A Fallen Person Detector with a Privacy-Preserving Edge-AI Camera

Abstract

As the population ages, Ambient-Assisted Living (AAL) environments are increasingly used to support older individuals’ safety and autonomy. In this study, we propose a low-cost, privacy-preserving sensor system integrated with mobile robots to enhance fall detection in AAL environments. We utilized the Luxonis OAKD Edge-AI camera mounted on a mobile robot to detect fallen individuals. The system was trained using YOLOv6 network on the E-FPDS dataset and optimized with a knowledge distillation approach onto the more compact YOLOv5 network, which was deployed on the camera. We evaluated the system’s performance using a custom dataset captured with a robot-mounted camera. We achieved a precision of 96.52%, a recall of 95.10%, and a recognition rate of 15 frames per second. The proposed system enhances the safety and autonomy of older individuals by enabling the rapid detection and response to falls.