AIMC Topic: Forearm
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Method of Forearm Muscles 3D Modeling Using Robotic Ultrasound Scanning.
Sensors (Basel, Switzerland)
Apr 4, 2025
The accurate assessment of muscle morphology and function is crucial for medical diagnostics, rehabilitation, and biomechanical research. This study presents a novel methodology for constructing volumetric models of forearm muscles based on three-dim...
Machine learning-enhanced back muscle strength prediction considering lifting condition and individual characteristics.
International journal of occupational safety and ergonomics : JOSE
Feb 19, 2025
This study investigated factors influencing back muscle strength, focusing on sex, forearm posture and lifting height. Lower back pain, prevalent in industries involving manual materials handling, is closely linked to back muscle strength. The study ...
Haptiknit: Distributed stiffness knitting for wearable haptics.
Science robotics
Dec 18, 2024
Haptic devices typically rely on rigid actuators and bulky power supply systems, limiting wearability. Soft materials improve comfort, but careful distribution of stiffness is required to ground actuation forces and enable load transfer to the skin. ...
Enhancing Multi-Object Detection in Ultrasound Images Through Semi-Supervised Learning, Focal Loss and Relation of Frame.
Ultrasound in medicine & biology
Sep 21, 2024
OBJECTIVE: To identify musculoskeletal anatomical structures in real time by using deep learning techniques.
Differentiating hand gestures from forearm muscle activity using machine learning.
International journal of occupational safety and ergonomics : JOSE
Aug 14, 2024
This study explored the use of forearm electromyography data to distinguish eight hand gestures. The neural network (NN) and random forest (RF) algorithms were tested on data from 10 participants. As window sizes increase from 200 ms to 1000 ms, the ...
Developing RPC-Net: Leveraging High-Density Electromyography and Machine Learning for Improved Hand Position Estimation.
IEEE transactions on bio-medical engineering
Apr 22, 2024
OBJECTIVE: The purpose of this study was to develop and evaluate the performance of RPC-Net (Recursive Prosthetic Control Network), a novel method using simple neural network architectures to translate electromyographic activity into hand position wi...
Exploratory analysis using machine learning algorithms to predict pinch strength by anthropometric and socio-demographic features.
International journal of occupational safety and ergonomics : JOSE
Mar 30, 2024
. This study examines the role of different machine learning (ML) algorithms to determine which socio-demographic factors and hand-forearm anthropometric dimensions can be used to accurately predict hand function. . The cross-sectional study was cond...
From Forearm to Wrist: Deep Learning for Surface Electromyography-Based Gesture Recognition.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jan 15, 2024
Though the forearm is the focus of the prostheses, myoelectric control with the electrodes on the wrist is more comfortable for general consumers because of its unobtrusiveness and incorporation with the existing wrist-based wearables. Recently, deep...
LSTM-MSA: A Novel Deep Learning Model With Dual-Stage Attention Mechanisms Forearm EMG-Based Hand Gesture Recognition.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Dec 7, 2023
This paper introduces the Long Short-Term Memory with Dual-Stage Attention (LSTM-MSA) model, an approach for analyzing electromyography (EMG) signals. EMG signals are crucial in applications like prosthetic control, rehabilitation, and human-computer...
Novel Wearable HD-EMG Sensor With Shift-Robust Gesture Recognition Using Deep Learning.
IEEE transactions on biomedical circuits and systems
Nov 21, 2023
In this work, we present a hardware-software solution to improve the robustness of hand gesture recognition to confounding factors in myoelectric control. The solution includes a novel, full-circumference, flexible, 64-channel high-density electromyo...
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