Abstract
Falls, especially in elderly persons, are an important health problem worldwide. Reliable fall detection systems can mitigate negative consequences of falls. Among the important challenges and issues reported in literature is the difficulty of fair comparison between fall detection systems and machine learning techniques for detection. In this paper, we present UP-Fall Detection Dataset. The dataset comprises raw and feature sets retrieved from 17 healthy young individuals without any impairment that performed 11 activities and falls, with three attempts each. The dataset also summarizes more than 850 GB of information from wearable sensors, ambient sensors and vision devices. Two experimental use cases were shown. The aim of our dataset is to help human activity recognition and machine learning research communities to fairly compare their fall detection solutions. It also provides many experimental possibilities for the signal recognition, vision, and machine learning community.
Highlights
According to the World Health Organization (WHO), falls are, globally, the second leading cause of unintentional injury and death
We adopted the activity recognition chain (ARC) approach [34] to develop the workflow of the fall detection system aiming to test the case scenarios described below
It reports the best performance based on the F1 -score obtained from the combination modality-window size
Summary
According to the World Health Organization (WHO), falls are, globally, the second leading cause of unintentional injury and death. Falls frequently cause functional dependencies in elderly. “Approximately 28–35% of people aged of 65 and over fall each year increasing to 32–42% for those over 70 years of age” [1]. The incidence of falls varies in different countries and is less frequent in developed countries [2]. In Mexico, 33.5% of the elderly over 60 years of age suffered at least one fall in the year prior to the interview [3]. Fall prevalence increases with age globally and is considered an important health problem
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