The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
RETRACTED: FL-PMI: Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems
Open accessAbstract
Recent technological developments, such as the Internet of Things (IoT), artificial intelligence, edge, and cloud computing, have paved the way in transforming traditional healthcare systems into smart healthcare (SHC) systems. SHC escalates healthcare management with increased efficiency, convenience, and personalization, via use of wearable devices and connectivity, to access information with rapid responses. Wearable devices are equipped with multiple sensors to identify a person’s movements. The unlabeled data acquired from these sensors are directly trained in the cloud servers, which require vast memory and high computational costs. To overcome this limitation in SHC, we propose a federated learning-based person movement identification (FL-PMI). The deep reinforcement learning (DRL) framework is leveraged in FL-PMI for auto-labeling the unlabeled data. The data are then trained using federated learning (FL), in which the edge servers allow the parameters alone to pass on the cloud, rather than passing vast amounts of sensor data. Finally, the bidirectional long short-term memory (BiLSTM) in FL-PMI classifies the data for various processes associated with the SHC. The simulation results proved the efficiency of FL-PMI, with 99.67% accuracy scores, minimized memory usage and computational costs, and reduced transmission data by 36.73%.
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“FL is a paradigm that aims to collaborate on data management and privacy issues by using evaluation metrics without transferring samples [47–49].”
From A comprehensive survey on federated learning techniques for healthcare informatics (2023) · cites this paper as [49]
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“AI and its applications in healthcare have recently made considerable strides [19].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [19]
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Cites
A survey on security and privacy of federated learning
Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., & Srivastava, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.007 · Cited by 1,480
Wearable Sensors for Remote Health Monitoring
Majumder, S., Mondal, T., & Deen, M. (2017) · Sensors · doi:10.3390/s17010130 · Cited by 1,375
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
Chen, Y., Qin, X., Wang, J., Yu, C., & Gao, W. (2020) · IEEE Intelligent Systems · doi:10.1109/mis.2020.2988604 · Cited by 1,005
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones
Micucci, D., Mobilio, M., & Napoletano, P. (2017) · doi:10.20944/preprints201706.0033.v1 · Cited by 596 · cited 4×
“For this evaluation, we considered the most popular and widely used UniMiB SHAR [18] dataset.”
MethodInfluentialUniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones
Micucci, D., Mobilio, M., & Napoletano, P. (2017) · Applied Sciences · doi:10.3390/app7101101 · Cited by 596
IoT-Based Applications in Healthcare Devices
Pradhan, B. K., Bhattacharyya, S., & Pal, K. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/6632599 · Cited by 481
On-body localization of wearable devices: An investigation of position-aware activity recognition
Sztyler, T., & Stuckenschmidt, H. (2016) · 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom) · doi:10.1109/percom.2016.7456521 · Cited by 369
A Review on Human Activity Recognition Using Vision-Based Method
Zhang, S., Wei, Z., Nie, J., Huang, L., Wang, S., & Li, Z. (2017) · Journal of Healthcare Engineering · doi:10.1155/2017/3090343 · Cited by 345
Federated Learning for Cybersecurity: Concepts, Challenges, and Future Directions
Alazab, M., Ramu, S. P., Parimala, M., Maddikunta, P. K. R., Gadekallu, T. R., & Pham, Q. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3119038 · Cited by 326
Self-citationComparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors
Li, F., Shirahama, K., Nisar, M., Köping, L., & Grzegorzek, M. (2018) · Sensors · doi:10.3390/s18020679 · Cited by 302
Research works printed but not linked: 2
- Wu, M., and Luo, J. (2019). Wearable technology applications in healthcare: A literature review. Online J. Nurs. Inform., 23.
- Gadekallu, T.R., Pham, Q.V., Huynh-The, T., Bhattacharya, S., Maddikunta, P.K.R., and Liyanage, M. (2021). Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions. arXiv.
Cited by
Sorted by citations
Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2023) · Cluster Computing · doi:10.1007/s10586-022-03658-4 · Cited by 399
Teo, Z. L., Jin, L., Liu, N., Li, S., Miao, D., Zhang, X., Ng, W. Y., Tan, T. F., Lee, D. M., Chua, K. J., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101419 · Cited by 255
“14 In the original FL framework, a centralized server broadcasts initial weights of the global model parameters to a set of selected participating site.”
MethodEdge Computing in Healthcare: Innovations, Opportunities, and Challenges
Rancea, A., Anghel, I. M., & Cioara, T. (2024) · Future Internet · doi:10.3390/fi16090329 · Cited by 238
Review on security of federated learning and its application in healthcare
Li, H., Li, C., Wang, J., Yang, A., Ma, Z., Zhang, Z., & Hua, D. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.02.021 · Cited by 202
A comprehensive survey on federated learning techniques for healthcare informatics
Dasaradharami Reddy, K., & Gadekallu, T. R. (2023) · Computational Intelligence and Neuroscience · doi:10.1155/2023/8393990 · Cited by 126
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Metadata
- Authors
- Arikumar, K., Prathiba, S., Alazab, M., Gadekallu, T., Pandya, S., Khan, J., & Moorthy, R.
- Affiliations
- Department of Computer Science and Engineering, St. Joseph’s Institute of Technology, Chennai 600119, India
- Anna University, Chennai (IN)
- Charles Darwin University (AU)
- Vellore Institute of Technology University (IN)
- Symbiosis International University (IN)
- King Saud University (SA)
- Sri Ramachandra Institute of Higher Education and Research (IN)
- Corresponding author
- Gadekallu, Thippa
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2022-02-11
- Volume
- 22
- Issue
- 4
- Pages
- 1377
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC8962969
- OpenAlex
- W4211249696
- Cited by
- 168
- References
- 42
- Citations by year
- 2026: 24 · 2025: 43 · 2024: 45 · 2023: 29 · 2022: 27
- FWCI
- 11.85
- Type
- journal article
- Language
- en
- Status
- Retracted
- Update notices
- retraction (2026-09-21)
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DiVA (Linnaeus University) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- King Saud University (RSP-2021/360, RSP-2021)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- federated learning, wearable devices, smart healthcare, deep reinforcement learning, auto-labeling, unlabeled data, edge computing, bidirectional LSTM, Internet of Things, memory and computational cost reduction
- MeSH terms
- Cloud Computing, Wearable Electronic Devices, Internet of Things, Artificial Intelligence, Delivery of Health Care, Humans
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