Analyzing Spinal Shape Changes During Posture Training Using a Wearable Device

Katharina Stollenwerk, Jonas Müller, André Hinkenjann, and Björn Krüger
In: Sensors (Aug. 2019)
 

Abstract

Lower back pain is one of the most prevalent diseases in Western societies. A large percentage of European and American populations suffer from back pain at some point in their lives. One successful approach to address lower back pain is postural training, which can be supported by wearable devices, providing real-time feedback about the user’s posture. In this work, we analyze the changes in posture induced by postural training. To this end, we compare snapshots before and after training, as measured by the Gokhale SpineTracker™. Considering pairs of before and after snapshots in different positions (standing, sitting, and bending), we introduce a feature space, that allows for unsupervised clustering. We show that resulting clusters represent certain groups of postural changes, which are meaningful to professional posture trainers.

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Bibtex

@ARTICLE{stollenwerk-2019a,
    author = {Stollenwerk, Katharina and M{\"u}ller, Jonas and Hinkenjann, Andr{\'e} and Kr{\"u}ger, Bj{\"o}rn},
     title = {Analyzing Spinal Shape Changes During Posture Training Using a Wearable Device},
   journal = {Sensors},
      year = {2019},
     month = aug,
  abstract = {Lower back pain is one of the most prevalent diseases in Western societies. A large percentage of
              European and American populations suffer from back pain at some point in their lives. One successful
              approach to address lower back pain is postural training, which can be supported by wearable
              devices, providing real-time feedback about the user’s posture. In this work, we analyze the
              changes in posture induced by postural training. To this end, we compare snapshots before and after
              training, as measured by the Gokhale SpineTracker™. Considering pairs of before and after
              snapshots in different positions (standing, sitting, and bending), we introduce a feature space,
              that allows for unsupervised clustering. We show that resulting clusters represent certain groups of
              postural changes, which are meaningful to professional posture trainers.},
       doi = {https://doi.org/10.3390/s19163625}
}