低成本神经形态跌倒检测：使用合成事件数据和混合SNN
Low-Cost Neuromorphic Fall Detection Using Synthetic Event Data and Hybrid SNNs
AI总结：
提出混合SNN-CNN模型，从智能手机视频合成事件相机数据，实现高效准确的跌倒检测。
摘要：
本工作提出了混合模型，将脉冲神经网络（SNN）与卷积神经网络（CNN）组件集成，以从传统智能手机视频生成的模拟事件相机数据（动态视觉传感器，DVS）中学习。主要针对人类跌倒检测，该方法通过将视频帧转换为事件数据，利用SNN的能效和时空处理能力。通过多个数据集上的模拟评估所提出的模型，并将其性能与传统机器学习模型进行比较。结果表明，在不牺牲准确性的情况下显著提高了效率，强调了将SNN和DVS技术结合用于现实环境中复杂任务的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
