Hybrid SNN-based Privacy-Preserving Fall Detection using Neuromorphic Sensors

Indoor surveillance is crucial for ensuring the safety and security of occupants within the premises. Only those who are ill or elderly tend to spend the most time at home. The use of indoor surveillance to continuously monitor these people’s security could help in the early detection and avoidance of tragic incidents. Ensuring privacy while achieving this task has led to a recent research focus on protecting privacy in human fall detection. This paper attempts to address the issue of privacy-preserving fall detection by employing the Dynamic Vision Sensor (DVS), which captures intensity changes without compromising individuals’ privacy. This paper introduces a novel event-based dataset named “DVSFall”, incorporating diverse daily living activities (ADL) and simulated falls. Captured from multiple viewpoints using DVS cameras, the dataset encompasses twenty-one participants across varying age groups. To evaluate the dataset, we employed Spiking Neural Networks (SNN) designed to replicate neural activity. Furthermore, we explored a hybrid framework, the 3D-CNN & SNN (NeuCube) approach, for fall detection. Our proposed framework achieved an accuracy of 94.59% with SNN and notably improved to 97.84% using the hybrid approach, as measured against the recorded dataset.