What Is Next in Wearable Computing for Mental and Physical Health Monitoring?

Dear Colleagues,

Rapid advancements in wearable computing have revolutionized the fields of mental and physical health monitoring, enabling for continuous and personalized data collection. As the adoption of wearable devices grows, there is a pressing need to address several key challenges. This Special Issue aims to explore cutting-edge research on privacy-aware deep learning structures, performance improvement using state-of-the-art deep learning architectures, transfer learning techniques, energy-efficient algorithms, optimized duty cycling, addressing real-world issues, and fine-grained labeling in wearable computing for mental and physical health monitoring.

Topics of Interest:

We invite original research contributions on the following (but not limited to) topics:

Privacy-aware deep learning structures: Submissions should focus on novel techniques that ensure data privacy and confidentiality while utilizing deep learning approaches for processing sensitive health-related data. Privacy-preserving methodologies, federated learning, and secure multi-party computation in wearable computing are of particular interest.

Performance improvement with state-of-the-art deep learning architectures: This topic seeks innovative approaches that leverage cutting-edge deep learning architectures, such as transformer networks, capsule networks, and attention mechanisms, to enhance the accuracy, efficiency, and scalability of health monitoring systems.

Transfer learning for wearable computing: Manuscripts exploring transfer learning techniques to adapt pre-trained models from related domains or tasks to enhance the performance and generalization of wearable computing models are encouraged. This can help address the challenge of limited labeled data in health monitoring applications.

Energy-efficient algorithms: Submissions in this area should focus on developing energy-efficient algorithms for wearable devices, enabling longer battery life and reduced power consumption while maintaining performance and data accuracy.

Optimized duty cycling: Research on optimized duty cycling techniques for wearable sensors, aiming to balance data acquisition frequency and power consumption to extend the operational lifespan of wearable devices, is highly relevant.

Issues occurring in the wild: Real-world wearable deployments often face challenges like noisy data, inter-device variability, and user compliance issues. Research focusing on robust solutions to address these challenges in practical scenarios is highly relevant.

Fine-grained labeling: To extract meaningful insights from wearable sensor data, precise and fine-grained labeling is crucial. Research on automatic or semi-automatic techniques for annotating and labeling health-related data is highly relevant to this Special Issue.

Dr. Yekta Said Can
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• deep wearable computing
• transfer learning
• energy efficient wearable computing
• privacy-aware deep learning
• health monitoring