Wrist-worn Accelerometer based Fall Detection for Embedded Systems and IoT devices using Deep Learning Algorithms

With increasing age, elderly persons are falling more often. While a third of people over 65 years are falling once a year, hospitalized people over 80 years are falling multiple times per year. A reliable fall detection is absolutely necessary for a fast help. Therefore, wristworn accelerometer based fall detection systems are developed but the accuracy and precision is not standardized, comparable or sometimes even known. In this paper, 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.