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A Hybrid Deep Learning Model for Human Activity Recognition Using Multimodal Body Sensing Data
Open accessAbstract
Human activity recognition from multimodal body sensor data has proven to be an effective approach for the care of elderly or physically impaired people in a smart healthcare environment. However, traditional machine learning techniques are mostly focused on a single sensing modality, which is not practical for robust healthcare applications. Therefore, recently increasing attention is being given by the researchers on the development of robust machine learning techniques that can exploit multimodal body sensor data and provide important decision making in Smart healthcare. In this paper, we propose an effective multi-sensors-based framework for human activity recognition using a hybrid deep learning model, which combines the simple recurrent units (SRUs) with the gated recurrent units (GRUs) of neural networks. We use the deep SRUs to process the sequences of multimodal input data by using the capability of their internal memory states. Moreover, we use the deep GRUs to store and learn how much of the past information is passed to the future state for solving fluctuations or instability in accuracy and vanishing gradient problems. The system has been compared against the conventional approaches on a publicly available standard dataset. The experimental results show that the proposed approach outperforms the available state-of-the-art methods.
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“This shows that pattern recognition algorithms may be used to identify human activities [173].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [173]
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“The F1-score estimated for deep SRUs-GRUs models is greater than the F1-score generated for current models.”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [173]
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Deep Learning for Sensor-based Human Activity Recognition
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., & Liu, Y. (2022) · ACM Computing Surveys · doi:10.1145/3447744 · Cited by 690
“ed an Autoencoder to first enhance feature extractions and then applied the cascade CNN-LSTM to extract local and global features for WiFi-based activity recognition. On the other hand, Gumaei et al. [51] proposed a hybrid model of different types of recurrent units (SRUs and GRUs) for handling different aspects of te…”
MethodRamanujam, E., Perumal, T., & Padmavathi, S. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3069927 · Cited by 455
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Self-citationDeep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
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“ed an Autoencoder to first enhance feature extractions and then applied the cascade CNN-LSTM to extract local and global features for WiFi-based activity recognition. On the other hand, Gumaei et al. [51] proposed a hybrid model of different types of recurrent units (SRUs and GRUs) for handling different aspects of te…”
Method
Metadata
- Authors
- Gumaei, A., Hassan, M. M., Alelaiwi, A., & Alsalman, H.
- Affiliations
- King Saud University (SA)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 99152-99160
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2966450377
- Cited by
- 157
- References
- 31
- Citations by year
- 2026: 10 · 2025: 21 · 2024: 22 · 2023: 31 · 2022: 25 · 2021: 27 · 2020: 20 · 2019: 1
- FWCI
- 8.17
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Deanship of Scientific Research, King Saud University (RGP-318)
- 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 › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Keywords
- human activity recognition, hybrid deep learning model, simple recurrent unit, gated recurrent unit, smart healthcare, elderly care, gradient vanishing, sequence data processing
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