@inproceedings{StollenwerkMuellersMuelleretal.2018,
  author    = {Katharina Stollenwerk and Johannes M{\"u}llers and Jonas M{\"u}ller and Andr{\´e} Hinkenjann and Bj{\"o}rn Kr{\"u}ger},
  title     = {Evaluating an Accelerometer-Based System for Spine Shape Monitoring},
  series = {Gervasi, Murgante et al. (Eds.): Computational Science and Its Applications – ICCSA 2018. 18th International Conference, Melbourne, VIC, Australia, July 2–5, 2018, Proceedings, Part IV. Lecture Notes in Computer Science (LNCS), vol 10963},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-319-95170-6},
  doi       = {10.1007/978-3-319-95171-3\_58},
  pages     = {740 -- 756},
  year      = {2018},
  abstract  = {In western societies a huge percentage of the population suffers from some kind of back pain at least once in their life. There are several approaches addressing back pain by postural modifications. Postural training and activity can be tracked by various wearable devices most of which are based on accelerometers. We present research on the accuracy of accelerometer-based posture measurements. To this end, we took simultaneous recordings using an optical motion capture system and a system consisting of five accelerometers in three different settings: On a test robot, in a template, and on actual human backs. We compare the accelerometer-based spine curve reconstruction against the motion capture data. Results show that tilt values from the accelerometers are captured highly accurate, and the spine curve reconstruction works well.},
  language  = {en}
}