Cold-Start Model Adaptation: Evaluation of Short Baseline Calibration

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

1 Citation (Scopus)

Abstract

Human physiology and reactions to external stimuli differ between individuals. Researchers have developed strategies to adapt to these differences but the adaptations generally require data from each individual beforehand. A cold-start occurs when there is no data from a new individual. To address this, current study proposes user calibration which uses short segments of easily obtainable baseline data to adapt to new individuals. Experiments were conducted on two public stress and affect detection datasets, WESAD and SWELL-KW, to assess the effectiveness of the proposed calibration method and to determine suitable duration of the baseline measurement. Results showed that user calibration always beat the non-personalized model and segments of 3-8 minutes seemed to be most promising to consider for future use.
Original languageEnglish
Title of host publication Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing, UbiComp/ISWC ’23
PublisherAssociation for Computing Machinery ACM
Pages417-422
Number of pages6
ISBN (Electronic) 979-8-4007-0200-6
DOIs
Publication statusPublished - 8 Oct 2023
MoE publication typeA4 Article in a conference publication
Event2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing, UbiComp/ISWC ’23 - Cancun, Mexico
Duration: 8 Oct 202312 Oct 2023

Conference

Conference2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing, UbiComp/ISWC ’23
Country/TerritoryMexico
CityCancun
Period8/10/2312/10/23

Funding

The work was funded by the Academy of Finland under GrantNos.: 334092, 351282 and VTT.

Keywords

  • personalization
  • stress detection
  • wearable devices
  • wellbeing

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