Posture Classification based on a Spine Shape Monitoring System

Icxa Khandelwal, Katharina Stollenwerk, Björn Krüger, and Andreas Weber
In proceedings of Computational Science and Its Applications -- ICCSA 2019, 2019
 

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 noninvasive approach to reduce back pain is postural modi cation 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 Gokhale SpineTracker device. We computed various features and evaluated the performance of K-Nearest Neighbors, Extra Trees, Arti cial Neural Networks and AdaBoost for global inter-subject classifi cation as well as for personalized subject specifi c classi cation.

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Bibtex

@INPROCEEDINGS{Khandelwal-2019,
     author = {Khandelwal, Icxa and Stollenwerk, Katharina and Kr{\"u}ger, Bj{\"o}rn and Weber, Andreas},
      title = {Posture Classification based on a Spine Shape Monitoring System},
  booktitle = {Computational Science and Its Applications -- ICCSA 2019},
       year = {2019},
       note = {accepted for publication},
   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 noninvasive
               approach to reduce back pain is postural modication 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 Gokhale SpineTracker device. We
               computed various features and evaluated the performance of K-Nearest
               Neighbors, Extra Trees, Articial Neural Networks and AdaBoost for
               global inter-subject classification as well as for personalized subject specific classication.}
}