AI RESEARCH
You Don't Need Attention: Gated Convolutional Modeling for Watch-Based Fall Detection
arXiv CS.CV
•
ArXi:2605.20275v1 Announce Type: new Existing deep learning approaches for wearable fall detection systems rely on self-attention mechanisms that impose quadratic computational overhead, distributing weights across all time steps. This global weight distribution impairs the precise localization of the brief impact signatures that characterize falls within short, fixed-length windows.