ArticleFrontiers in bioengineering and biotechnology2022
Deep learning and session-specific rapid recalibration for dynamic hand gesture recognition from EMG.
Article in Frontiers in bioengineering and biotechnology, 2022. 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.
- Few-shot prototype adaptation for generalizable electromyography gesture recognition.Scientific reports · 2026Article
- Artificial intelligence integration in surgery through hand and instrument tracking: a systematic literature review.Frontiers in surgery · 2025Review
- Influence of electrode placement on the recognition of different gesture categories using high-density sEMG.Frontiers in neuroscience · 2025Article
- Surface electromyography evaluation for decoding hand motor intent in children with congenital upper limb deficiency.Scientific reports · 2024Article
- Effects of Training and Calibration Data on Surface Electromyogram-Based Recognition for Upper Limb Amputees.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
5 authors.
Funding
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Abstract
We anticipate wide adoption of wrist and forearm electomyographic (EMG) interface devices worn daily by the same user. This presents unique challenges that are not yet well addressed in the EMG literature, such as adapting for session-specific differences while learning a longer-term model of the specific user. In this manuscript we present two contributions toward this goal. First, we present the MiSDIREKt (Multi-Session Dynamic Interaction Recordings of EMG and Kinematics) dataset acquired using a novel hardware design. A single participant performed four kinds of hand interaction tasks in virtual reality for 43 distinct sessions over 12 days, totaling 814 min. Second, we analyze this data using a non-linear encoder-decoder for dimensionality reduction in gesture classification. We find that an architecture which recalibrates with a small amount of single session data performs at an accuracy of 79.5% on that session, as opposed to architectures which learn solely from the single session (49.6%) or learn only from the training data (55.2%).
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