Pattern recognition of human arm movement using deep reinforcement learning
Abstract
Hand gesture recognition is one of the major research areas in the field of Human computer interaction (HCl). This paper proposes a deep reinforcement learning algorithm to recognize the human arm movement patterns using an IoT sensor device. Recent studies have explored supervised learning based methods, such as CNN and RNN to implement the HCl device. On the other hand, the deep reinforcement learning approach has also been investigated. Algorithms using this approach, learn the patterns from sensors using only the reward feedback with no class labels. This allows users to control the IoT device and produce the desired arm movement patterns without creating any labels. In this paper, the performance of convolutional neural network (CNN) with the DQN model was compared with that of long short-term memory (LSTM) models with DQN. Results show that the CNN based DQN model was more stable compared to the LSTM based model, and yielded a high classification accuracy of 98.33% to predict the arm movement patterns.
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[230] uses DRL to predict arm movements with 98.33% accuracy.
“In the domain of HAR, [230] uses DRL to predict arm movements with 98.33% accuracy.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [230]
In DL-based HAR, the reward of the reinforcement learning agent has to be given by a human.
“Additionally, in the realm of DL-based HAR, the reward of the agent has to be given by a human, as in the case of [230,232].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [230]
Reinforcement learning is a relatively new area being explored for select areas in HAR, such as modelling muscle and arm movements.
“Reinforcement learning is a relatively new area that is being explored for select areas in HAR, such as modelling muscle and arm movements [230,231].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [230]
The CNN exceeded the LSTM in terms of developing an ideal Deep Q Network for pattern recognition of human arm movement and gesture recognition using deep learning methods and wearable-sensor technologies.
“The CNN exceeded the LSTM in terms of developing an ideal Deep Q Network (DQN) for pattern recognition of human arm movement and gesture recognition using DL methods and wearable-sensor technologies [167].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [167]
Cites
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225 · see citing works
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471 · see citing works
(1998) · Kybernetes · doi:10.1108/k.1998.27.9.1093.3 · Cited by 1,576 · see citing works
Boyali, A., & Hashimoto, N. (2015) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2015.09.001 · Cited by 52 · see citing works
The bionic hand movement using myo sensor and neural networks
Pambudi, M. R., Sigit, R., & Harsono, T. (2016) · doi:10.1109/kcic.2016.7883656 · Cited by 10 · see citing works
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Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625 · see citing works
“Additionally, in the realm of DL-based HAR, the reward of the agent has to be given by a human, as in the case of [40, 246].”
BackgroundMethodInfluentialReview of Wearable Devices and Data Collection Considerations for Connected Health
Vijayan, V., Connolly, J. P., Condell, J., McKelvey, N., & Gardiner, P. (2021) · Sensors · doi:10.3390/s21165589 · Cited by 435 · see citing works
Hand Gesture Recognition Using EMG-IMU Signals and Deep Q-Networks
Vásconez, J. P., Barona López, L. I., Valdivieso Caraguay, Á. L., & Benalcázar, M. E. (2022) · Sensors · doi:10.3390/s22249613 · Cited by 55 · see citing works
- Authors
- Seok, W., Kim, Y., & Park, C.
- Affiliations
- Kwangwoon University (KR)
- Venue
- 2018 International Conference on Information Networking (ICOIN)
- Publisher
- IEEE
- Published
- 2018-01
- Pages
- 917-919
- ISSN
- 1976-7684 (ISSN-L)
- OpenAlex
- W2798400628
- Cited by
- 25
- References
- 6
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 4 · 2023: 4 · 2022: 5 · 2021: 4 · 2019: 3 · 2018: 1
- FWCI
- 1.61
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Information Networking (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Keywords
- hand gesture recognition, deep reinforcement learning, deep Q-network, convolutional neural networks, long short-term memory, IoT sensor data, human-computer interaction, label-free learning
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