Approaches and principles of fall detection for elderly and patient

TLDR

Fall detection for elderly and patients is a key research area driven by the healthcare industry's demand for effective monitoring technologies. This survey aims to classify and analyze existing fall‑detection methods by examining fall characteristics and outlining their approaches and underlying principles. The authors categorize methods into wearable, ambient, and camera‑based approaches, further subdividing each by principle, and evaluate their strengths, weaknesses, and potential improvements.

Abstract

Fall detection for elderly and patient has been an active research topic due to that the healthcare industry has a big demand for products and technology of fall detection. This paper gives a survey of fall detection for elderly and patient, focusing on identifying approaches and principles of the existing fall detection methods. To properly build the classification tree of the methods of fall detection we first study the characteristics of fall. Then according to what sensors and how sensors are used we first divide the methods of fall detection into three approaches: wearable device, ambience device, and camera-based. Further we divide each approach into two to three classes according to the used principles. For each class of algorithm we analyze their merits and demerits. We also give comments on how we can improve some algorithms.