From Fall Detection to Fall Prevention: A Generic Classification of Fall-Related Systems

Falls are a major health problem for the frail community dwelling old people. For more than two decades, falls have been extensively investigated by medical institutions to mitigate their impact (e.g., lack of independence and fear of falling) and minimize their consequences (e.g., cost of hospitalization and so on). However, the problem of elderly falling does not only concern health-professionals but has also drawn the interest of the scientific community. In fact, falls have been the object of many research studies and the purpose of many commercial products from academia and industry. These studies have tackled the problem using fall detection approaches exhausting a variety of sensing methods. Lately, researcher has shifted their efforts to fall prevention where falls might be spotted before they even happen. Despite their restriction to clinical studies, early fall prediction systems have started to emerge. At the same time, current reviews in this field lack a common ground classification. In this context, the main contribution of this paper is to give a comprehensive overview on elderly falls and to propose a generic classification of fall-related systems based on their sensor deployment. An extensive research scheme from fall detection to fall prevention systems have also been conducted based on this common ground classification. Data processing techniques in both fall detection and fall prevention tracks are also highlighted. The objective of this paper is to deliver medical technologists in the field of public health a good position regarding fall-related systems.