Today, daily life is composed of many computing systems, therefore interacting with them in
a natural way makes the communication process more comfortable. Human–Computer …

The increasing amount of data in electromyographic (EMG) signal research has greatly
increased the importance of develo** advanced data analysis and machine learning …

Current deep-learning models are mostly built upon neural networks, ie multiple layers of
parameterized differentiable non-linear modules that can be trained by backpropagation. In …

Hands are two of the most crucial organs and they play major role for human activities.
Therefore, amputee people experience many difficulties in daily life. To overcome these …

In recent years, sEMG (surface electromyography) signals have been increasingly used to
operate wearable devices. The development of mechanical lower limbs or exoskeletons …

Extraction of potential electromyography (EMG) features has become one of the important
roles in EMG pattern recognition. In this paper, two EMG features, namely, enhanced …

Feature selection is a vital pre-processing phase for most machine learning and data mining
courses. This article proposes new variants of the Harris hawk optimization called memory …

Human lower limb activity recognition (HLLAR) has grown in popularity over the last decade
mainly because to its applications in the identification and control of neuromuscular …

This paper proposed a wearable smart sEMG recorder integrated gradient boosting decision
tree (GBDT) based hand gesture recognition. A hydrogel-silica gel based flexible surface …

Zero crossings are a practical and efficient feature to approximate the frequency of a
sampled series of data. Some research describes in different ways how to compute the zero …