Recursive decomposition of electromyographic signals with a varying number of active sources: Bayesian modelling and filtering

International audienceThis paper describes a sequential decomposition algorithm for single channel intramuscular electromyography (iEMG) generated by a varying number of active motor neurons. As in previous work, we establish a Hidden Markov Model of iEMG, in which each motor neuron spike train is modeled as a renewal process with inter-spike intervals following a discrete Weibull law and motor unit action potentials are modeled as impulse responses of linear time-invariant systems with known prior. We then expand this model by introducing an activation vector associated to the state vector of the Hidden Markov Model. This activation vector represents recruitment/derecruitment of motor units and is estimated together with the state vector using Bayesian filtering. Non-stationarity of the model parameters is addressed by means of a sliding window approach, thus making the algorithm adaptive to variations in contraction force and motor unit action potential waveforms. The algorithm was validated using simulated and experimental iEMG signals with varying number of active motor units. The experimental signals were acquired from the tibialis anterior and abductor digiti minimi muscles by fine wire and needle electrodes. The decomposition accuracy in both simulated and experimental signals exceeded 90% and the recruitment/derecruitment was successfully tracked by the algorithm. Because of its parallel structure, this algorithm can be efficiently accelerated, which lays the basis for its future real-time applications in human-machine interfaces, e.g. for prosthetic control

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

A Study of Myoelectric Signal Processing

This dissertation of various aspects of electromyogram (EMG: muscle electrical activity) signal processing is comprised of two projects in which I was the lead investigator and two team projects in which I participated. The first investigator-led project was a study of reconstructing continuous EMG discharge rates from neural impulses. Related methods for calculating neural firing rates in other contexts were adapted and applied to the intramuscular motor unit action potential train firing rate. Statistical results based on simulation and clinical data suggest that performances of spline-based methods are superior to conventional filter-based methods in the absence of decomposition error, but they unacceptably degrade in the presence of even the smallest decomposition errors present in real EMG data, which is typically around 3-5%. Optimal parameters for each method are found, and with normal decomposition error rates, ranks of these methods with their optimal parameters are given. Overall, Hanning filtering and Berger methods exhibit consistent and significant advantages over other methods. In the second investigator-led project, the technique of signal whitening was applied prior to motion classification of upper limb surface EMG signals previously collected from the forearm muscles of intact and amputee subjects. The motions classified consisted of 11 hand and wrist actions pertaining to prosthesis control. Theoretical models and experimental data showed that whitening increased EMG signal bandwidth by 65-75% and the coefficients of variation of temporal features computed from the EMG were reduced. As a result, a consistent classification accuracy improvement of 3-5% was observed for all subjects at small analysis durations (\u3c 100 ms). In the first team-based project, advanced modeling methods of the constant posture EMG-torque relationship about the elbow were studied: whitened and multi-channel EMG signals, training set duration, regularized model parameter estimation and nonlinear models. Combined, these methods reduced error to less than a quarter of standard techniques. In the second team-based project, a study related biceps-triceps surface EMG to elbow torque at seven joint angles during constant-posture contractions. Models accounting for co-contraction estimated that individual flexion muscle torques were much higher than models that did not account for co-contraction

On-line recursive decomposition of intramuscular EMG signals using GPU-implemented bayesian filtering

Objective: Real-time intramuscular electromyography (iEMG) decomposition, which is needed in biofeedback studies and interfacing applications, is a complex procedure that involves identifying the motor neuron spike trains from a streaming iEMG recording. Methods: We have previously proposed a sequential decomposition algorithm based on a Hidden Markov Model of EMG, which used Bayesian filter to estimate unknown parameters of motor unit (MU) spike trains, as well as their action potentials (MUAPs). Here, we present a modification of this original model in order to achieve a real-time performance of the algorithm as well as a parallel computation implementation of the algorithm on Graphics Processing Unit (GPU). Specifically, the Kalman filter previously used to estimate the MUAPs, is replaced by a least-mean-square filter. Additionally, we introduce a number of heuristics that help to omit the most improbable decomposition scenarios while searching for the best solution. Then, a GPU-implementation of the proposed algorithm is presented. Results: Simulated iEMG signals containing up to 10 active MUs, as well as five experimental fine-wire iEMG signals acquired from the tibialis anterior muscle, were decomposed in real time. The accuracy of decompositions depended on the level of muscle activation, but in all cases exceeded 85%. Conclusion: The proposed method and implementation provide an accurate, real-time interface with spinal motor neurons. Significance: The presented real time implementation of the decomposition algorithm substantially broadens the domain of its application

Neuromechanical Modelling of Articulatory Movements from Surface Electromyography and Speech Formants

Speech articulation is produced by the movements of muscles in the larynx, pharynx, mouth and face. Therefore speech shows acoustic features as formants which are directly related with neuromotor actions of these muscles. The first two formants are strongly related with jaw and tongue muscular activity. Speech can be used as a simple and ubiquitous signal, easy to record and process, either locally or on e-Health platforms. This fact may open a wide set of applications in the study of functional grading and monitoring neurodegenerative diseases. A relevant question, in this sense, is how far speech correlates and neuromotor actions are related. This preliminary study is intended to find answers to this question by using surface electromyographic recordings on the masseter and the acoustic kinematics related with the first formant. It is shown in the study that relevant correlations can be found among the surface electromyographic activity (dynamic muscle behavior) and the positions and first derivatives of the first formant (kinematic variables related to vertical velocity and acceleration of the joint jaw and tongue biomechanical system). As an application example, it is shown that the probability density function associated to these kinematic variables is more sensitive than classical features as Vowel Space Area (VSA) or Formant Centralization Ratio (FCR) in characterizing neuromotor degeneration in Parkinson's Disease.This work is being funded by Grants TEC2016-77791-C4-4-R from the Ministry of Economic Affairs and Competitiveness of Spain, Teka-Park 55 02 CENIE-0348_CIE_6_E POCTEP (InterReg Programme) and 16-30805A, SIX Research Center (CZ.1.05/2.1.00/03.0072), and LO1401 from the Czech Republic Government

