An Explainable Method for Cost-Efficient Multi-View Fall Detection

Human fall detection is a crucial topic to study, since there are a lot of cases of person’s fall at hospitals, homes and retirement homes. In fact, falls are very costly, especially for elderly people and persons with special needs, since they may cause death or serious injuries that require instance medical intervention. In order to prevent further repercussions after this type of accidents, modern automated fall detection methods are presented as a type of effective alerting systems that are widely used for emerging healthcare applications. In this study, we present a multi-view-based fall detection method that runs in real time, using only CPU and consequently it can be deployed in hospitals, retirement homes and cribs without any financial problems related to expensive hardware. Indeed, a light weight human pose estimator has been adopted in order to detect human body key-points from two different views in order to solve the problematic of image depth ambiguity. Then, we extract few explainable features, based on the automatically detected key-points, while being associated to confirmed descriptors of posture and balance biomechanics. The extracted features are thereafter fed into a machine learning classifier in order to predict whether there is a fall or not. The proposed method has been tested on a challenging public dataset, and the preliminary obtained results show its effectiveness compared to other relevant state-of-the-art methods.