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Deep Learning Based Fall Detection Algorithms for Embedded Systems, Smartwatches, and IoT Devices Using Accelerometers

 
: Kraft, Dimitri; Srinivasan, Karthik; Bieber, Gerald

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Volltext ()

Technologies 8 (2020), Nr.4, Art. 72, 17 S.
ISSN: 2227-7080
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IGD ()
fall detection; accelerometer; deep learning; neural networks; wrist; Internet of Things (IoT); Lead Topic: Individual Health; Research Line: Human computer interaction (HCI)

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.

: http://publica.fraunhofer.de/dokumente/N-633643.html