Abstract The detection of surface electromyography (sEMG) signals on the skin has attracted increasing attention because its ability to monitor muscle conditions in a noninvasive manner and thus possesses great application potential assess athletic status training efficiency real time or evaluate postoperative rehabilitation conveniently. Here, flexible wireless sEMG monitoring system that cons...

Surface electromyography (sEMG) is an important measurement for monitoring exercise and fitness. Because if its high sampling frequency requirement, wireless transmission of sEMG data is a challenge. In this article a wireless sEMG measurement system with a sampling frequency of 2 KHz is developed based upon a MSP 430 microcontroller and Bluetooth transmission. Standard isotonic and isometric m...

This paper presented a new ant colony optimization (ACO) feature selection method to classify hand motion surface electromyography (sEMG) signals. The multiple channels of sEMG recordings make the dimensionality of sEMG feature grow dramatically. It is known that the informative feature subset with small size is a precondition for the accurate and computationally efficient classification strate...

The aim of this study was an investigation whether the examined subjects can be classified as normal, myopathic or neuropathic, based on 4-channel surface electromyographic (SEMG) recordings of the biceps brachii muscle recorded at isometric voluntary contractions. Five different force levels were used: 10, 30, 50, 70 and 100 % of maximum voluntary contraction (MVC), with each recording lasting...

Abstract The use of surface electromyography (sEMG) is rapidly spreading, from robotic prostheses and muscle computer interfaces to rehabilitation devices controlled by residual muscular activities. In this context, sEMG-based gesture recognition plays an enabling role in controlling prosthetics real-life settings. Our work aimed at developing a low-cost, print-and-play platform acquire analyse...

This chapter presents a usefulness of wavelet transform (WT) algorithm in pre-processing stage of surface electromyography (sEMG) signal analysis particularly in application of noise reduction. The successful pre-processing stage based on wavelet decomposition and denoising algorithm is proposed in this chapter together with the principle, theory, up-todate literature review and experimental re...

In this paper, a novel prediction model is proposed to estimate human continuous motion intention using fuzzy wavelet neural network (FWNN) and zeroing (ZNN). During walking, seven channel surface electromyography (sEMG) signals data of hip knee are collected, two selected processed from the muscles based on physiological correlation analysis. Then, FWNN built as an recognition model, with sEMG...

In the field of human-machine interaction, gesture recognition using sparse multichannel surface electromyography (sEMG) remains a challenge. Based on Hilbert filling curve, dual-view multi-scale convolutional neural network (DVMSCNN) is designed to enhance performance in this paper. The consists two parts. first part, sEMG filled and obtained images time electrode domain are used as inputs blo...

Assessment of liquid intake is necessary to obtain a complete picture an individual’s hydration status. Measurements using state-of-the-art wearable devices have been demonstrated, but none these combined high sensitivity, unobtrusiveness, and automated estimation volume, i.e., machine learning. Such capability would immense value in variety medical contexts, such as monitoring patients with dy...

Surface electromyography signal (sEMG) recognition technology requires a large number of samples to ensure the accuracy training results. However, sEMG signals generally have problems small amount data, complicated acquisition process and environmental influence, which hinders improvement classification. In order improve classification, an feature generation method based on energy generative ad...