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

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


Association for Computing Machinery -ACM-; National Science Foundation -NSF-:
PETRA 2020, 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments. Conference Proceedings : June 30 - July 3, 2020, Virtual Conference
New York: ACM, 2020
ISBN: 978-1-4503-7773-7
Art. 48, 10 pp.
International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) <13, 2020, Online>
International Workshop on Wearable Systems and Applications for Smart Healthcare (HealthWear) <1, 2020, Online>
Conference Paper
Fraunhofer IGD ()
Lead Topic: Individual Health; Research Line: Human computer interaction (HCI); artificial intelligence (AI); fall detection; mobile devices; pattern recognition

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.