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.

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

Unlabelled: 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.

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).

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.

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.

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.

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.

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.