Catching Patient’s Attention at the Right Time to Help Them Undergo Behavioural Change: Stress Classification Experiment from Blood Volume Pulse

Aneta Lisowska, Szymon Wilk, Mor Peleg

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

Abstract

The CAPABLE project aims to improve the wellbeing of cancer patients managed at home via a coaching system recommending personalized evidence-based health behavioral change interventions and supporting patients compliance. Focusing on managing stress via deep breathing intervention, we hypothesise that the patients are more likely to perform suggested breathing exercises when they need calming down. To prompt them at the right time, we developed a machine-learning stress detector based on blood volume pulse that can be measured via consumer-grade smartwatches. We used a publicly available WESAD dataset to evaluate it. Simple 1D CNN achieves 0.837 average F1-score in binary stress vs. non-stress classification and 0.653 in stress vs. amusement vs. neutral classification reaching the state-of-art performance. Personalisation of the population model via fine-tuning on a small number of annotated patient-specific samples yields 12% improvement in stress vs. amusement vs. neutral classification. In future work we will include additional context information to further refine the timing of the prompt and adjust the exercise level.

Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine - 19th International Conference on Artificial Intelligence in Medicine, AIME 2021, Proceedings
EditorsAllan Tucker, Pedro Henriques Abreu, Jaime Cardoso, Pedro Pereira Rodrigues, David Riaño
PublisherSpringer Science and Business Media Deutschland GmbH
Pages72-82
Number of pages11
ISBN (Print)9783030772109
DOIs
StatePublished - 2021
Event19th International Conference on Artificial Intelligence in Medicine, AIME 2021 - Virtual, Online
Duration: 15 Jun 202118 Jun 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12721 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Conference on Artificial Intelligence in Medicine, AIME 2021
CityVirtual, Online
Period15/06/2118/06/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

  • Blood volume pulse
  • Classification
  • Fogg behavioral model
  • Stress
  • Wearable

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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