Low-Cost Neuromorphic Fall Detection Using Synthetic Event Data and Hybrid SNNs
For edge computing applications requiring low-power fall detection, this work provides a practical method to leverage neuromorphic hardware without specialized event cameras.
This work develops hybrid SNN-CNN models for fall detection using synthetic event data from smartphone videos, achieving significant efficiency gains without sacrificing accuracy compared to traditional models.
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.