用模拟事件数据训练混合神经网络，实现低功耗跌倒检测
Low-Cost Neuromorphic Fall Detection Using Synthetic Event Data and Hybrid SNNs
- 将手机视频转为事件数据，融合SNN与CNN构建混合模型
- 在多个数据集上验证，效率显著提升且精度不下降
- 适合边缘设备部署，尤其适用于低功耗智能监护场景
本文提出一种融合脉冲神经网络（SNN）与卷积神经网络（CNN）的混合模型，利用从传统手机视频生成的仿真事件数据（动态视觉传感器，DVS）进行学习。该方法针对人体跌倒检测任务，通过将视频帧转换为事件数据，充分发挥SNN在能效和时空处理方面的优势。模型在多个数据集上通过仿真评估，性能对比传统机器学习模型，结果显示在不牺牲准确率的前提下实现了显著的能效提升，验证了SNN与DVS技术结合在真实环境复杂任务中的潜力。
原文摘要 · Abstract (English)
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.
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