ToF Sensor-Based Fall Detection for Elderly Care

Three-dimensional time-of-flight (ToF) sensors are increasingly utilized for human pose and gesture recognition. This paper explores the application of low-resolution 3D ToF sensors for detecting fall events in indoor environments. We present a novel retrospective fall confirmation approach based on XGBoost that integrates fall posture data from distance snapshots and suspected fall trajectories. Our experiment results demonstrate strong detection performance, including improved accuracy and response time, compared to traditional methods, highlighting the efficacy of leveraging the history posture change process from stored sensor data alongside real-time ranging data judgment.