The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
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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In the citing paper
“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]
In the citing paper
“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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Citations
Cites
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
Ordóñez, F., & Roggen, D. (2016) · Sensors · doi:10.3390/s16010115 · Cited by 2,761 · cited 1×
“Among the deep learning methods, Deep Belief Network (DBN) [23]–[25] and Convolutional Neural Network (CNN) network are popular [26], [27].”
MethodA Survey on Human Activity Recognition using Wearable Sensors
Lara, O. D., & Labrador, M. A. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2012.110112.00192 · Cited by 2,662
Deep learning for sensor-based activity recognition: A survey
Wang, J., Chen, Y., Hao, S., Peng, X., & Hu, L. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.02.010 · Cited by 2,014 · cited 1×
“Now-a-days, deep learning methods have been controlling over other ML algorithms for a tremendous scope of utilizations [20]–[28].”
Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks
Serkan Kiranyaz, B., İnce, T., & Gabbouj, M. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2468589 · Cited by 1,997
Activity recognition from accelerometer data
Ravi, N., Dandekar, N., Mysore, P., & Littman, M. L. (2005) · Cited by 1,422
Sensor-Based Activity Recognition
Chen, L., Hoey, J., Nugent, C. D., Cook, D. J., & Yu, Z. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2012.2198883 · Cited by 1,074
Nweke, H. F., Teh, Y. W., Al-garadi, M. A., & Alo, U. R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.056 · Cited by 930
Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges
Gravina, R., Alinia, P., Ghasemzadeh, H., & Fortino, G. (2017) · Information Fusion · doi:10.1016/j.inffus.2016.09.005 · Cited by 899
Facial expression recognition from video sequences: temporal and static modeling
Cohen, I., Sebe, N., Garg, A., Chen, L. S., & Huang, T. S. (2003) · Computer Vision and Image Understanding · doi:10.1016/s1077-3142(03)00081-x · Cited by 874
Cited by
Sorted by citations
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
Review of Wearable Devices and Data Collection Considerations for Connected Health
Vijayan, V., Connolly, J. P., Condell, J., McKelvey, N., & Gardiner, P. (2021) · Sensors · doi:10.3390/s21165589 · Cited by 435
A hybrid deep learning model for efficient intrusion detection in big data environment
Hassan, M. M., Gumaei, A., Alsanad, A., Alrubaian, M., & Fortino, G. (2019) · Information Sciences · doi:10.1016/j.ins.2019.10.069 · Cited by 418
Self-citationDeep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., & Liu, Y. (2020) · arXiv · doi:10.48550/arxiv.2001.07416 · Cited by 314
“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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