Abstract
A fall of an elderly person often leads to serious injuries or even death. Many falls occur in the home environment and remain unrecognized. Therefore, a reliable fall detection is absolutely necessary for a fast help. Wrist-worn accelerometer based fall detection systems are developed, but the accuracy and precision are not standardized, comparable, or sometimes even known. In this work, we present an overview about existing public databases with sensor based fall datasets and harmonize existing wrist-worn datasets for a broader and robust evaluation. Furthermore, we are analyzing the current possible recognition rate of fall detection using deep learning algorithms for mobile and embedded systems. The presented results and databases can be used for further research and optimizations in order to increase the recognition rate to enhance the independent life of the elderly. Furthermore, we give an outlook for a convenient application and wrist device.
Highlights
IntroductionDepending on the health condition of the elderly, almost 10 percent of the people who fall will suffer from serious injuries, or might even die directly after a fall if no intermediate help is available [1]
The independent life of an elderly person can be changed drastically after a fall
Regarding the Long Short-Term Memory (LSTM)-based neural network, the single-axis version shows a comparable performance with respect to their Convolutional neural networks (CNN) counterparts
Summary
Depending on the health condition of the elderly, almost 10 percent of the people who fall will suffer from serious injuries, or might even die directly after a fall if no intermediate help is available [1]. One common approach to fall detection is using wrist worn detection systems that are measuring acceleration forces. These wrist devices are gaining more and more acceptance across the population and becoming increasingly powerful in terms of computational performance that the usage of artificial intelligence is reasonable. The evaluation of mobile fall detection systems is highly sophisticated because live data from falls of elderly people are rare.
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