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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Pattern recognition algorithms may be used to identify human activities.
“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]
The F1-score estimated for deep SRUs-GRUs models is greater than the F1-score generated for current models.
“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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- MOHAMMAD MEHEDI HASSAN (SM'18) received the Ph.D. degree in computer engineering from Kyung Hee University, South Korea, in 2011. He is currently an Associate Professor with the Information Systems Department, College of Computer and Information Sciences (CCIS), King Saud University (KSU), Riyadh, Saudi Arabia. He has published more than 130 research papers in the journals and conferences of international repute. He has also played role of the guest editor of several international ISI-indexed journals. His research interests include cloud computing, the Internet of Things, cognitive computing, sensor net- works, big data, mobile cloud, and publish/subscribe systems. He received the Best Journal Paper Award from the IEEE SYSTEMS JOURNAL, in 2018, the Best Paper Award from CloudComp Conference at China, in 2014, and Excellence in Research Award from CCIS, KSU, in 2015 and 2016, respectively. ABDULHAMEED ALELAIWI received the Ph.D. degree in software engineering from the College of Engineering, Florida Institute of Technology- Melbourne, USA, in 2002. He is currently an Associate Professor with the Software Engi- neering Department, College of Computer and Information Sciences (CCIS), King Saud Uni- versity (KSU), Riyadh, Saudi Arabia. He is also serving as the Vice Dean for Research Chairs Program at KSU. He has authored and coauthored many publications. He has published more than 70 research papers in the ISI-Indexed journals of international repute. He has secured several national and international research grants in the domain of software engineering, cloud computing, and pervasive healthcare. His research interests include software testing analysis and design, cloud computing, multimedia, the Internet of things, big data, and mobile cloud. He has served as a Technical Program Committee Member in numerous reputed international confer- ences/workshops. He has also played role of the Guest Editor of several international ISI-indexed journals.
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- 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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