Posture tracking using a machine learning algorithm for a home AAL environment

  • The number of home office workers sitting for many hours is increasing. The sensor chair is tracking users’ sitting behavior which the help of pressure sensors and tries to avoid wrong postures which may cause diseases. The system provides live monitoring of the pressure distribution via web interface, as well as sitting posture prediction in real time. Posture analysis is realized through machine learning algorithm using a decision tree classifier that is compared to a random forest. Data acquisition and aggregation for the learning process happens with a mobile app adding users biometrical data and the taken sitting posture as label. The sensor chair is able to differentiate between an arched back, a neutral posture or a laid back position taken on the chair. The classifier achieves an accuracy of 97.4% on our test set and is comparable to the performance of the random forest with 98.9%.

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Metadaten
Author:Maksim Sandybekov, Clemens Grabow, Maksym GaidukORCiD, Ralf SeepoldORCiDGND
URL:https://doi.org/10.1007/978-981-13-8303-8_31
ISBN:978-981-13-8302-1
ISBN:978-981-13-8303-8
Parent Title (English):Intelligent Decision Technologies 2019, Proceedings of the 11th KES International Conference on Intelligent Decision Technologies (KES-IDT 2019) - Volume 2 ; (Smart Innovation, Systems and Technologies ; 143)
Publisher:Springer
Place of publication:Singapore
Document Type:Article
Language:English
Year of Publication:2019
Release Date:2019/07/11
Tag:AAL; IoT; Posture tracking
Edition:First online: 2 June 2019
First Page:337
Last Page:347
Institutes:Fakultät Informatik
DDC functional group:000 Allgemeines, Informatik, Informationswissenschaft
600 Technik, Medizin, angewandte Wissenschaften
Open Access?:Nein
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)
Licence (English):License LogoLizenzbedingungen Springer