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Posture Tracking Using a Machine Learning Algorithm for a Home AAL Environment

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Book cover Intelligent Decision Technologies 2019

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 143))

Abstract

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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Acknowledgements

This research was partially funded by the EU Interreg V-Program “Alpenrhein-Bodensee-Hochrhein”: Project “IBH Living Lab Active and Assisted Living”, grants ABH040, ABH041 and ABH066.

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Authors and Affiliations

  1. HTWG Konstanz, Alfred-Wachtel-Str. 8, 78462, Konstanz, Germany

    Maksim Sandybekov, Clemens Grabow, Maksym Gaiduk & Ralf Seepold

  2. Universidad de Sevilla, Avda. Reina Mercedes s/n, 41012, Seville, Spain

    Maksym Gaiduk

  3. I.M. Sechenov First Moscow State Medical University, 2-4 Bolshaya Pirogovskaya st., 119991, Moscow, Russia

    Ralf Seepold

Authors
  1. Maksim Sandybekov
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  2. Clemens Grabow
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  3. Maksym Gaiduk
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  4. Ralf Seepold
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Corresponding author

Correspondence to Ralf Seepold .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Gdynia Maritime University, Gdynia, Poland

    Ireneusz Czarnowski

  2. Bournemouth University and KES International, Poole, Dorset, UK

    Robert J. Howlett

  3. University of Canberra, Canberra, ACT, Australia

    Lakhmi C. Jain

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Sandybekov, M., Grabow, C., Gaiduk, M., Seepold, R. (2019). Posture Tracking Using a Machine Learning Algorithm for a Home AAL Environment. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2019. Smart Innovation, Systems and Technologies, vol 143. Springer, Singapore. https://doi.org/10.1007/978-981-13-8303-8_31

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