ReviewSensors (Basel, Switzerland)2022
Anomaly Detection Framework for Wearables Data: A Perspective Review on Data Concepts, Data Analysis Algorithms and Prospects.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
23 citing papers in PubMed, 64 citations in OpenAlex.
- Artificial Intelligence-Driven Wearable and Connected Technology for Allergy: Real-Time Monitoring and Predictive Management for Personalized Care.The journal of allergy and clinical immunology. In practice · 2025Review
- Evaporative microclimate interval cooling reduces heat stress in thermal insulating protective clothing.Journal of occupational medicine and toxicology (London, England) · 2025Article
- Wearable technologies to predict and prevent and heart failure hospitalizations: a systematic review.European heart journal. Digital health · 2025Review
- Decoding dynamic brain networks in Parkinson's disease with temporal attention.Scientific reports · 2025Article
- Relapse prediction using wearable data through convolutional autoencoders and clustering for patients with psychotic disorders.Scientific reports · 2025Article
- Cross-device federated unsupervised learning for the detection of anomalies in single-lead electrocardiogram signals.PLOS digital health · 2025Article
- Toward a framework for risk mitigation of potential misuse of artificial intelligence in biomedical research.Nature machine intelligence · 2024Article
- Aligned bodies, united hearts: embodied emotional dynamics of an Islamic ritual.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- [Healthcare 4.0-Medicine in transition].Herz · 2024Review
- Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care.JMIR mHealth and uHealth · 2024Article
- Continuous Monitoring of Heart Rate Variability in Free-Living Conditions Using Wearable Sensors: Exploratory Observational Study.JMIR formative research · 2024Article
- Exploring the Potentials of Wearable Technologies in Managing Vestibular Hypofunction.Bioengineering (Basel, Switzerland) · 2024Review
- Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges.Pharmaceutics · 2024Review
- Detecting anomalies in smart wearables for hypertension: a deep learning mechanism.Frontiers in public health · 2024Article
- Missing data imputation techniques for wireless continuous vital signs monitoring.Journal of clinical monitoring and computing · 2023Article
- Design and Implementation of an Atrial Fibrillation Detection Algorithm on the ARM Cortex-M4 Microcontroller.Sensors (Basel, Switzerland) · 2023Article
- Hyperparameter optimization for cardiovascular disease data-driven prognostic system.Visual computing for industry, biomedicine, and art · 2023Article
- A Disentangled VAE-BiLSTM Model for Heart Rate Anomaly Detection.Bioengineering (Basel, Switzerland) · 2023Article
- Matching science to reality: how to deploy a participant-driven digital brain health platform.Frontiers in dementia · 2023Article
- Use of consumer wearables to monitor and predict pain in patients with sickle cell disease.Frontiers in digital health · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wearable devices use sensors to evaluate physiological parameters, such as the heart rate, pulse rate, number of steps taken, body fat and diet. The continuous monitoring of physiological parameters offers a potential solution to assess personal healthcare. Identifying outliers or anomalies in heart rates and other features can help identify patterns that can play a significant role in understanding the underlying cause of disease states. Since anomalies are present within the vast amount of data generated by wearable device sensors, identifying anomalies requires accurate automated techniques. Given the clinical significance of anomalies and their impact on diagnosis and treatment, a wide range of detection methods have been proposed to detect anomalies. Much of what is reported herein is based on previously published literature. Clinical studies employing wearable devices are also increasing. In this article, we review the nature of the wearables-associated data and the downstream processing methods for detecting anomalies. In addition, we also review supervised and un-supervised techniques as well as semi-supervised methods that overcome the challenges of missing and un-annotated healthcare data.
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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.