Meditation detection using sensors from wearable devices
Casado, Constantino Álvarez; Paananen, Petteri; Siirtola, Pekka; Pirttikangas, Susanna; López, Miguel Bordallo (2021-09-21)
Constantino Álvarez Casado, Petteri Paananen, Pekka Siirtola, Susanna Pirttikangas, and Miguel Bordallo López. 2021. Meditation Detection Using Sensors from Wearable Devices. In Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers (UbiComp '21). Association for Computing Machinery, New York, NY, USA, 112–116. DOI:https://doi.org/10.1145/3460418.3479318
© 2021 Association for Computing Machinery. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in UbiComp '21: Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers, https://doi.org/10.1145/3460418.3479318.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe2021100850402
Tiivistelmä
Abstract
Meditation is a practice that aims at self-inducing a state of calmed rest. In this work, we analyze physiological signals collected with wearable sensors to observe if meditation has a noticeable effect on the human body response and if this effect is inversely related to stress and can be detected using the same biosignals and similar features and methods. Our work is based on the extraction of statistical and physiological features and extends the models found in the literature by extracting 30 additional features related to heart rate variability. The results show that using wrist wearable devices, meditation periods can be distinguished from spontaneous rest with an accuracy of up to 86% accuracy.
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