Abstract
Human falls are considered as an important health problem worldwide. Fall detection systems can alert when a fall occurs reducing the time in which a person obtains medical attention. In this regard, there are different approaches to design fall detection systems, such as wearable sensors, ambient sensors, vision devices, and more recently multimodal approaches. However, these systems depend on the types of devices selected for data acquisition, the location in which these devices are placed, and how fall detection is done. Previously, we have created a multimodal dataset namely UP-Fall Detection and we developed a fall detection system. But the latter cannot be applied on realistic conditions due to a lack of proper selection of minimal sensors. In this work, we propose a methodological analysis to determine the minimal number of sensors required for developing an accurate fall detection system, using the UP-Fall Detection dataset. Specifically, we analyze five wearable sensors and two camera viewpoints separately. After that, we combine them in a feature level to evaluate and select the most suitable single or combined sources of information. From this analysis we found that a wearable sensor at the waist and a lateral viewpoint from a camera exhibits 98.72% of accuracy (intra-subject). At the end, we present a case study on the usage of the analysis results to deploy a minimal-sensor based fall detection system which finally reports 87.56% of accuracy (inter-subject).
Highlights
Falls are considered as an important health problem worldwide [1]
5) EVALUATION We evaluated the performance of the different configurations of the fall detection system in terms of four metrics: accuracy (1), precision (2), sensitivity (3) and F-score (4), where TP, TN, FP and FN represent true positive, true negative, false positive and false negative values
In this work, we determined the minimal number of sensors required for developing an accurate fall detection system, using the configuration of the UP-Fall Detection dataset
Summary
Falls are considered as an important health problem worldwide [1]. Fall detection systems can alert when a fall occurs reducing the time in which a person obtains medical attention. Especially elders, often remain laying on the floor worsening the psychological and physical harm caused by the fall. This problem has gained a lot of attention from the research community since the phenomenon of population ageing is occurring around the world [2]. Fall detection systems can help to provide rapid provision of assistance when fall occurs [3]. Fall detection systems have been developed with wearable sensors, ambient sensors and vision devices [5] using one of these three approaches or, recently, a combination of
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