ArticleSensors (Basel, Switzerland)2022
RETRACTED: FL-PMI: Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 27 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 146 citations in OpenAlex.
- Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture.Cell reports. Medicine · 2024Pooled it
- RETRACTED: Arikumar et al. FL-PMI: Federated Learning-Based Person Movement Identification Through Wearable Devices in Smart Healthcare Systems.Sensors (Basel, Switzerland) · 2026Article
- Wearable Sensing for Clinical Physiology Monitoring: Emerging Paradigms.Physiology (Bethesda, Md.) · 2026Review
- Federated Learning Performance Depends on Site Variation in Global HIV Data Consortia.medRxiv : the preprint server for health sciences · 2026Article
- Adaptive ensemble sizing with reinforcement learning for real-time ankle injury detection in wearable sensor systems.BMC sports science, medicine & rehabilitation · 2026Article
- DCEM-TCRCN: an innovative approach to depression detection using wearable IoT devices and deep learning.International journal of computer assisted radiology and surgery · 2025Article
- Enhancing rehabilitation in stroke survivors: a deep learning approach to access upper extremity movement using accelerometry data.Frontiers in artificial intelligence · 2025Article
- Revolutionizing healthcare data analytics with federated learning: A comprehensive survey of applications, systems, and future directions.Computational and structural biotechnology journal · 2025Review
- PSA-FL-CDM: A Novel Federated Learning-Based Consensus Model for Post-Stroke Assessment.Sensors (Basel, Switzerland) · 2024Article
- Age-appropriate design of smart senior care product APP interface based on deep learning.Heliyon · 2024Article
- Swimtrans Net: a multimodal robotic system for swimming action recognition driven via Swin-Transformer.Frontiers in neurorobotics · 2024Article
- Survey of Medical Applications of Federated Learning.Healthcare informatics research · 2024Article
- A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects.Complex & intelligent systems · 2023Article
- A Comprehensive Survey on Federated Learning Techniques for Healthcare Informatics.Computational intelligence and neuroscience · 2023Review
- User-Centered Design Methodologies for the Prototype Development of a Smart Harness and Related System to Provide Haptic Cues to Persons with Parkinson's Disease.Sensors (Basel, Switzerland) · 2022Article
- Leveraging IoT-Aware Technologies and AI Techniques for Real-Time Critical Healthcare Applications.Sensors (Basel, Switzerland) · 2022Review
- A Cluster-Driven Adaptive Training Approach for Federated Learning.Sensors (Basel, Switzerland) · 2022Article
- Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues.Cluster computing · 2022Article
- Deep Unsupervised Domain Adaptation with Time Series Sensor Data: A Survey.Sensors (Basel, Switzerland) · 2022Review
- Using Identity-Based Cryptography as a Foundation for an Effective and Secure Cloud Model for E-Health.Computational intelligence and neuroscience · 2022Article
Corrections and comments
- Retracted
Authors and funding
7 authors at 7 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.