3 citations · 3 across the 3 of their papers we have counts for
5 papers
You Don't Need Attention: Gated Convolutional Modeling for Watch-Based Fall Detection
Sana Alamgeer, Ronish Kumar, Awatif Yasmin +2
Existing deep learning approaches for wearable fall detection systems rely on self-attention mechanisms that impose quadratic computational overhead, distributing weights across al…
Personalized Fall Detection by Balancing Data with Selective Feedback Using Contrastive Learning
Awatif Yasmin, Tarek Mahmud, Sana Alamgeer +1
Personalized fall detection models can significantly improve accuracy by adapting to individual motion patterns, yet their effectiveness is often limited by the scarcity of real-wo…
TransConv-DDPM: Enhanced Diffusion Model for Generating Time-Series Data in Healthcare
Md Shahriar Kabir, Sana Alamgeer, Minakshi Debnath +1
The lack of real-world data in clinical fields poses a major obstacle in training effective AI models for diagnostic and preventive tools in medicine. Generative AI has shown promi…
Deep Hybrid Model for Region of Interest Detection in Omnidirectional Videos
Sana Alamgeer, Mylene Farias, Marcelo Carvalho
The main goal of the project is to design a new model that predicts regions of interest in 360 videos. The region of interest (ROI) plays an important role in 360$^{\circ…
AI-Generated Fall Data: Assessing LLMs and Diffusion Model for Wearable Fall Detection
Sana Alamgeer, Yasine Souissi, Anne H. H. Ngu
Training fall detection systems is challenging due to the scarcity of real-world fall data, particularly from elderly individuals. To address this, we explore the potential of Larg…