Wearable Electronics and Machine Learning Converge to Catch Operator Fatigue Before It Turns Dangerous

Fatigue has long been one of the most stubborn hazards in high-stakes work environments. A surgeon at the end of a long operation, a pilot descending through turbulence on the fourth leg of a shift, a nurse finishing a triple consecutive night rotation—all of them experience a gradual erosion of decision-making, attention, and motor control that neither they nor their colleagues can reliably measure in the moment. A new collection of research, assembled as a Research Topic in the journal Frontiers in Electronics under the title “Machine learning for operator fatigue detection and monitoring with wearable electronics,” argues that this era of guesswork is coming to an end. Edited by N. Sertac Artan of the New York Institute of Technology and Ihsan Cicek of Gebze Technical University, the collection brings together studies that combine body-worn sensors with machine learning to turn fatigue from an invisible, subjective state into a continuous, objective, and personalized measurement.

The scale of the problem is difficult to overstate. Fatigue is pervasive across healthcare, aviation, industrial operations, and virtually any domain in which humans supervise complex machinery or make consequential decisions under time pressure. Crucially, the editorial emphasizes that fatigue is not one thing but many. It has physiological, neural, and psychological components, and it can present as an acute, fluctuating, prolonged, or chronic condition, with burnout representing the extreme end of the chronic spectrum. A tired pilot twenty minutes before landing is not experiencing the same phenomenon as a healthcare worker suffering emotional exhaustion after months of sustained overload, even though both are colloquially described as fatigued. This heterogeneity is precisely why the field has struggled for decades to establish a single reliable metric, and it frames the central scientific question running through the collection: can machine learning disentangle these overlapping signals into actionable, individual-specific assessments?

The answer emerging from the contributing studies is a cautious but encouraging yes, built on a foundation of physiological monitoring. Heart rate variability, or HRV—the beat-to-beat variation in heart rhythm governed by the autonomic nervous system—features prominently across nearly every contribution. In one study, Aboagye and colleagues combined HRV with activity and sleep metrics drawn from wearable devices to predict fatigue in individuals suffering from post-COVID fatigue, a population in which the condition is often severe, poorly understood, and difficult to quantify clinically. Their findings reinforce the idea that the dynamics of the autonomic nervous system, long implicated in stress and recovery research, carry sufficient information about fatigue to support predictive models rather than merely retrospective detection.

The same physiological family of signals proves versatile across very different operators. Dei Rossi and colleagues used HRV-based metrics to predict burnout risk, while Yuan and colleagues applied HRV features to a strikingly different problem: classifying the mental workload of pilots during different flight phases. The fact that comparable cardiac data can illuminate both the slow-burning psychological erosion of burnout and the moment-to-moment cognitive demands of flying an aircraft illustrates both the power and the challenge of these measurements. The signals are rich, but the interpretations are context-dependent, and the machine learning models must be designed with that context in mind.

Not all contributions rely on the heart. Schneider and colleagues measured cognitive workload using the P300 component of event-related potentials, a well-characterized electrical signature recorded by electroencephalography that reflects how the brain evaluates surprising or salient stimuli. By tracking how this marker responds in real working conditions, they demonstrated improvements in efficiency and ergonomic outcomes associated with a smart surgical lighting system—an example of fatigue and workload research feeding directly back into equipment design for operating rooms. Meanwhile, Abdollahpour and Artan took a different neural approach, using functional near-infrared spectroscopy, or fNIRS, which measures brain hemodynamic activity through near-infrared light passing harmlessly through the skull. Rather than treating fatigue as a static label, they modeled it as an evolving brain network, tracing how patterns of neural connectivity reorganize as fatigue progresses through successive stages.

Beyond the choice of sensors, the collection reveals a set of methodological innovations that may matter just as much as the hardware. Standard classification and regression frameworks struggle with fatigue data because the condition is complex, variable, and graded rather than neatly categorical. Dei Rossi and colleagues made a compelling case for ordinal representations—models that respect the ordered nature of severity—by reframing burnout prediction as a pairwise ranking problem. Instead of asking a classifier to sort people into fixed boxes, the model learns to say which of two individuals is more exhausted, an approach that aligned more closely with actual fatigue severity than categorical or continuous alternatives.

