ArticleSensors (Basel, Switzerland)2022
Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning.
Article 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 12 papers.
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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.
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Who cites it
12 citing papers in PubMed, 39 citations in OpenAlex.
- Functional near-infrared spectroscopy: Systematic mapping of abnormal brain function features in neurological disorders.Neural regeneration research · 2026Article
- Identifying neuromuscular and mental fatigue in elite youth table tennis players using machine learning.Scientific reports · 2026Article
- Gravity-awareness: a deep learning and LLM framework for predicting human adaptation to altered gravitational environments.Frontiers in psychology · 2026Article
- The Impact of Minimally Invasive Surgical Modality and Task Complexity on Cognitive Workload: An fNIRS Study.Brain sciences · 2025Article
- Article
- The state of the art in assessing mental fatigue in the cockpit using head-worn sensing technology.Frontiers in neuroergonomics · 2025Review
- Article
- Real-time monitoring of military health and readiness: a perspective on future research.Frontiers in digital health · 2025Article
- Impact of CPAP Therapy on Cognition and Fatigue in Patients with Moderate to Severe Sleep Apnea: A Longitudinal Observational Study.Clocks & sleep · 2024Article
- Artificial Intelligence-Driven Approaches to Managing Surgeon Fatigue and Improving Performance.Cureus · 2024Article
- Novel Feature Generation for Classification of Motor Activity from Functional Near-Infrared Spectroscopy Signals Using Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- State-of-the-Art on Brain-Computer Interface Technology.Sensors (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Wearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain-Computer Interfaces (BCI) allows for unobtrusively monitoring one's cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact performance and attention, among other capabilities. The monitoring of this state will be applied in real learning settings to detect and advise on effective break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices were employed to build a BCI to automatically detect the state of cognitive fatigue using machine learning algorithms. An experimental procedure was developed to effectively induce cognitive fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task and a concentration task. Machine learning models were user-tuned to account for the individual dynamics of each participant, reaching classification accuracy scores of around 70.91 ± 13.67 %. We concluded that, although effective for some subjects, the methodology needs to be individually validated before being applied. Moreover, time on task was not a particularly determining factor for classification, i.e., to induce cognitive fatigue. Further research will include other physiological signals and human-computer interaction variables.
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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.