Dynamic hand gesture recognition for wearable devices with low complexity recurrent neural networks
Open accessAbstract
Gesture recognition is a very essential technology for many wearable devices. While previous algorithms are mostly based on statistical methods including the hidden Markov model, we develop two dynamic hand gesture recognition techniques using low complexity recurrent neural network (RNN) algorithms. One is based on video signal and employs a combined structure of a convolutional neural network (CNN) and an RNN. The other uses accelerometer data and only requires an RNN. Fixed-point optimization that quantizes most of the weights into two bits is conducted to optimize the amount of memory size for weight storage and reduce the power consumption in hardware and software based implementations.
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A wrist-worn wearable outfitted with an inertial measurement unit (IMU) can easily detect wrist shaking to control smart devices.
“For example, a wrist-worn wearable outfitted with an inertial measurement unit (IMU) can easily detect the wrist shaking [47,48,49] to control smart devices to skip a song by shaking the hand, instead of bringing up the screen, locating, and pushing a button.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [48]
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Flagged first, then by citations
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Cited by
Sorted by citations
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
“For example, a wrist-worn wearable outfitted with an inertial measurement unit (IMU) can easily detect the wrist shaking [250, 324, 336] to control smart devices to skip a song by shaking the hand, instead of bringing up the screen, locating, and pushing a button.”
BackgroundDeep Fisher discriminant learning for mobile hand gesture recognition
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- Authors
- Shin, S., & Sung, W.
- Affiliations
- Seoul National University (KR)
- ORCID
- Shin, Sungho
- Venue
- 2016 IEEE International Symposium on Circuits and Systems (ISCAS)
- Publisher
- IEEE
- Published
- 2016-05
- Pages
- 2274-2277
- ISSN
- 0271-4302 (ISSN-L)
- OpenAlex
- W2513850443
- Cited by
- 59
- References
- 20
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 6 · 2022: 12 · 2021: 6 · 2020: 5 · 2019: 9 · 2018: 8 · 2017: 6 · 2016: 2
- FWCI
- 5.69
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Symposium on Circuits and Systems proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Research Foundation
- Ministry of Science, ICT and Future Planning
- National Research Foundation of Korea (2015R1A2A1A10056051)
- 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 › Advanced Neural Network Applications
- Keywords
- hand gesture recognition, wearable devices, recurrent neural networks, low-complexity neural networks, convolutional neural networks, accelerometer data, video-based gesture recognition, memory reduction, energy consumption reduction, hardware implementation
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