Low-Cost Neuromorphic Fall Detection Using Synthetic Event Data and Hybrid SNNs
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Computer Science > Machine Learning
Title:Low-Cost Neuromorphic Fall Detection Using Synthetic Event Data and Hybrid SNNs
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.
| Comments: | 4 pages, 6 figures, presented at ICONS 2025 during the Poster Session, but not published |
| Subjects: | Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2606.18732 [cs.LG] |
| (or arXiv:2606.18732v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.18732
arXiv-issued DOI via DataCite (pending registration)
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