The pre-clinical diagnostics is essential for management of Parkinson's disease (PD). Although PD has been studied intensively in the last decades, the pre-clinical indicators of that motor disorder have yet to be established. Several approaches were proposed but the definitive method is still lacking. Here we report on the non-linear characteristics of surface electromyogram (sEMG) and tremor ...

This paper proposes a neuromusculoskeletal (NMS) model to predict individual muscle force during elbow flexion and extension. Four male subjects were asked to do voluntary elbow flexion and extension. An inertial sensor and surface electromyography (sEMG) sensors were attached to subject's forearm. Joint angle calculated by fusion of acceleration and angular rate using an extended Kalman filter...

In this paper, we investigate the use of surface electromyographic (sEMG) signals collected from articulatory muscles on the face and neck for performing automatic speech recognition. While previous work has typically used full-scale recognition experiments to evaluate appropriate feature representation schemes for sEMG signals, we present a systematic information-theoretic analysis for feature...

The Electromyogram (EMG) signals recorded from the back muscles often contain large electrocardiogram (ECG) artefacts. For better interpretation of these SEMG signals, it is essential to remove ECG artefacts. This paper reports research conducted to address the problem of removing ECG artefacts from SEMG recordings using new approach of Independent Component Analysis (ICA) called Multi-step ICA...

The control of prosthetic limb would be more effective if it is based on Surface Electromyogram (SEMG) signals from remnant muscles. The analysis of SEMG signals depend on a number of factors, such as amplitude as well as timeand frequency-domain properties. Time series analysis using Auto Regressive (AR) model and Mean frequency which is tolerant to white Gaussian noise are used as feature ext...

BACKGROUND Surface electromyography (sEMG) signals have been used in numerous studies for the classification of hand gestures and movements and successfully implemented in the position control of different prosthetic hands for amputees. sEMG could also potentially be used for controlling wearable devices which could assist persons with reduced muscle mass, such as those suffering from sarcopeni...

The present paper proposes a method for estimating joint angular velocities from multi-channel surface electromyogram (sEMG) signals. This method uses a selective desensitization neural network (SDNN) as a function approximator that learns the relation between integrated sEMG signals and instantaneous joint angular velocities. A comparison experiment with a Kalman filter model shows that this m...

Surface electromyography (sEMG) is a popular research tool in sport and rehabilitation sciences. Common study designs include the comparison of sEMG amplitudes collected from different muscles as participants perform various exercises and techniques under different loads. Based on such comparisons, researchers attempt to draw conclusions concerning the neuro- and electrophysiological underpinni...

The surface electromyography (sEMG) signal is a low amplitude signal that emanates from contracting muscles. It can be used directly to measure muscle activity (once noise has been removed) or it can be smoothed for some other application, e.g., orthoses or prostheses control. Here, an automatic heuristic procedure is presented which applies singular spectrum analysis (SSA) and cluster analysis...

Identifying finger and wrist flexion based actions using single channel surface electromyogram have a number of rehabilitation, defence and human computer interface applications. These applications are currently infeasible because of unreliability in classification of sEMG when the level of muscle contraction is low and when there are multiple active muscles. The presence of noise and cross-tal...