Hand Gesture Recognition Using EMG-IMU Signals and Deep Q-Networks
Open accessAbstract
Hand gesture recognition systems (HGR) based on electromyography signals (EMGs) and inertial measurement unit signals (IMUs) have been studied for different applications in recent years. Most commonly, cutting-edge HGR methods are based on supervised machine learning methods. However, the potential benefits of reinforcement learning (RL) techniques have shown that these techniques could be a viable option for classifying EMGs. Methods based on RL have several advantages such as promising classification performance and online learning from experience. In this work, we developed an HGR system made up of the following stages: pre-processing, feature extraction, classification, and post-processing. For the classification stage, we built an RL-based agent capable of learning to classify and recognize eleven hand gestures—five static and six dynamic—using a deep Q-network (DQN) algorithm based on EMG and IMU information. The proposed system uses a feed-forward artificial neural network (ANN) for the representation of the agent policy. We carried out the same experiments with two different types of sensors to compare their performance, which are the Myo armband sensor and the G-force sensor. We performed experiments using training, validation, and test set distributions, and the results were evaluated for user-specific HGR models. The final accuracy results demonstrated that the best model was able to reach up to 97.50%±1.13% and 88.15%±2.84% for the classification and recognition, respectively, with regard to static gestures, and 98.95%±0.62% and 90.47%±4.57% for the classification and recognition, respectively, with regard to dynamic gestures with the Myo armband sensor. The results obtained in this work demonstrated that RL methods such as the DQN are capable of learning a policy from online experience to classify and recognize static and dynamic gestures using EMG and IMU signals.
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This type of research is often used in hand rehabilitation training, gesture recognition systems, and prosthetic control.
“This type of research is often used in hand rehabilitation training 98 , gesture recognition systems 132 , and prosthetic control 131,133 .”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [132]
The combination of IMU and EMG achieves 97.5% accuracy in a gesture recognition system with 11 motion modes.
“| IMU and EMG | Gesture recognition system | 11 | 97.5% | 132 |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [132]
The combination of IMU and EMG with ANN achieves 97.5% accuracy with 7% optimization.
“| IMU and EMG | ANN | 97.5% | 7% | 132 |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [132]
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- Authors
- Vásconez, J. P., Barona López, L. I., Valdivieso Caraguay, Á. L., & Benalcázar, M. E.
- Affiliations
- National Polytechnic School (EC)
- Artificial Intelligence and Computer Vision Research Lab, Escuela Politécnica Nacional, Quito 170517, Ecuador
- Corresponding author
- Benalcázar, Marco E.
- ORCID
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2022-12-08
- Volume
- 22
- Issue
- 24
- Pages
- 9613
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC9784287
- OpenAlex
- W4311855658
- Cited by
- 55
- References
- 28
- Citations by year
- 2026: 15 · 2025: 21 · 2024: 11 · 2023: 8
- FWCI
- 4.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- hand gesture recognition, electromyography, inertial measurement unit, deep Q-network, reinforcement learning, Myo armband, static and dynamic gestures, user-specific models, gesture classification
- MeSH terms
- Algorithms, Electromyography, Gestures, Hand, Neural Networks, Computer, Upper Extremity
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