Intelligent Sensing for Mental Health: AI and Unobtrusive Wearables in Diagnosing and Intervening in Depression and Cognitive Disorders

Depression and cognitive disorders present complex challenges in diagnosis and intervention, often relying on subjective assessments or late-stage clinical manifestations. Recent advances in AI-driven multimodal sensing and unobtrusive wearables offer unprecedented opportunities to decode subtle behavioral, physiological, and emotional biomarkers—including those associated with core cognitive processes such as attention, memory, and executive function. For instance, passive data streams from wearables—such as electrodermal activity (EDA), heart rate variability (HRV), actigraphy, and sleep architecture—can reveal dysregulated autonomic nervous system (ANS) function, a hallmark of depression, while also providing proxies for cognitive load and mental fatigue.

Intelligent sensing technologies now enable continuous, objective measurement of foundational cognitive mechanisms. Eye-tracking embedded in smart glasses can capture attentional patterns and visual scanning behaviors; EEG-based wearables monitor neural oscillations associated with working memory and cognitive control; and accelerometers paired with machine learning can quantify psychomotor slowing, a key feature of both depression and cognitive impairment.

In depression, these processes are frequently altered: reduced inhibitory control manifests as perseverative negative thinking, diminished attentional flexibility is observed as rigid thought patterns, and impaired memory consolidation contributes to overgeneralized autobiographical recall. In cognitive impairment or neurodegenerative conditions, core processes such as memory, attention, and executive function decline, detectable through digital biomarkers like reduced vocal recall accuracy, increased eye-tracking distractibility, and slowed digital trail-making performance.

These AI-enhanced tools also support ecological validity by translating traditional cognitive assessment from controlled laboratory settings into real-world and clinical contexts. For example, smart home systems equipped with ambient sensors can monitor activities of daily living, detecting subtle declines in functional cognition through behavior patterns like meal preparation efficiency or nocturnal wandering. Wearable devices can administer micro-prompts for in-the-moment cognitive tests (e.g., digital n-back or Stroop tasks), capturing real-time fluctuations in performance influenced by context, fatigue, or medication. VR-based paradigms simulate everyday challenges—such as grocery shopping or financial planning—to assess decision-making, planning, and prospective memory in an engaging yet controlled manner.

Furthermore, AI-driven sensing and wearable technologies enable personalized interventions for depression and cognitive impairment by dynamically adjusting digital therapies in real time based on physiological and behavioral data. For example, they can deliver adaptive cognitive training or just-in-time mindfulness prompts when detecting biomarkers of emotional distress or cognitive overload. These closed-loop systems provide tailored, context-aware support that promotes symptom regulation and functional recovery.

However, key gaps remain, including the validation of multimodal digital phenotypes against gold-standard neuropsychological tests (e.g., MoCA, HAM-D) and the development of personalized AI interventions that adapt to individual symptom trajectories. This Special Issue seeks to bridge these gaps by integrating computational psychiatry with wearable neurotechnology, fostering a new era of objective, scalable mental health monitoring and precision therapeutics.

The goal of this Research Topic is to advance research on AI-driven and wearable-based solutions for the early detection, continuous monitoring, and personalized intervention of depression and cognitive disorders. We aim to foster interdisciplinary collaborations that validate digital biomarkers against clinical standards, optimize adaptive AI interventions, and translate passive sensing data into actionable insights for precision mental healthcare. At the same time, we welcome contributions that leverage advanced AI-enabled sensing to uncover novel cognitive mechanisms underlying these disorders, refine cognitive theory, and bridge the gap between laboratory findings and real-world cognition.

The scope of this special issue includes, but is not limited to:
1. Multimodal biomarkers for depression and cognitive disorders.
2. Decoding emotional states via wearables and sleep monitoring technology.
3. Voice and facial screening for depression and cognitive decline.
4. AR/VR/MR-based interactive system for depression and cognitive disorders.
5. AI interventions for emotional and cognitive support.
6. Intervention approaches for cognitive and emotional decline for the elderly.
7. IoT-enabled smart home cues for depression and cognitive support.
8. Longitudinal wearable monitoring of depression and cognitive disorders.
9. Predictive models of depression and cognitive disorders.
10. Benchmarking AI tools against gold-standard assessments.

Article types and fees

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• Brief Research Report
• Case Report
• Clinical Trial
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
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• General Commentary
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Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Registered Report
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Artificial Intelligence (AI); wearable devices; unobtrusive sensing; depression; cognitive disorders; digital biomarkers; passive monitoring; affective computing; predictive modeling; personalized intervention

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.