of muscle activation intervals

The aim of this work is to propose an improvement to the double threshold algorithm for muscular activation intervals estimation developed by Bonato and his co-workers. The proposed method has been designed in order to be adaptive also when the Signal to Noise ratio (SNR) of the sEMG signal changes during the trial, by re-evaluating the parameters of the algorithm according to the estimated local SNR and the desired detection and false alarm probabilities. This novel implementation is also suitable for working in pseudo real-time since it can give information on burst estimation shortly after the end of the current muscular activity. The proposed method was tested on simulated signals taking into account changes in the SNR during the trial, and results were compared with those obtained with the classical implementation of the algorithm.

Teager-Kaiser energy operator signal conditioning improves EMG onset detection

Accurate identification of the onset of muscle activity is an important element in the biomechanical analysis of human movement. The purpose of this study was to determine if inclusion of the Teager–Kaiser energy operator (TKEO) in signal conditioning would increase the accuracy of popular electromyography (EMG) onset detection methods. Three methods, visual determination, threshold-based method, and approximated generalized likelihood ratio were used to estimate the onset of EMG burst with and without TKEO conditioning. Reference signals, with known onset times, were constructed from EMG signals collected during isometric contraction of the vastus lateralis (n = 17). Additionally, vastus lateralis EMG signals (n = 255) recorded during gait were used to evaluate a clinical application of the TKEO conditioning. Inclusion of TKEO in signal conditioning significantly reduced mean detection error of all three methods compared with signal conditioning without TKEO, using artificially generated reference data (13 vs. 98 ms, p < 0.001) and also compared with experimental data collected during gait (55 vs. 124 ms, p < 0.001). In conclusion, addition of TKEO as a step in conditioning surface EMG signals increases the detection accuracy of EMG burst boundaries.

Human movement onset detection from isometric force and torque measurements: a supervised pattern recognition approach

OBJECTIVE

Recent research has successfully introduced the application of robotics and mechatronics to functional assessment and motor therapy. Measurements of movement initiation in isometric conditions are widely used in clinical rehabilitation and their importance in functional assessment has been demonstrated for specific parts of the human body. The determination of the voluntary movement initiation time, also referred to as onset time, represents a challenging issue since the time window characterizing the movement onset is of particular relevance for the understanding of recovery mechanisms after a neurological damage. Establishing it manually as well as a troublesome task may also introduce oversight errors and loss of information.

METHODS

The most commonly used methods for automatic onset time detection compare the raw signal, or some extracted measures such as its derivatives (i.e., velocity and acceleration) with a chosen threshold. However, they suffer from high variability and systematic errors because of the weakness of the signal, the abnormality of response profiles as well as the variability of movement initiation times among patients. In this paper, we introduce a technique to optimise onset detection according to each input signal. It is based on a classification system that enables us to establish which deterministic method provides the most accurate onset time on the basis of information directly derived from the raw signal.

RESULTS

The approach was tested on annotated force and torque datasets. Each dataset is constituted by 768 signals acquired from eight anatomical districts in 96 patients who carried out six tasks related to common daily activities. The results show that the proposed technique improves not only on the performance achieved by each of the deterministic methods, but also on that attained by a group of clinical experts.

CONCLUSIONS

The paper describes a classification system detecting the voluntary movement initiation time and adaptable to different signals. By using a set of features directly derived from raw data, we obtained promising results. Furthermore, although the technique has been developed within the scope of isometric force and torque signal analysis, it can be applied to other detection problems where several simple detectors are available.