Deep Learning for Human Activity Recognition: Advances and Applications
A Special Issue of Applied Sciences (ISSN 2076-3417) belonging to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 January 2027 | Viewed by 1476
Editors
Interests: convolutional neural networks; human activity recognition; recurrent neural networks; telematics sensors; driving behaviour
Interests: human activity recognition; computer vision; machine/deep learning and intelligent systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent breakthroughs in deep learning have decisively transformed Human Activity Recognition (HAR), enabling machines to perceive, interpret, and anticipate human behavior with unprecedented accuracy and robustness. This Special Issue seeks high-quality contributions that push the methodological and application boundaries of HAR. Topics of interest include (but are not limited to) novel network architectures (e.g., graph neural networks, transformer-based models); efficient multimodal fusion of visual, inertial, and ambient-sensor data; self-supervised and few-shot learning strategies for data-scarce scenarios; on-device inference and model compression for edge deployment; privacy-preserving and federated learning frameworks; and trustworthy AI techniques addressing interpretability, fairness, and robustness. Beyond methodological innovations, we particularly welcome application-oriented studies in healthcare monitoring, sports analytics, smart environments, human–robot interaction, autonomous driving, and security. Submissions should present solid experimental validation, and open datasets or code where possible, and clearly articulate how the work advances the state of the art and broadens the practical impact of HAR.
Dr. Ioannis Vernikos
Dr. Evaggelos Spyrou
Guest Editors
Manuscript Submission Information
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Keywords
- deep learning
- human activity recognition
- multimodal fusion
- graph neural network
- transformer
- self-supervised learning
- few-shot learning
- edge AI
- federated learning
- privacy-preserving HAR
- interpretable AI
- smart healthcare
- human–robot interaction
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