ArticleSensors (Basel, Switzerland)2020
An sEMG-Controlled 3D Game for Rehabilitation Therapies: Real-Time Time Hand Gesture Recognition Using Deep Learning Techniques.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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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.
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Who cites it
24 citing papers in PubMed, 92 citations in OpenAlex.
- Dynamic hand exercise recognition for game-based finger rehabilitation.Scientific reports · 2026Article
- MightyU - A portable sensor-based video game application for exercise training of children and adolescents with cerebral palsy.PloS one · 2026Article
- Stage-specific EMG feature optimization for enhanced post-stroke hand gesture recognition.Journal of neuroengineering and rehabilitation · 2025Article
- Exploratory development of human-machine interaction strategies for post-stroke upper-limb rehabilitation.Journal of neuroengineering and rehabilitation · 2025Article
- A Novel Bilateral Data Fusion Approach for EMG-Driven Deep Learning in Post-Stroke Paretic Gesture Recognition.Sensors (Basel, Switzerland) · 2025Article
- RETRACTED: Optimizing the impact of time domain segmentation techniques on upper limb EMG decoding using multimodal features.PloS one · 2025Article
- Hand gestures classification of sEMG signals based on BiLSTM-metaheuristic optimization and hybrid U-Net-MobileNetV2 encoder architecture.Scientific reports · 2024Article
- Phasor-Based Myoelectric Synergy Features: A Fast Hand-Crafted Feature Extraction Scheme for Boosting Performance in Gait Phase Recognition.Sensors (Basel, Switzerland) · 2024Article
- Article
- Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education.Cureus · 2024Review
- The Role of AI in Serious Games and Gamification for Health: Scoping Review.JMIR serious games · 2024Article
- Editorial: AI empowered cerebro-cardiovascular health engineering.Frontiers in physiology · 2023Article
- Artificial Intelligence-Driven Serious Games in Health Care: Scoping Review.JMIR serious games · 2022Article
- Hand Exoskeleton Design and Human-Machine Interaction Strategies for Rehabilitation.Bioengineering (Basel, Switzerland) · 2022Article
- A Simulation Study to Assess the Factors of Influence on Mean and Median Frequency of sEMG Signals during Muscle Fatigue.Sensors (Basel, Switzerland) · 2022Article
- Multi-Category Gesture Recognition Modeling Based on sEMG and IMU Signals.Sensors (Basel, Switzerland) · 2022Article
- An sEMG-Controlled Forearm Bracelet for Assessing and Training Manual Dexterity in Rehabilitation: A Systematic Review.Journal of clinical medicine · 2022Review
- CSAC-Net: Fast Adaptive sEMG Recognition through Attention Convolution Network and Model-Agnostic Meta-Learning.Sensors (Basel, Switzerland) · 2022Article
- An interactive game for rehabilitation based on real-time hand gesture recognition.Frontiers in physiology · 2022Article
- Effects of EMG-Controlled Video Games on the Upper Limb Functionality in Patients with Multiple Sclerosis: A Feasibility Study and Development Description.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
In recent years the advances in Artificial Intelligence (AI) have been seen to play an important role in human well-being, in particular enabling novel forms of human-computer interaction for people with a disability. In this paper, we propose a sEMG-controlled 3D game that leverages a deep learning-based architecture for real-time gesture recognition. The 3D game experience developed in the study is focused on rehabilitation exercises, allowing individuals with certain disabilities to use low-cost sEMG sensors to control the game experience. For this purpose, we acquired a novel dataset of seven gestures using the Myo armband device, which we utilized to train the proposed deep learning model. The signals captured were used as an input of a Conv-GRU architecture to classify the gestures. Further, we ran a live system with the participation of different individuals and analyzed the neural network's classification for hand gestures. Finally, we also evaluated our system, testing it for 20 rounds with new participants and analyzed its results in a user study.
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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.