ArticleSensors (Basel, Switzerland)2019
Inferring Static Hand Poses from a Low-Cost Non-Intrusive sEMG Sensor.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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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.
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.
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Who cites it
15 citing papers in PubMed, 45 citations in OpenAlex.
- Article
- Multi-Category Gesture Recognition Modeling Based on sEMG and IMU Signals.Sensors (Basel, Switzerland) · 2022Article
- Improving the Robustness of Human-Machine Interactive Control for Myoelectric Prosthetic Hand During Arm Position Changing.Frontiers in neurorobotics · 2022Article
- Effectiveness of soft robotic glove versus repetitive transcranial magnetic stimulation in post-stroke patients with severe upper limb dysfunction: A randomised controlled trial.Frontiers in neurology · 2022Article
- A novel concatenate feature fusion RCNN architecture for sEMG-based hand gesture recognition.PloS one · 2022Article
- Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography.Sensors (Basel, Switzerland) · 2021Article
- Qualitative Recognition of Primary Taste Sensation Based on Surface Electromyography.Sensors (Basel, Switzerland) · 2021Article
- Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future.Frontiers in neuroscience · 2021Review
- An sEMG-Controlled 3D Game for Rehabilitation Therapies: Real-Time Time Hand Gesture Recognition Using Deep Learning Techniques.Sensors (Basel, Switzerland) · 2020Article
- Flexible Virtual Reality System for Neurorehabilitation and Quality of Life Improvement.Sensors (Basel, Switzerland) · 2020Article
- Development of Sign Language Motion Recognition System for Hearing-Impaired People Using Electromyography Signal.Sensors (Basel, Switzerland) · 2020Article
- A Novel Surface Electromyographic Signal-Based Hand Gesture Prediction Using a Recurrent Neural Network.Sensors (Basel, Switzerland) · 2020Article
- Assistance Robotics and Biosensors 2019.Sensors (Basel, Switzerland) · 2020Article
- Lw-CNN-Based Myoelectric Signal Recognition and Real-Time Control of Robotic Arm for Upper-Limb Rehabilitation.Computational intelligence and neuroscience · 2020Article
- Real-Time Surface EMG Pattern Recognition for Hand Gestures Based on an Artificial Neural Network.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Every year, a significant number of people lose a body part in an accident, through sickness or in high-risk manual jobs. Several studies and research works have tried to reduce the constraints and risks in their lives through the use of technology. This work proposes a learning-based approach that performs gesture recognition using a surface electromyography-based device, the Myo Armband released by Thalmic Labs, which is a commercial device and has eight non-intrusive low-cost sensors. With 35 able-bodied subjects, and using the Myo Armband device, which is able to record data at about 200 MHz, we collected a dataset that includes six dissimilar hand gestures. We used a gated recurrent unit network to train a system that, as input, takes raw signals extracted from the surface electromyography sensors. The proposed approach obtained a 99.90% training accuracy and 99.75% validation accuracy. We also evaluated the proposed system on a test set (new subjects) obtaining an accuracy of 77.85%. In addition, we showed the test prediction results for each gesture separately and analyzed which gestures for the Myo armband with our suggested network can be difficult to distinguish accurately. Moreover, we studied for first time the gated recurrent unit network capability in gesture recognition approaches. Finally, we integrated our method in a system that is able to classify live hand gestures.
Indexed as
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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.