ArticleScientific data2025
Hand kinematics, high-density sEMG comprising forearm and far-field potentials for motion intent recognition.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- ULTRA-MoCap: A Multimodal IMU and sEMG Dataset for Upper Body Joint Kinematics Analysis.Scientific data · 2026Article
- PhysioMio: bilateral and longitudinal HD-sEMG dataset of 16 hand gestures from 48 stroke patients.Scientific data · 2026Article
- L-SHADE optimized learning framework for sEMG hand gesture recognition.Scientific reports · 2025Article
- Research on Eye-Tracking Control Methods Based on an Improved YOLOv11 Model.Sensors (Basel, Switzerland) · 2025Article
- Hand kinematics, high-density sEMG comprising forearm and far-field potentials for motion intent recognition.Scientific data · 2025Article
- Influence of electrode placement on the recognition of different gesture categories using high-density sEMG.Frontiers in neuroscience · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Surface electromyography (sEMG) signals reflect spinal motor neuron activities, and can be used as intuitive inputs for human-machine interaction (HMI) via movement intent recognition. The motor neuron potentials of far-field (wrist) and near-field (forearm) decomposed from high-density (HD) sEMG prospectively provide robust neural drives for HMI, which is a challenging research hotspot. However, there are no publicly available databases that include HD sEMG signals of forearm-wrist (FW) muscles, and hand kinematics (KIN). This paper presents the HD-FW KIN dataset that comprises HD 448-channel sEMG arrays distributed on forearm and wrist with simultaneously recording of finger joint angles and finger flexion forces. This dataset contains muscle activities of 21 subjects while performing 20 hand gestures, and 9 individual or combined finger flexion under two force levels. The usabilities of HD sEMG for hand gesture recognition, finger angle and force prediction were validated. The proposed database allows a comprehensive extraction of the neural drive from forearm and wrist, providing neural interfaces for the development of advanced prosthetic hand and wrist-worn consumer electronics.
Indexed as
Identifiers
What OpenQuestion holds
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.