The first few months of 2020 have profoundly changed the way we live our lives and carry
out our daily activities. Although the widespread use of futuristic robotaxis and self-driving …

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

The SARS-CoV2 virus, which causes COVID-19 (coronavirus disease) has become a
pandemic and has expanded all over the world. Because of increasing number of cases day …

To determine the applications of machine learning (ML) techniques used for the primary
prevention of work-related musculoskeletal disorders (WMSDs), a scoping review was …

Features extracted from the electromyography (EMG) signal normally consist of irrelevant
and redundant features. Conventionally, feature selection is an effective way to evaluate the …

Open wireless sensor networks (WSNs) in Internet of things (IoT) has led to many zero-day
security attacks. Since intrusion detection is a key security solution, this paper presents a …

Exercise fatigue is a common physiological phenomenon in human activities. The
occurrence of exercise fatigue can reduce human power output and exercise performance …

The main objective of this work is to establish a framework for processing and evaluating the
lower limb electromyography (EMG) signals ready to be fed to a rehabilitation robot. We …

In this work, an attempt has been made to classify emotional states using Electrodermal
Activity (EDA) signals and Convolutional Neural Network (CNN) learned features. The EDA …

Muscle fatigue detection based on surface Electromyography (sEMG) is one of the essential
goals of human–computer interaction. The main challenge is that the sEMG signal is …