Wearable and Ambient Sensors: The Path from Quantified Self to Digital Twins

Dear Colleagues,

This Special Issue will provide a comprehensive and in-depth exploration of the latest advancements and emerging trends in the field of wearables and ambient sensors and devices. Wearables and ambient sensing technologies have significantly improved our understanding of human behavior, health, and emotions, giving rise to the notion of the “Quantified Self” by incorporating wearable measurements into daily life. Wearable and ambient devices have been used for real-time tracking of physiological, behavioral, and environmental data, allowing individuals and researchers to monitor everything from physical activity and sleep patterns to stress levels and emotional states.

Recent advances in technology are pushing the boundaries even further by enabling the creation of “Digital Twins”—virtual representations of individuals that mirror their physical, physiological, and behavioral states in real time. These digital counterparts open up exciting opportunities, including personalized wellness and healthcare through predictive modeling, early detection of diseases, remote patient monitoring, adaptive therapeutic interventions, and simulation of treatment outcomes. They also hold promise in areas such as mental health monitoring, fitness, rehabilitation, and chronic disease management.

To realize the full potential of Digital Twins, a wide range of enabling technologies must converge. These include next-generation wearable devices such as epidermal and flexible sensors; multi-modal sensors and sensor fusion techniques for integrating diverse data streams (e.g. wearable and ambient data streams); energy harvesting technologies that support long-term, battery-free operation; IoT concepts for real-time data integration; methods of artificial intelligence and machine learning for data analysis; and human–computer interaction for user engagement.

We aim to highlight the cutting-edge advances bringing the concept of Digital Twins closer to practical implementation. This Special Issue’s scope covers a wide range of aspects related to wearable and ambient sensors and underlying technologies such as sensor design, miniaturization, power management, and communication protocols.

This Special Issue will focus on (but is not limited to) the following topics: 

• Advanced Sensor Technologies: Novel materials and fabrication techniques for highly sensitive and accurate sensors, including nano-sensors, epidermal sensors, and flexible/stretchable electronics;
• Wearable Health Monitoring Systems: Applications in continuous health tracking, such as monitoring vital signs including heart rate, blood pressure, respiration, activity, and sleep patterns, and their role in enabling Digital Twins;
• Smart Device Integration: Seamless integration of wearable sensors with ambient and environmental sensors to enable multi-modal measurement, context-aware data collection, and holistic evaluation of the user’s condition;
• Sensor Fusion and Multi-modal Sensing: Combining data from diverse sensing modalities (e.g., physiological, biomechanical, biochemical, environmental) to improve accuracy, robustness, and contextual relevance;
• Energy Harvesting and Management: Innovative power solutions for wearable and ambient devices, including harvesting energy from body heat, motion, solar, or RF signals, as well as ultra-low-power system design;
• Human-Computer Interaction (HCI): Development of intuitive and adaptive user interfaces, gesture and voice recognition, haptic feedback systems, and emotion-aware interaction through wearable and ambient sensors;
• Artificial Intelligence and Machine Learning: AI-driven data processing and modeling approaches for personalized insights, anomaly detection, behavioral modeling, and predictive healthcare applications using data from wearable and ambient sensors;
• Edge and Cloud Computing in Sensing: data processing architectures for real-time analysis, decision support, and scalable Digital Twin implementations;
• Digital Twin Frameworks and Applications: Architectures, platforms, and case studies showcasing how sensor-based data feeds into Digital Twin models for simulation, prediction, and personalization.

Dr. Edward Sazonov
Dr. Masudul Imtiaz
Dr. Sawal Hamid Md Ali
Guest Editors

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• smart devices
• wearable sensors
• sensor technologies
• energy harvesting
• IoT integration