@article{17344,
  author = {Matthias Boeker and Petter Jakobsen and Michael Riegler and LA Stabell and Ole Fasmer and P{\r a}l Halvorsen and Hugo Hammer},
  title = {Affect Recognition in Muscular Response Signals},
  abstract = {This study investigated the potential of recognising arousal in motor activity collected by wristworn accelerometers. We hypothesise that emotional arousal emerges from the generalised central nervous system which embeds affective states within motor activity. We formulate arousal detection as a statistical problem of separating two sets - motor activity under emotional arousal and motor activity without arousal. We propose a novel test regime based on machine learning assuming that the two sets can be distinguished if a machine learning classifier can separate the sets better than random guessing. To increase the statistical power of the testing regime, the performance of the classifiers is evaluated in a cross-validation framework, and to test if the classifiers perform better than random guessing, a repeated cross-validation corrected t-test is used. The classifiers were evaluated on the basis of accuracy and Matthew{\textquoteright}s correlation coefficient. The suggested procedures were further compared against a traditional multivariate paired Hotelling{\textquoteright}s T-squared test. The classifiers achieved an accuracy of about 60\%, and according to the proposed t-test were significantly better than random guessing. The suggested test regime demonstrated higher statistical power than Hotelling{\textquoteright}s T-squared test, and we conclude that we can distinguish between motor activity under emotional arousal and without it.},
  year = {2023},
  journal = {IEEE Access},
  volume = {11},
  pages = {61914 - 61928},
  month = {05/2023},
  publisher = {IEEE},
  doi = {10.1109/ACCESS.2023.3279720},
}