Fall detection based on depth-data in practice

Abstract

Falls are a leading cause of accidental deaths among the elderly population. The aim of fall detection is to ensure quick help for fall victims by automatically informing caretakers. We present a fall detection method based on depth-data that is able to detect falls reliably while having a low false alarm rate-not only under experimental conditions but also in practice. We emphasize person detection and tracking and utilize features that are invariant with respect to the sensor position, robust to partial occlusions, and computationally efficient. Our method operates in real-time on inexpensive hardware and enables fall detection systems that are unobtrusive, economic, and plug and play. We evaluate our method on an extensive dataset and demonstrate its capability under practical conditions in a long-term evaluation.