Manuscript Topics

The healthcare industry has radically changed as the IoT have recalibrated endless applications within the structure. IoMT sensors such as wearables, moodables, ingestible sensors and trackers have the potential to provide a proactive approach to personalized healthcare. The emergence of smartphone and wearable devices has begun to show promise for the remote assessment of psychological well-being which includes both mental and emotional health. While the terms mental health and emotional health are sometimes used interchangeably, they are distinct. Mental health involves cognitive thinking and harnessing one’s attention to stay focused, which includes processing information, storing it in memory, and understanding this new information. Emotional health, on the other hand, refers to the ability to express feelings which are based upon the information processed by an individual. Affective health is a psycho-physiological process, triggered by an external (event, object) or internal (memory) stimuli and captured through verbal (voice, tone, text) and non-verbal (facial expressions, body language, physiological indicators) observed manifestations. Affect can be expressed as a tone of voice, a smile, a frown, a laugh, a smirk, a tear, pressed lips, a crinkled forehead, a scrunched nose, furrowed eyebrows, an eye gaze, and changes in heart rate or blood pressure. These modes of expressions or observed manifestations are referred to as biomarkers.

Affect recognition is accurate when it combines different signals (biomarkers) from users and information about the user's context and situation. The biomarkers can now be measured using smart sensors in wearable devices like fitness watches, or a chest wearable or with the help of implants such as pacemakers and might be beneficial for predicting the presence of an early affective disorder that is not yet clinically evident. Affective state recognition from wearable biosensors can complement context-aware recommendation, mood stabilization, and stress and depression management, for mental and emotional well-being. The recent advances in smart sensors have significantly improved access to wearable trackers that can measure the physiological changes occurring in the body in real-time and can help to automate the affective health monitoring by detecting psychological issues such as stress disorder, mood disorder, depression, anxiety, borderline personality disorder and insomnia, amongst others.

This special issue is envisioned to fetch advances in accurate affective state detection and mental health monitoring with smart sensors with state-of-the-art pattern recognition and computer vision algorithms for real-time applications. We seek contributions on innovative methods in the field of smart sensors for personalized affective health monitoring using artificial intelligence including pattern recognition & computer vision models.

Original research articles and review papers are sought in areas including, but not limited to:
• New biomarker sensors theory, design, and modelling
• Bio-signal processing methods
• Wearable and implantable sensors-based health monitoring
• Personalized mental health monitoring using smart sensors.
• Signal collection and feature extraction.
• Time latency in intelligent health monitoring.
Privacy preservation in medical sensor data.
• Emotional stress identification using digital sensors.
• Affection state detection in real-time using digital sensors.
• Co-relation identification between mental and emotional health.
• Affective Computing using multimodal sensing.
• Novel data analytics techniques for sensors data.
• Digital sensors empowered emotion identification for Human Computer Interaction.
• Cost-effective models for intelligent health monitoring.
• Explainable AI for Affective Health monitoring

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