ArticleFrontiers in neurorobotics2024
Posture-invariant myoelectric control with self-calibrating random forests.
Article in Frontiers in neurorobotics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Assessing the Resilience of sEMG Classifiers to Sensor Malfunction and Signal Saturation.Sensors (Basel, Switzerland) · 2026Article
- Predicting Motor Intent from Residual Neck Muscle Activity in Individuals with Neck Weakness from ALS.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025Article
- EMG Dataset for Gesture Recognition with Arm Translation.Scientific data · 2025Article
- Pre-training, personalization, and self-calibration: all a neural network-based myoelectric decoder needs.Frontiers in neurorobotics · 2025Article
- Influence of electrode placement on the recognition of different gesture categories using high-density sEMG.Frontiers in neuroscience · 2025Article
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Authors and funding
3 authors.
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Abstract
Introduction: Myoelectric control systems translate different patterns of electromyographic (EMG) signals into the control commands of diverse human-machine interfaces via hand gesture recognition, enabling intuitive control of prosthesis and immersive interactions in the metaverse. The effect of arm position is a confounding factor leading to the variability of EMG characteristics. Developing a model with its characteristics and performance invariant across postures, could largely promote the translation of myoelectric control into real world practice. Methods: Here we propose a self-calibrating random forest (RF) model which can (1) be pre-trained on data from many users, then one-shot calibrated on a new user and (2) self-calibrate in an unsupervised and autonomous way to adapt to varying arm positions. Results: Analyses on data from 86 participants (66 for pre-training and 20 in real-time evaluation experiments) demonstrate the high generalisability of the proposed RF architecture to varying arm positions. Discussion: Our work promotes the use of simple, explainable, efficient and parallelisable model for posture-invariant myoelectric control.
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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.