AIMC Topic: Electromyography

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Functional motor mapping of domestic pig lumbar spinal cord using penetrating microelectrodes.

Journal of neuroengineering and rehabilitation
The restoration of standing and walking after spinal cord injury (SCI) remains a top priority for individuals with paraplegia. Despite significant advancements in neuromodulation techniques, challenges such as limited selectivity and inconsistent out...

L-SHADE optimized learning framework for sEMG hand gesture recognition.

Scientific reports
In recent years, Hand Gesture Recognition (HGR) devices have been designed to recognize gestures in real time using machine-learning classifiers (MLCs). However, the performance of these classifiers heavily relies on the tuning of their hyperparamete...

A parallel and efficient transformer deep learning network for continuous estimation of hand kinematics from electromyographic signals.

Scientific reports
Surface electromyography (EMG) provides a non-invasive human-machine interaction interface that can promote the coherence of human-machine interaction operations. Decomposing surface electromyographic signals into hand joint angles in real time can b...

Real-time biofeedback monitoring rehabilitation of distal radius fracture.

Journal of neuroengineering and rehabilitation
BACKGROUND: Elderly patients often face challenges in recovering from distal radius fractures (DRFs), and inadequately guided rehabilitation may lead to delayed healing or secondary injury.

SHAP-driven insights into multimodal data: behavior phase prediction for industrial safety applications.

Scientific reports
Unsafe behaviors among coal miners are a primary factor contributing to accidents, posing significant challenges for safety management. This study develops a behavior state prediction framework using artificial intelligence and machine learning (ML) ...

A novel sEMG-based hand gesture prediction method using a new motion detection algorithm and an LCNN model.

Biomedical physics & engineering express
This paper proposes a novel gesture prediction method for accurately predicting hand gesture types from raw sEMG signals in real time. First, we utilize a linear combination of the mean and standard deviation of sEMG signals within a sliding window t...

FatigueNet: A hybrid graph neural network and transformer framework for real-time multimodal fatigue detection.

Scientific reports
Fatigue creates complex challenges that present themselves through cognitive problems alongside physical impacts and emotional consequences. FatigueNet represents a modern multimodal framework that deals with two main weaknesses in present-day fatigu...

From zero- to few-shot: deep temporal learning of wrist EMG enables scalable cross-user gesture recognition.

Journal of neural engineering
Wrist electromyography (EMG) is emerging as an enticing wearable input modality for human-machine interaction. Traditionally recorded from the forearm for use in transradial prostheses, wrist-based EMG sensors are now being integrated into devices su...

Motor unit number estimation based on convolutional neural network.

Journal of neural engineering
. The compound muscle action potential (CMAP) scan contains a muscle's detailed stimulus-activation information and thereby can be used for motor unit number estimation (MUNE). Due to the challenges in accurately obtaining the motor unit numbers from...

Hip, knee, and ankle joint forces during exoskeletal-assisted walking: Comparison of approaches to simulate human-robot interactions.

PloS one
The overall goal of this study was to develop a computational framework to quantify hip, knee, and ankle joint forces during exoskeletal-assisted walking (EAW) in the ReWalk P6.0, an FDA-approved lower-extremity exoskeleton. The first objective was t...