Highly Accurate Real-Time Decomposition of Single Channel Intramuscular EMG.

OBJECTIVE: Real-time intramuscular electromyography (iEMG) decomposition, as an identification procedure of individual motor neuron (MN) discharge timings from a streaming iEMG recording, has the potential to be used in human-machine interfacing. However, for these applications, the decomposition accuracy and speed of current approaches need to be improved. METHODS: In our previous work, a real-time decomposition algorithm based on a Hidden Markov Model of EMG, using GPU-implemented Bayesian filter to estimate the spike trains of motor units (MU) and their action potentials (MUAPs), was proposed. In this paper, a substantially extended version of this algorithm that boosts the accuracy while maintaining real-time implementation, is introduced. Specifically, multiple heuristics that aim at resolving the problems leading to performance degradation, are applied to the original model. In addition, the recursive maximum likelihood (RML) estimator previously used to estimate the statistical parameters of the spike trains, is replaced by a linear regression (LR) estimator, which is computationally more efficient, in order to ensure real-time decomposition with the new heuristics. RESULTS: The algorithm was validated using twenty-one experimental iEMG signals acquired from the tibialis anterior muscle of five subjects by fine wire electrodes. All signals were decomposed in real time. The decomposition accuracy depended on the level of muscle activation and was when less than 10 MUs were identified, substantially exceeding previous real-time results. CONCLUSION: Single channel iEMG signals can be very accurately decomposed in real time with the proposed algorithm. SIGNIFICANCE: The proposed highly accurate algorithm for single-channel iEMG decomposition has the potential of providing neural information on motor tasks for human interfacing.

Recursive Decomposition of Electromyographic Signals With a Varying Number of Active Sources: Bayesian Modeling and Filtering.

OBJECTIVE: This paper describes a sequential decomposition algorithm for single-channel intramuscular electromyography (iEMG) generated by a varying number of active motor neurons. METHODS: 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 with 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. RESULTS: 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%. CONCLUSION: 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 real-time applications in human-machine interfaces. SIGNIFICANCE: The proposed method substantially broadens the domains of applicability of the algorithm.

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.

Simulation of Motor Unit Action Potential Recordings From Intramuscular Multichannel Scanning Electrodes.

Multi-channel intramuscular EMG (iEMG) provides information on motor neuron behavior, muscle fiber (MF) innervation geometry and, recently, has been proposed as a means to establish a human-machine interface. OBJECTIVE: to provide a reliable benchmark for computational methods applied to such recordings, we propose a simulation model for iEMG signals acquired by intramuscular multi-channel electrodes. METHODS: we propose several modifications to the existing motor unit action potentials (MUAPs) simulation methods, such as farthest point sampling (FPS) for the distribution of motor unit territory centers in the muscle cross-section, accurate fiber-neuron assignment algorithm, modeling of motor neuron action potential propagation delay, and a model of multi-channel scanning electrode. RESULTS: we provide representative applications of this model to the estimation of motor unit territories and the iEMG decomposition evaluation. Also, we extend it to a full multi-channel iEMG simulator using classic linear EMG modeling. CONCLUSIONS: altogether, the proposed models provide accurate MUAPs across the entire motor unit territories and for various electrode configurations. SIGNIFICANCE: they can be used for the development and evaluation of mathematical methods for multi-channel iEMG processing and analysis.

Morphological self stabilization of locomotion gaits: illustration on a few examples from bio-inspired locomotion.

To a large extent, robotics locomotion can be viewed as cyclic motions, named gaits. Due to the high complexity of the locomotion dynamics, to find the control laws that ensure an expected gait and its stability with respect to external perturbations, is a challenging issue for feedback control. To address this issue, a promising way is to take inspiration from animals that intensively exploit the interactions of the passive degrees of freedom of their body with their physical surroundings, to outsource the high-level exteroceptive feedback control to low-level proprioceptive ones. In this case, passive interactions can ensure most of the expected control goals. In this article, we propose a methodological framework to study the role of morphology in the design of locomotion gaits and their stability. This framework ranges from modelling to control aspects, and is illustrated through three examples from bio-inspired locomotion: a three-dimensional micro air vehicle in hovering flight, a pendular planar climber and a bipedal planar walker. In these three cases, we will see how simple considerations based on the morphology of the body can ensure the existence of passive stable gaits without requiring any high-level control.

Walking Gait Step Length Asymmetry Induced by Handheld Device.

The modeling and feature extraction of human gait motion are crucial in biomechanics studies, human localization, and robotics applications. Recent studies in pedestrian navigation aim at extracting gait features based on the data of low-cost sensors embedded in handheld devices, such as smartphones. The general assumption in pedestrian dead reckoning (PDR) strategy for navigation application is that the presence of a device in hand does not impact the gait symmetry and that all steps are identical. This hypothesis, which is used to estimate the traveled distance, is investigated in this paper with an experimental study. Ten healthy volunteers participated in motion lab tests with a 0.190 kg device in hand. Several walking trials with different device carrying modes and several gait speeds were performed. For a fixed walking speed, it is shown that the steps differ in their duration when holding a mass equivalent to a smartphone mass, which invalidates classical symmetry hypothesis in the PDR step length modeling. It is also shown that this hypothesis can lead to a 2.5% to 6.3% error on the PDR estimated traveled distance for the different walking trials.

Sequential decoding of intramuscular EMG signals via estimation of a Markov model.

This paper addresses the sequential decoding of intramuscular single-channel electromyographic (EMG) signals to extract the activity of individual motor neurons. A hidden Markov model is derived from the physiological generation of the EMG signal. The EMG signal is described as a sum of several action potentials (wavelet) trains, embedded in noise. For each train, the time interval between wavelets is modeled by a process that parameters are linked to the muscular activity. The parameters of this process are estimated sequentially by a Bayes filter, along with the firing instants. The method was tested on some simulated signals and an experimental one, from which the rates of detection and classification of action potentials were above 95% with respect to the reference decomposition. The method works sequentially in time, and is the first to address the problem of intramuscular EMG decomposition online. It has potential applications for man-machine interfacing based on motor neuron activities.

Distribution of forces between synergistics and antagonistics muscles using an optimization criterion depending on muscle contraction behavior.

In this paper, a new neuromusculoskeletal simulation strategy is proposed. It is based on a cascade control approach with an inner muscular-force control loop and an outer joint-position control loop. The originality of the work is located in the optimization criterion used to distribute forces between synergistic and antagonistic muscles. The cost function and the inequality constraints depend on an estimation of the muscle fiber length and its time derivative. The advantages of a such criterion are exposed by theoretical analysis and numerical tests. The simulation model used in the numerical tests consists in an anthropomorphic arm model composed by two joints and six muscles. Each muscle is modeled as a second-order dynamical system including activation and contraction dynamics. Contraction dynamics is represented using a classical Hill's model.