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cs.LG updates on arXiv.org

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Low-Cost Neuromorphic Fall Detection Using Synthetic Even...
[Submitted on 17 Jun 2026] · 2026-06-18 · via cs.LG updates on arXiv.org

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Abstract:This work presents the development of hybrid models that integrate spiking neural networks (SNNs) with components of convolutional neural networks (CNNs) to learn from simulated event-based camera data (Dynamic Vision Sensor, DVS) generated from conventional smartphone videos. Aimed primarily at human fall detection, the approach leverages the energy efficiency and spatio-temporal processing capabilities of SNNs by converting video frames into event-based data. The proposed models are evaluated through simulations on multiple datasets, comparing their performance to that of traditional machine learning models. Results demonstrate significant gains in efficiency without sacrificing accuracy, underscoring the potential of combining SNNs and DVS technology for complex tasks in real-world environments.

Submission history

From: Daniel Yunge [view email]
[v1] Wed, 17 Jun 2026 06:21:13 UTC (500 KB)

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