Time itself emerges as a central ingredient in these models. Aboagye and colleagues used physiological data collected during a single day to predict fatigue levels on the following day, effectively turning wearables into a forecast system that could warn individuals and their care teams before exhaustion arrives. Yuan and colleagues analyzed how dynamic workloads transition across task phases, capturing the fact that a pilot’s cognitive state during approach is shaped by everything that came before. Abdollahpour and Artan’s evolving brain networks add a further temporal dimension, treating fatigue as a process unfolding over minutes and hours rather than a snapshot. In parallel, the studies showcase a range of algorithmic tools: ensemble and tree-based methods such as XGBoost appear in multiple contributions, prized for their robustness on tabular physiological data, while recurrent neural network architectures and graph-based brain connectivity models represent the emerging deep learning frontier, better suited to sequential and relational structure respectively.

Perhaps most importantly, the research is not confined to the laboratory. Yuan and colleagues demonstrated the feasibility of continuous workload modeling in real aviation settings, using HRV measurements to assess pilots’ cognitive states in an environment where instrumentation, safety regulations, and operational stress all complicate data collection. Schneider and colleagues deployed their approach in the demanding context of surgical lighting, and Aboagye and colleagues predicted daily fatigue levels in individuals with post-COVID fatigue, a clinical population whose needs are growing worldwide. These deployments suggest that the technology is approaching maturity at the system level: sensors comfortable enough to wear all day, algorithms robust enough to run continuously, and outputs specific enough to inform real decisions about rest, scheduling, and workload distribution.

Yet the editorial is refreshingly candid about the obstacles that remain. Generalizability is the dominant concern: the majority of fatigue studies rely on relatively small, domain-specific datasets that often fail to transfer across populations and operational settings. A model trained on healthy young pilots cannot simply be redeployed for post-COVID patients or night-shift nurses. Compounding this is the absence of widely accepted ground truths—there is no gold-standard fatigue measurement to train against, in part because fatigue manifests in multiple forms and arises from diverse physiological, cognitive, and psychological processes. Dei Rossi and colleagues showed that higher-level constructs such as emotional exhaustion cannot be easily inferred from physiological signals alone, a sobering reminder that the most psychologically salient aspects of burnout may resist capture by any wrist-worn sensor.

The editorial’s authors argue that the way forward requires two coordinated efforts. First, the field needs a more comprehensive taxonomy of fatigue—one that explicitly distinguishes mental from physiological fatigue, and chronic from acute presentations, so that models and datasets can be compared on common ground. Second, and enabled by the explosion of wearable devices and advances in deep learning, the community must build large, multimodal, publicly available datasets that span the full spectrum of fatigue forms and temporal presentations. If those datasets materialize, the vision sketched by this Research Topic becomes concrete: a future in which an operator’s fatigue is monitored continuously and objectively by unobtrusive electronics, machine learning models flag rising risk before errors occur, and interventions—from schedule changes to environmental adjustments—arrive in time to protect both workers and the people who depend on them. The convergence of wearables and machine learning has made that future plausible; the coming years will determine how quickly it becomes routine.

Subject of Research: Machine learning-based detection and monitoring of operator fatigue using wearable electronics

Article Title: Editorial: Machine learning for operator fatigue detection and monitoring with wearable electronics

Article References: Artan, N. S., & Cicek, I. (2026). Editorial: Machine learning for operator fatigue detection and monitoring with wearable electronics. Frontiers in Electronics, 7, Article 1940900. https://doi.org/10.3389/felec.2026.1940900

Image Credits: AI Generated

DOI: 10.3389/felec.2026.1940900

Keywords: fatigue, wearable electronics, machine learning, heart rate variability, EEG, fNIRS, burnout, mental workload, pilots, post-COVID fatigue, autonomic nervous system, XGBoost