In the area of biomedical digital signal processing (DSP), wavelet analysis, neural networks and pattern recognition methods are being developed for analysis of EMG signals (generated by the muscles) in neuromuscular disease and CTG (the cardiotocogram) signals during labor. These are traditionally very difficult signals to quantify and innovative approaches to analysis are required for clinica...

Numerous medical and biomechanical applications involve electromyogram (EMG) signal processing in real time. Amplitude analysis of the EMG often requires computation of the signal's linear envelope. For this purpose, several methods are commonly described in the literature; however, not all match the speed requirement of real-time applications. We introduce an implementation which accelerates t...

This paper presents the investigation pattern of arm movements for the purpose of the rehabilitation for a stroke patient in the virtual reality (VR) environments. The investigation results are used to design the virtual objects in the virtual environments. The muscle activities are analyzed by using electromyography (EMG). Six EMG channel are attached to the right arm of the subject, which is ...

The selection of most suitable mother wavelet function is still an open research problem in various signal and image processing applications. This paper presents a comparative study of different wavelet families (Daubechies, Symlets, Coiflets, and Biorthogonal) for analysis of wrist motions from electromyography (EMG) signals. EMG signals are decomposed into three levels using discrete wavelet ...

The aim of this study was to investigate the influence of fatty tissue layer on EMG signal parameters as a function of force level and type of muscle in young women. On the basis of body mass index (BMI) and the amount of fatty tissue (FT) 30 young women were divided into two equal groups: obese (O) and reference group (R). The EMG signal was measured on 5 levels of load from 2 muscles: palmari...

The electromyogram (EMG) signal contains lots of information about muscular changes related muscular disorder such as myopathy. In this paper, we present a new method for classification of EMG signals based on singular value decomposition. The EMG signal represent in the matrix form, features extracted from the singular value decomposition and singular value of EMG signal used as features for c...

Manual lifting is one of the common practices used in the industries to transport or move objects to a desired place. Nowadays, even though mechanized equipment is widely available, manual lifting is still considered as an essential way to perform material handling task. Improper lifting strategies may contribute to musculoskeletal disorders (MSDs), where overexertion contributes as the highest...

In order to analyse surface electromyography (EMG) signals, it is necessary to extract the features based on a time or frequency domain. These approaches are based on the mathematical assumption of signal stationarity. Stationarity of EMG signals is thoroughly examined, especially in isotonic contractions. According to research, conflicting results have been identified depending on varying wind...

Electromyography (EMG) is widely applied for neural engineering. For motion pattern recognition, many features of multi-channel EMG signals were investigated, but the relationships between muscles were not considered. In this study, a novel STFT-ranking feature based on short-time Fourier transform (STFT) is proposed. The novelty of STFT-ranking features is considering and covering the relation...