A hybrid deep convolutional and recurrent neural network for complex activity recognition using multimodal sensors
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Lv et al. built a CNN + RNN model with stacked multisensor data in each channel for fusion before feeding into the CNN layer.
“Unlike previous models with one-dimension time-series input, Lv et al. [192] builds a CNN + RNN model with stacked multisensor data in each channel for fusion before feeding into the CNN layer.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [192]
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- Authors
- Lv, M., Xu, W., & Chen, T.
- Affiliations
- Zhejiang University of Technology (CN)
- Corresponding author
- Chen, Tieming
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 362
- Pages
- 33-40
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2962958625
- Cited by
- 64
- References
- 34
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 11 · 2023: 11 · 2022: 10 · 2021: 10 · 2020: 7
- FWCI
- 3.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neurocomputing (publishedVersion)
- Funders
- National Natural Science Foundation of China (61772026)
- Natural Science Foundation of Zhejiang Province (LY18F020033)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Keywords
- multimodal sensors, wearable computing, mobile computing, convolutional recurrent neural network, hierarchical convolutional neural network, temporal relations, sensor fusion, human activity recognition
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