Human Fall Detection in SAGIN Environment Using Ultrasonic Sensors and Hybrid Deep Learning

Fall Detection Systems (FDS) are an integral part in many Ambient Assisted Living (AAL) systems for ensuring the safety of senior citizens, especially in the underserved, isolated, and remote areas where there is unavailability of conventional communication systems. The conventional FDS systems mainly rely on cameras and wearable devices that impose significant challenges like privacy and acceptability. This paper presents a non-invasive and non-intrusive FDS leveraging ultrasonic sensors for fall detection, mitigating the challenges posed by camera systems and wearable devices, resulting into privacy preserving human fall detection. We propose a hybrid deep learning fusion approach that fuses Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), and Bi-directional LSTM (BLSTM), which achieves an accuracy of 98.14% for fall detection from time-series data. The main motivation of this study is to integrate our Fall detection system with the Space-Air-Ground-Integrated Network (SAGIN) framework to facilitate real-time alerts and emergency responses in the remote and isolated areas affected by unreliable communication systems. The integration of the FDS with the SAGIN framework presents a multi-tier processing at three levels, including Ground, Air, and Space. At the Ground level, the edge devices at the local site facilitate initial fall detection with lower latency. At the Air level, the aerial platforms like drones present an extended coverage range and facilitate data relay. And at the Space level, the satellites facilitate global connectivity, data analysis, and management for a longer course of time. Thus, the SAGIN integration with FDS systems ensures precise and real-time fall detection in remote and isolated areas, guaranteeing the availability of the communication networks. The proposed approach reduces the latency with the help of edge computing and showcases a resilient and scalable architecture for emergency response and health monitoring.