You Don't Need Attention: Gated Convolutional Modeling for Watch-Based Fall Detection

arXiv:2605.20275v1 Announce Type: cross Abstract: 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. To overcome this challenge, we propose Gated-CNN, a lightweight dual-stream architecture that processes accelerometer and gyroscope streams through independent one-dimensional convolutional feature ex

Source: arXiv cs.AI — read the full report at the original publisher.

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