Multimodal Sensing and Learning for Wearable Systems: Challenges, Methods, and Applications
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".
Deadline for manuscript submissions: 1 May 2026 | Viewed by 23
Special Issue Editors
Interests: efficient AI (e.g., pruning, distillation, quantization, NAS); multi-modalities learning; pervasive computing; AI for science
Interests: intelligent data analysis for connected health applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Wearable systems are increasingly used in healthcare, fitness, rehabilitation, sports, and industrial applications, driven by advances in sensor technologies, wireless communication, and artificial intelligence. Integrating multiple sensing modalities—such as inertial measurement units (IMUs), physiological sensors, cameras, and microphones—enables richer context understanding, improved recognition accuracy, and more robust performance under diverse conditions. However, multimodal wearable systems face notable challenges, including heterogeneous data alignment, missing modality handling, energy-efficient processing, and privacy protection. This Special Issue invites original research and review articles that explore novel methods, algorithms, architectures, and applications for multimodal wearable systems. Contributions may address sensor fusion techniques, personalized and adaptive models, privacy-preserving approaches, and generative modeling for data augmentation. By gathering interdisciplinary work, this Special Issue aims to accelerate innovation and practical deployment of intelligent, context-aware wearable technologies.
Dr. Jianyuan Ni
Dr. Shuai Zhang
Guest Editors
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Keywords
- multimodal sensing
- wearable systems
- human activity recognition
- sensor fusion
- edge computing
- privacy-preserving learning
- federated learning
- generative modeling
- healthcare applications
- context-aware systems
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