IMU Sensor-Based Hand Gesture Recognition for Human-Machine Interfaces
Open accessAbstract
We propose an efficient hand gesture recognition (HGR) algorithm, which can cope with time-dependent data from an inertial measurement unit (IMU) sensor and support real-time learning for various human-machine interface (HMI) applications. Although the data extracted from IMU sensors are time-dependent, most existing HGR algorithms do not consider this characteristic, which results in the degradation of recognition performance. Because the dynamic time warping (DTW) technique considers the time-dependent characteristic of IMU sensor data, the recognition performance of DTW-based algorithms is better than that of others. However, the DTW technique requires a very complex learning algorithm, which makes it difficult to support real-time learning. To solve this issue, the proposed HGR algorithm is based on a restricted column energy (RCE) neural network, which has a very simple learning scheme in which neurons are activated when necessary. By replacing the metric calculation of the RCE neural network with DTW distance, the proposed algorithm exhibits superior recognition performance for time-dependent sensor data while supporting real-time learning. Our verification results on a field-programmable gate array (FPGA)-based test platform show that the proposed HGR algorithm can achieve a recognition accuracy of 98.6% and supports real-time learning and recognition at an operating frequency of 150 MHz.
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The RCE NN-based HGR system had a 98.6% accuracy rate.
“This sort of technology had a 98.6% accuracy rate [182].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [182]
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- Authors
- Kim, M., Cho, J., Lee, S., & Jung, Y.
- Affiliations
- Korea Aerospace University (KR)
- Sejong University (KR)
- Corresponding author
- Jung, Yunho
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-09-04
- Volume
- 19
- Issue
- 18
- Pages
- 3827
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6767360
- OpenAlex
- W2971733775
- Cited by
- 169
- References
- 27
- Citations by year
- 2026: 17 · 2025: 23 · 2024: 24 · 2023: 35 · 2022: 24 · 2021: 31 · 2020: 13 · 2019: 2
- FWCI
- 16.55
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.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 › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- hand gesture recognition, inertial measurement unit, human-machine interaction, dynamic time warping, real-time learning, FPGA implementation
- MeSH terms
- Machine Learning, Algorithms, Gestures, Hand, Humans, User-Computer Interface, Biosensing Techniques, Neural Networks, Computer
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