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International Conference on Computational Science and Its Applications

ICCSA 2019: Computational Science and Its Applications – ICCSA 2019 pp 494–509Cite as

Posture Classification Based on a Spine Shape Monitoring System

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11624))

Abstract

Lower back pain is one of the leading causes for musculoskeletal disability throughout the world. A large percentage of the population suffers from lower back pain at some point in their life. One non-invasive approach to reduce back pain is postural modification which can be learned through training. In this context, wearables are becoming more and more prominent since they are capable of providing feedback about the user’s posture in real-time. Optimal, healthy posture depends on the position (sitting, standing, hinging) the user is in. Meaningful feedback needs to adapt to the current position and, in the best case, identify the position automatically to minimize necessary interactions from the user. In this work, we present results of classifying the positions of users based on the readings of the device. We computed various features and evaluated the performance of K-Nearest Neighbors, Extra Trees, Artificial Neural Networks and AdaBoost for global inter-subject classification as well as for personalized subject-specific classification.

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Notes

  1. 1.

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Author information

Authors and Affiliations

  1. University of Bonn, Bonn, Germany

    Icxa Khandelwal & Andreas Weber

  2. Hochschule Bonn-Rhein Sieg, Sankt Augustin, Germany

    Katharina Stollenwerk

  3. Gokhale Method Institute, Stanford, CA, USA

    Björn Krüger

Authors
  1. Icxa Khandelwal
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  2. Katharina Stollenwerk
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  3. Björn Krüger
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  4. Andreas Weber
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Corresponding author

Correspondence to Icxa Khandelwal .

Editor information

Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  3. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Saint Petersburg State University, Saint Petersburg, Russia

    Vladimir Korkhov

  6. Polytechnic University of Bari, Bari, Italy

    Carmelo Torre

  7. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  8. Monash University, Clayton, VIC, Australia

    David Taniar

  9. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  10. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

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Khandelwal, I., Stollenwerk, K., Krüger, B., Weber, A. (2019). Posture Classification Based on a Spine Shape Monitoring System. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11624. Springer, Cham. https://doi.org/10.1007/978-3-030-24311-1_36

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  • DOI: https://doi.org/10.1007/978-3-030-24311-1_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24310-4

  • Online ISBN: 978-3-030-24311-1

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