ReviewiScience2026
Advances in electromyography armbands for gesture recognition and multimodal fusion.
Review in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- MXene/graphene composite flexible impedance glove with wide linear range for cross-subject gesture recognition.Microsystems & nanoengineering · 2026Article
- Systematic Evaluation of sEMG Processing Pipelines for Gesture Recognition.Bioengineering (Basel, Switzerland) · 2026Article
- Two-Stage Meta-Learning with Matched Feature Regularization for Cross-Subject sEMG Gesture Recognition Under Posture Variation.Sensors (Basel, Switzerland) · 2026Article
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
No grant is acknowledged in the PubMed record.
Abstract
Surface electromyography (sEMG) signals carry abundant information regarding human motion and muscle activity, and armbands equipped with sEMG acquisition can decode gestures via pattern recognition algorithms. Consequently, sEMG armbands have been increasingly adopted for building natural and efficient human-machine interfaces. With the expansion of datasets, rapid hardware iteration, and the emergence of multiple sensing modalities, it has become essential to systematically examine how different armband designs and integration strategies affect recognition performance. This paper systematically reviews the architectures and technical specifications of mainstream sEMG armbands and compares the integration and performance of additional modalities-such as inertial measurement unit (IMU), force myography (FMG), magnetomyography (MMG), sonomyography (SMG), near-infrared spectroscopy (NIRS), light myography (LMG), and electrical impedance tomography (EIT)-within sEMG-based systems. This review also highlights the conceptual value of multimodal fusion for improving robustness and generalizability and outlines directions for developing lightweight, low-power, and cost-effective armbands that better support complex human-machine interaction scenarios.
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.