ReviewChronic diseases and translational medicine2022
A comprehensive review of approaches to detect fatigue using machine learning techniques.
Review in Chronic diseases and translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Human-inspired time-series health evaluation with an adaptive multimodal electronic skin.Science advances · 2026Article
- Structure-aware fatigue modeling in foot deformities: A digital health framework for tissue-specific running injury risk prediction using multi-modal data.PLOS digital health · 2026Article
- Enhancing hospital workforce planning, scheduling, and performance evaluation through an AI-driven human resource management system.Scientific reports · 2026Article
- Suitability of just-in-time adaptive intervention in post-COVID-19-related symptoms: A systematic scoping review.PLOS digital health · 2025Article
- Artificial Intelligence-Driven Approaches to Managing Surgeon Fatigue and Improving Performance.Cureus · 2024Article
- Exploring the Applicability of Physiological Monitoring to Manage Physical Fatigue in Firefighters.Sensors (Basel, Switzerland) · 2023Article
- A Multimodal Feature Fusion Framework for Sleep-Deprived Fatigue Detection to Prevent Accidents.Sensors (Basel, Switzerland) · 2023Article
- Machine Learning Approach to Model Physical Fatigue during Incremental Exercise among Firefighters.Sensors (Basel, Switzerland) · 2022Article
- A Deep Learning Approach to Classify Sitting and Sleep History from Raw Accelerometry Data during Simulated Driving.Sensors (Basel, Switzerland) · 2022Article
- Neuropsychological Predictors of Fatigue in Post-COVID Syndrome.Journal of clinical medicine · 2022Article
- A comprehensive review of approaches to detect fatigue using machine learning techniques.Chronic diseases and translational medicine · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the past decades, there have been numerous advancements in the field of technology. This has led to many scientific breakthroughs in the field of medical sciences. In this, rapidly transforming world we are having a difficult time and the problem of fatigue is becoming prevalent. So, this study aimed to understand what is fatigue, its repercussions, and techniques to detect it using machine learning (ML) approaches. This paper introduces, discusses methods and recent advancements in the field of fatigue detection. Further, we categorized the methods that can be used to detect fatigue into four diverse groups, that is, mathematical models, rule-based implementation, ML, and deep learning. This study presents, compares, and contrasts various algorithms to find the most promising approach that can be used for the detection of fatigue. Finally, the paper discusses the possible areas for improvement.
Indexed as
Identifiers
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.