Related papers: Activity Detection from Wearable Electromyogram Se…
Despite the widespread installation of accelerometers in almost all mobile phones and wearable devices, activity recognition using accelerometers is still immature due to the poor recognition accuracy of existing recognition methods and the…
High-density electromyography (HDEMG) can detect myoelectric activity as control inputs to a variety of electronically-controlled devices. Furthermore, HDEMG sensors may be built into a variety of clothing, allowing for a non-intrusive…
Surface electromyographic (sEMG) signal serve as a signal source commonly used for lower limb movement recognition, reflecting the intent of human movement. However, it has been a challenge to improve the movements recognition rate while…
Surface electromyography is a valid tool to gather muscular contraction signals from intact and amputated subjects. Electromyographic signals can be used to control prosthetic devices in a noninvasive way distinguishing the movements…
sEMG pattern recognition algorithms have been explored extensively in decoding movement intent, yet are known to be vulnerable to changing recording conditions, exhibiting significant drops in performance across subjects, and even across…
$\textit{Objective.}$ In this article, we present data and methods for decoding hand gestures using surface electromyogram (EMG) signals. EMG-based upper limb interfaces are valuable for amputee rehabilitation, artificial supernumerary limb…
The aim of this work was to identify six basic movements of the hand using two systems. Being an interdisciplinary topic, there has been conducted studying in the anatomy of forearm muscles, biosignals, the method of electromyography (EMG)…
The main focus of this work is on developing models for the activity profile of a terrorist group, detecting sudden spurts and downfalls in this profile, and, in general, tracking it over a period of time. Toward this goal, a $d$-state…
As a relatively new field, network neuroscience has tended to focus on aggregate behaviours of the brain averaged over many successive experiments or over long recordings in order to construct robust brain models. These models are limited…
Recognizing human activity plays a significant role in the advancements of human-interaction applications in healthcare, personal fitness, and smart devices. Many papers presented various techniques for human activity representation that…
One of most popular experimental techniques for investigation of brain activity is the so-called method of evoked potentials: the subject repeatedly makes some movements (by his/her finger) whereas brain activity and some auxiliary signals…
Surface Electromyography (sEMG) is widely studied for its applications in rehabilitation, prosthetics, robotic arm control, and human-machine interaction. However, classifying Activities of Daily Living (ADL) using sEMG signals often…
Human physical motion activity identification has many potential applications in various fields, such as medical diagnosis, military sensing, sports analysis, and human-computer security interaction. With the recent advances in smartphones…
Electrodermal Activity (EDA) is a non-invasive physiological signal widely available in wearable devices and reflects sympathetic nervous system (SNS) activation. Prior multi-modal studies have demonstrated robust performance in…
With the increased popularity of smart phones, there is a greater need to have a robust authentication mechanism that handles various security threats and privacy leakages effectively. This paper studies continuous authentication for touch…
Developing accurate hand gesture perception models is critical for various robotic applications, enabling effective communication between humans and machines and directly impacting neurorobotics and interactive robots. Recently, surface…
Human activity recognition is critical for applications such as early intervention and health analytics. Traditional activity recognition relies on inertial measurement units (IMUs), which are resource intensive and require calibration.…
Electromyography (EMG) signals are obtained from muscle cell activity. The recording and analysis of EMG signals has several applications. The EMG is of diagnostic importance for treating patients suffering from neurological and…
Thumb gestures provide an effective and unobtrusive input modality for wearable and always-available human-machine interaction. Wrist-worn surface electromyography (sEMG) has emerged as a promising approach for compact and wearable…
Action completion detection is the problem of modelling the action's progression towards localising the moment of completion - when the action's goal is confidently considered achieved. In this work, we assess the ability of two temporal…