Wearable Computing and Activity Recognition
A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "Internet of Things (IoT) and Industrial IoT".
Deadline for manuscript submissions: 31 July 2025 | Viewed by 12
Special Issue Editors
Interests: pervasive computing; human–computer-interaction; IoT
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The convergence of wearable technology and advanced machine learning (ML) techniques has revolutionized the field of human activity recognition (HAR). Wearable devices, equipped with various sensors, provide continuous and detailed data that, when analyzed with sophisticated ML algorithms, can accurately identify and interpret a wide range of human activities. This Special Issue, "Wearable Computing and Activity Recognition", seeks to gather pioneering research and innovative methodologies that leverage wearable technology for HAR, pushing the boundaries of what is possible in this dynamic field.
Human activity recognition is essential for numerous applications, including healthcare, fitness, security, and smart environments. The continuous data flow from wearables necessitates robust ML models to process and make sense of this information in real time. We aim to showcase research that enhances the accuracy, reliability, and practicality of HAR systems using wearable computing. We invite you to submit your latest findings and contribute to advancing this exciting area of research.
Prof. Dr. Yang Gao
Dr. Yincheng Jin
Guest Editors
Manuscript Submission Information
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Keywords
- sensor design and integration
- activity recognition algorithms
- data processing and analysis
- applications in healthcare
- user interfaces and interaction techniques for wearable devices
- data privacy and security in wearable devices
- sports and fitness
- integration of llms with wearable devices for enhanced activity recognition
- conversational agents and virtual assistants in wearables
- multimodal data fusion with llms for improved activity recognition
- lifelong/continual human activity learning
- adaptation and generalization
