HiHAR: A Hierarchical Hybrid Deep Learning Architecture for Wearable Sensor-Based Human Activity Recognition
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Wearable sensor-based human activity recognition (HAR) is the study that deals with sensor data to understand human movement and behavior. In a HAR model, feature extraction is widely considered to be the most essential and challenging part as the sensor signals contain important information in both spatial and temporal contexts. In addition, because people often carry out an activity for awhile before changing to another activity, the sensor data also contain long-term context dependencies. In this paper, in order to enhance the long, short-term and spatial features from the sensor data, we propose a hierarchical deep learning-based HAR model (HiHAR) which is constructed from two powerful deep neural network architectures: convolutional neural network (CNN) and bidirectional long short-term memory network (BiLSTM).With the hierarchical structure, HiHAR contains two stages: local and global. In the local stage, a CNN and a BiLSTM are applied on the window-data level to extract local spatiotemporal features. The global stage with another BiSLTM is used to extract long-term context information from adjacent windows in both forward and backward time directions, then performs activity classification task. Our experiment results on two public datasets (UCI HAPT and MobiAct scenario) indicate that the proposed hybrid model achieves competitive performance compared to other state-of-the-art HAR models with an average accuracy of 97.98% and 96.16%, respectively.
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Replacing LSTM with BiGRU and using an optimized attention mechanism reduces computational cost, making CNNd-TAm more efficient than Transformer-based [23] and hierarchical models [19].
“Moreover, replacing LSTM with BiGRU and using an optimized attention mechanism reduces computational cost, making CNNd-TAm more efficient than Transformer-based [23] and hierarchical models [19], and suitable for deployment on resource-constrained wearable devices without compromising accuracy.”
From Adaptive temporal attention mechanism and hybrid deep CNN model for wearable sensor-based human activity recognition (2025) · cites this paper as [19]
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- Authors
- Hoai Thu, N. T., & Han, D. S.
- Affiliations
- Kyungpook National University (KR)
- Venue
- IEEE Access
- Published
- 2021
- Volume
- 9
- Pages
- 145271-145281
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3210561523
- Cited by
- 54
- References
- 43
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 19 · 2023: 11 · 2022: 8
- FWCI
- 3.42
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2021-2020-0-01808)
- IITP
- 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, hierarchical deep learning, convolutional neural networks, bidirectional LSTM, spatiotemporal feature extraction, hybrid CNN-BiLSTM
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