Fall detection for elderly care using non-invasive vision-based systems remains an important yet unsolved problem. Driven by strict privacy requirements, inference must run at the edge of the vision sensor, demanding robust, real-time, and always-on perception under tight hardware constraints. To address these challenges, we propose a neuromorphic fall detection system that integrates the Sony IMX636 event-based vision sensor with the Intel Loihi 2 neuromorphic processor via a dedicated FPGA-based interface, leveraging the sparsity of event data together with near-memory asynchronous processing. Using a newly recorded dataset under diverse environmental conditions, we explore the design space of sparse neural networks deployable on a single Loihi 2 chip and analyze the tradeoffs between detection F1 score and computational cost. Notably, on the Pareto front, our LIF-based convolutional SNN with graded spikes achieves the highest computational efficiency, reaching a 55x synaptic operations sparsity for an F1 score of 58%. The LIF with graded spikes shows a gain of 6% in F1 score with 5x less operations compared to binary spikes. Furthermore, our MCUNet feature extractor with patched inference, combined with the S4D state space model, achieves the highest F1 score of 84% with a synaptic operations sparsity of 2x and a total power consumption of 90 mW on Loihi 2. Overall, our smart security camera proof-of-concept highlights the potential of integrating neuromorphic sensing and processing for edge AI applications where latency, energy consumption, and privacy are critical.

翻译：针对老年护理中非侵入式视觉跌倒检测这一重要但尚未解决的研究问题，受严格隐私要求驱动，推理计算必须在视觉传感器边缘侧执行，这要求系统在严苛硬件约束下实现鲁棒、实时且持续在线的感知能力。为应对这些挑战，我们提出一种神经形态跌倒检测系统，通过专用FPGA接口将Sony IMX636事件视觉传感器与Intel Loihi 2神经形态处理器集成，利用事件数据的稀疏特性与近存异步处理优势。基于新采集的多样化环境条件数据集，我们探索了可部署于单颗Loihi 2芯片的稀疏神经网络设计空间，分析检测F1分数与计算开销之间的权衡关系。值得关注的是，在帕累托前沿上，采用分级脉冲的LIF卷积脉冲神经网络（SNN）取得最高计算效率，以55倍突触操作稀疏度实现58%的F1分数。分级脉冲LIF相比二进制脉冲节省5倍计算量，F1分数提升6%。此外，采用分块推理的MCUNet特征提取器结合S4D状态空间模型，在Loihi 2上以2倍突触操作稀疏度和总功耗90mW实现84%的最高F1分数。总体而言，本智能安防摄像头概念验证凸显了神经形态感知与处理集成方案在延迟、能耗及隐私敏感的边缘AI应用中的巨大潜力。