According to recent studies by USA CDC, a notable proportion of elderly individuals experience falls each year, with approximately 20% of these fall events resulting in serious injuries such as fractures or head trauma. Given this statistic, detecting fall events is crucial for individuals who are elderly or are at risk of falls due to medical conditions.

Meanwhile, time-of-flight (ToF) sensors are increasingly utilized for human pose and gesture recognition. This paper explores the application of low-resolution (8*8) ToF sensors for detecting fall events in indoor environments (e.g., bathroom). We present a novel retrospective fall confirmation approach based on XGBoost that integrates fall postures data from distance snapshots and suspected fall trajectories. Our experiment results demonstrate strong detection performance, including accuracy and response time compared to traditional methods, highlighting the efficacy of leveraging history posture change process from stored sensor data alongside real-time ranging data judgement. Moreover, we explore and discuss the possibilities to use the low-resolution ToF sensor to realize the assessment of the seriousness of a fall event, facilitating timely medical assistance.

This work contributes to the research on applying advanced sensors and machine learning to elderly care and healthcare tasks and underscores the capability of low-resolution ToF sensors in monitoring human activity while respecting privacy concerns.