المشروع الصيفي
تخصص الذكاء الاصطناعي · دفعة 2025
WiFi Human Activity Recognition
This project explores how we can recognize human activities (like walking, sitting, or falling) using only WiFi signals, specifically Channel State Information (CSI). When a person moves, it affects how WiFi signals behave — and by learning from these changes, we can try to classify the activity taking place. The main challenge in this field is that most available datasets are small and not diverse, which makes models perform poorly on new users or environments or domains. While current approaches use self-supervised learning (SSL), transfer learning, or few-shot learning to adapt across domains, they still struggle when the source data lacks variety. Our project focuses on using diffusion models to generate synthetic CSI data, aiming to increase dataset diversity and improve model generalization.
المشرف: Muhammad Mubashar
مع أكاديمية كاوست
- دفعة 2025
جامعة الملك عبدالله للعلوم والتقنية (كاوست) · المدرسة الصيفية للذكاء الاصطناعي
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