ArticleSensors (Basel, Switzerland)2024
Glove-Net: Enhancing Grasp Classification with Multisensory Data and Deep Learning Approach.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Markerless Video-Based Gait Analysis for Motor Phenotyping in Individuals with Schizophrenia Using Interpretable Machine Learning.Diagnostics (Basel, Switzerland) · 2026Article
- A Dual-Task Gait Fusion Framework for Classifying Parkinson's Disease Severity from Wearable Sensor Data.Diagnostics (Basel, Switzerland) · 2026Article
- Feasibility of mechanomyography-based fatigue classification for passive lower-limb exoskeleton evaluation: A pilot study.PloS one · 2026Article
- Integrating contrastive cross-modal attention and stacked GRU for hand function rehabilitation robot control.PloS one · 2026Article
- Synergistic grasp analysis: A cross-sectional exploration using a multi-sensory data glove.Wearable technologies · 2025Article
- A Static Sign Language Recognition Method Enhanced with Self-Attention Mechanisms.Sensors (Basel, Switzerland) · 2024Article
- A Recurrent Deep Network for Gait Phase Identification from EMG Signals During Exoskeleton-Assisted Walking.Sensors (Basel, Switzerland) · 2024Article
- Walking and scuba diving assisted amphibious exoskeleton robots: the designing of power assist control and myoelectricity based wearers' fatigue evaluation.Frontiers in neuroscience · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Grasp classification is pivotal for understanding human interactions with objects, with wide-ranging applications in robotics, prosthetics, and rehabilitation. This study introduces a novel methodology utilizing a multisensory data glove to capture intricate grasp dynamics, including finger posture bending angles and fingertip forces. Our dataset comprises data collected from 10 participants engaging in grasp trials with 24 objects using the YCB object set. We evaluate classification performance under three scenarios: utilizing grasp posture alone, utilizing grasp force alone, and combining both modalities. We propose
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.