Abstract
Abstract Elderly care is becoming a relevant issue with the increase of population ageing. Fall injuries, with their impact on social and healthcare cost, represent one of the biggest concerns over the years. Researchers are focusing their attention on several fall-detection algorithms. In this paper, we present a deep-learning solution for automatic fall detection from RGB videos. The proposed approach achieved a mean recall of 0.916, prompting the possibility of translating this approach in the actual monitoring practice. Moreover to enable the scientific community making research on the topic the dataset used for our experiments will be released. This could enhance elderly people safety and quality of life, attenuating risks during elderly activities of daily living with reduced healthcare costs as a final result.
Highlights
Fall-related injury represents a major social issue
Results were evaluated with the area under the ROC curves (AUC) and the confusion matrices
In this paper, we proposed a deep-learning method for automatic fall detection from RGB videos
Summary
Fall frequency drastically increases among elderly: the 28–35% of people over 65 fall each year (World Health Organization, Ageing and Life Course Unit, 2008). This percentage reaches the 32–42% for people over 70. Automatic fall detection is becoming a crucial task to increase safety of elderly people, especially when they live alone. According to (Mubashir, Shao, & Seed, 2013), fall detection approaches are based on: wearable, ambience and vision devices (Mehmood, Nadeem, Ashraf, Alghamdi, & Siddiqui, 2019; Liciotti, Bernardini, Romeo, & Frontoni, in press; Shojaei-Hashemi, Nasiopolous, Little, & Pourazad, 2018; Wang, Chen, Zhou, Sun, & Dong, 2016). A possible solution would be to monitor, through RGB camera, home environment and develop a fall-detection algorithm that could alert caregivers once falls occur
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