Abstract
People’s life expectancy is increasing, resulting in a growing elderly population. That population is subject to dependency issues, falls being a problematic one due to the associated health complications. Some projects are trying to enhance the independence of elderly people by monitoring their status, typically by means of wearable devices. These devices often feature Machine Learning (ML) algorithms for fall detection using accelerometers. However, the software deployed often lacks reliable data for the models’ training. To overcome such an issue, we have developed a publicly available fall simulator capable of recreating accelerometer fall samples of two of the most common types of falls: syncope and forward. Those simulated samples are like real falls recorded using real accelerometers in order to use them later as input for ML applications. To validate our approach, we have used different classifiers over both simulated falls and data from two public datasets based on real data. Our tests show that the fall simulator achieves a high accuracy for generating accelerometer data from a fall, allowing to create larger datasets for training fall detection software in wearable devices.
Highlights
According to the World Health Organization (WHO), the number of people over 60 years of age is set to double by 2050
In order to contribute to the research efforts of building reliable datasets suitable for Machine Learning (ML) purposes, we propose the Intelligent Systems Group (ISG) Fall Simulator, a fall simulator for the generation of falls in a realistic way
In order to properly evaluate the simulator’s performance, we have compared the obtained accelerometer records in our simulated falls to the ones created using real-life subjects from previously validated works, namely, the UMA-Fall dataset [30], which contains accelerometer records generated by a group of 19 experimental subjects that emulated a set of predetermined Activities of Daily Life (ADL) and several types of falls; and the UP-Fall dataset [12], which contains records of 11 different activities performed by 17 different subjects, five of those were different kinds of falls
Summary
According to the World Health Organization (WHO), the number of people over 60 years of age is set to double by 2050 This ageing of the world population will require major societal changes to improve life quality in the elderly [1]. Care of such population from the social perspective will require a significant effort, from the point of view of the administration, and from the family, as ageing is usually related to dependency. In this sense, one concern is how to take care of seniors living alone. It is important to supervise the health condition of independent people, by monitoring their environment for being able to respond to unexpected conditions
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