Recent advancements in machine learning (ML) and artificial intelligence (AI) have paved the way for novel applications in wearable electronics, significantly enhancing personalized healthcare, fitness monitoring, and human-machine interaction. With the rapid advancement of wearable technology and machine learning, monitoring human physiological and psychological states has become increasingly feasible and effective. One critical area of application is the tracking and management of operator fatigue—a pervasive issue with significant implications for performance, safety, and well-being across various professions. Fatigue is a multifaceted condition influenced by physical, cognitive, and emotional factors, and its impact can be catastrophic in high-stakes environments such as aviation, surgery, sports, and transportation.
This Research Topic aims to consolidate recent advances in deep learning solutions to fatigue detection and monitoring in the context of wearable electronics. The Research Topic will address current challenges, showcase innovative solutions, and highlight interdisciplinary approaches to improving real-time fatigue detection and management. As such, this collection welcomes transformative applications, novel algorithms, efficient circuits, systems, and embedded deep-learning solutions.
Our goal is to provide researchers, engineers, and practitioners with a platform to share state-of-the-art findings and solutions in wearable fatigue monitoring. It will also foster cross-disciplinary collaboration, bridging the gap between electronics, deep learning, physiology, and human factors. By narrowing the focus to operator fatigue tracking, the collection will cater to a targeted audience seeking practical and theoretical insights into addressing this critical challenge.
Key topics include but are not limited to:
• Development of wearable sensors for physiological and behavioral fatigue indicators (e.g., heart rate variability, eye-tracking, EEG, EMG, and posture analysis).
• Machine learning algorithms for multi-modal data fusion and fatigue prediction.
Applications in specific fields such as:
• Aviation: Ensuring pilot alertness and decision-making capabilities.
• Healthcare: Tracking surgeon fatigue to enhance patient outcomes and minimize errors.
• Security: Preventing lapses in judgment for everyday security decisions due to user fatigue.
• Sports: Monitoring fatigue to optimize performance and reduce injury risks for athletes.
• Transportation: Detecting and mitigating fatigue among truck drivers and other vehicle operators.
• Other safety-critical environments: Fatigue monitoring, detection, and prevention in other safety-critical environments such as on the factory floor, or in military settings.
• Design and evaluation of user-friendly, energy-efficient wearable devices for continuous fatigue monitoring.
• Ethical, privacy, and data security considerations in wearable fatigue tracking systems.
• Machine learning models optimized for low-power wearable devices
• Development of robust, privacy-preserving models for wearable systems
• Cross-disciplinary approaches integrating ML, IoT, and wearable sensors
• Novel embedded systems enabling ML applications in wearable electronics.
Keywords: machine learning, wearables, fatigue, physiological monitoring, human-machine interaction, occupational health
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
