Multi-input CNN-GRU based human activity recognition using wearable sensors
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Dua et al. proposed a CNN combined with GRU model and obtained accuracy of 96.20%, 97.21%, and 95.27% on UCI-HAR, WISDM, and PAMAP2 datasets, respectively.
“Dua et al. [248] proposed a model with CNN combined with GRU and obtained an accuracy of 96.20%, 97.21%, and 95.27% on UCI-HAR, WISDM [81], and PAMAP2 [91] datasets, respectively.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [248]
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- Authors
- Dua, N., Singh, S. N., & Semwal, V. B.
- Affiliations
- National Institute of Technology Jamshedpur (IN)
- Maulana Azad National Institute of Technology (IN)
- Corresponding author
- Dua, Nidhi
- Venue
- Computing
- Published
- 2021-03-10
- Volume
- 103
- Issue
- 7
- Pages
- 1461-1478
- ISSN
- 0010-485X (ISSN-L)
- 1436-5057
- OpenAlex
- W3133590696
- Cited by
- 432
- References
- 54
- Citations by year
- 2026: 44 · 2025: 82 · 2024: 96 · 2023: 122 · 2022: 70 · 2021: 18
- FWCI
- 27.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computing (publishedVersion, public-domain)
- Funders
- Science and Engineering Research Board (ECR/2018/000203)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- human activity recognition, wearable sensors, CNN-GRU, convolutional neural networks, gated recurrent unit, end-to-end feature extraction, accelerometer, gyroscope, time series classification, UCI-HAR, WISDM, PAMAP2
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