Modélisation de signaux électromyographiques par des processus de renouvellement - Filtre bayésien pour l'estimation séquentielle de paramètres à destination de la commande d'une prothèse d'avant-bras

soumise aux rapporteurs le 25/10/13We deal with intramuscular electromyographical signals (iEMG signals) taken in the muscles of a human upper limb. iEMG signal are an image of the control of the central nervous system on the muscles. They are made of a superimposition of wavelet trains, each wavelet codes a group of muscle fibers and its discharge rate codes the force developed by the group. The objective is to extract sequentially pieces of information from the signal iEMG. We believe that this information may be used to control an upper limb prosthesis. In the first place, we model a spike train as a Markov chain and we present mass functions to model the inter-spike intervals. The discrete Weibull mass function holds our attention: we realize an online estimation of its parameters. Secondly, we model the iEMG signal with a hidden Markov model based on the above model of spike train. We are able to propagate sequentially Bayes estimator of the parameters of the hidden Markov model with a Bayes filter, particularly the shapes of the wavelets and their discharge rates. Finally, we propose a method to estimate the number of wavelet trains, a discrete parameter of the model. We confirm the proposed methods and algorithms on simulated signals and iEMG signals.Nous traitons des signaux électromyographiques intramusculaires (signaux iEMG) relevés dans les muscles de l'avant-bras. Les signaux iEMG représentent une image de la commande du système nerveux central vers les muscles. Ils se composent d'une superposition de trains d'ondelettes, chaque ondelette code un groupe de fibres musculaires et son taux de mise à feu code l'effort produit par ce groupe. L'objectif est d'extraire de façon séquentielle des informations du signal iEMG. Nous espérons que ces informations se révèleront utiles pour la commande d'une prothèse d'avant-bras. En premier lieu, nous modélisons un train d'impulsions comme une chaîne de Markov et nous discutons des lois pouvant caractériser le temps entre deux impulsions. La loi de Weibull discrète a retenu notre attention. Nous avons mis en place une méthode d'estimation en ligne de ses paramètres. En second lieu, nous modélisons le signal iEMG par un modèle de Markov caché s'appuyant sur le modèle de train d'impulsions ci-dessus. La mise en place d'un filtre bayésien nous permet de propager séquentiellement une estimation bayésienne des paramètres du modèle de Markov caché, en particulier la forme des ondelettes et leur taux de mise à feu. Nous proposons finalement une méthode d'estimation du nombre de trains d'ondelettes, un paramètre discret du modèle. Nous validons les méthodes et algorithmes proposés sur des signaux simulés et des signaux iEMG

A unifying framework for the identification of motor primitives

Chiovetto E, d’Avella A, Giese MA. A unifying framework for the identification of motor primitives. Plos One. Submitted

Waveform analysis of forearm muscle activity during dynamic wrist flexion and extension: Effects of forearm posture and torque direction

Background and Aim: For both isometric and dynamic movements at the wrist, a popular analysis technique for forearm muscle activation includes averaged time-series data that may not represent changes in muscle activity throughout the task. Changes in muscle fiber length and environmental stimuli can alter forearm/upper arm muscle activity during dynamic tasks (D. A. Forman et al., 2020a). The purpose of this study was to determine the effects of forearm posture and torque on forearm muscle activity using waveform analysis. Methods: 12 participants performed a controlled wrist flexion/extension (±40°) tracking task using a wrist manipulandum. Participants were positioned in a neutral, 30° pronated, or 30° supinated forearm posture and the manipulandum applied a constant torque that resisted either wrist extension or flexion. Posture-torque combinations were performed once each, with six flexion/extension repetitions completed per condition. Wrist kinematics were tracked using the manipulandum and the movement cycle was time normalized. Surface electromyography from eight forearm/upper arm muscles were normalized to maximum voluntary contractions. Statistical non-parametric mapping analyzed waveforms for each muscle using a two-way repeated measures ANOVA for main/interaction effects (p=0.05), with post-hoc t-tests. Results: All muscles showed main effects for both posture and torque direction. Decreases in activity were observed in non-neutral forearm postures (flexors: 53-70%, extensors: 5-23% of the cycle). Flexion torque increased muscle activity for FCR, FDS, and FCU during 0-56% and 75-100%, 9-81%, and 22-51% of the movement cycle, respectively. ED and ECU had significantly increased activity during 0-26% and 70-100% of the movement cycle during the extension torque direction. During the neutral-flexion condition, FCR activity increased compared to all other conditions during 58-70% of the movement. Conclusion: When evaluating the entire waveform, non-neutral forearm postures decreased activity for all muscles during specific ranges. The extension torque increased ED and ECU activity at the start and end of the movement, while the flexion torque increased FCR and FDS activity for the majority of the movement. Also, FCR was important in supporting wrist extension during the neutral-flexion condition. Waveform analysis demonstrated complex forearm muscle activity patterns that could provide insight into neuromuscular control, performance, and fatigue progression