Is it a good time to survey you? Cognitive load classification from blood volume pulse

By: Lisowska A., Wilk S., Peleg M.
Published in: Proceedings – IEEE Symposium on Computer-Based Medical Systems
SDGs : SDG 03  |  Units: Social Sciences  | Time: 2021 |  Link
Description: The CAPABLE project aims to improve the wellbeing of cancer patients managed at home via a mobile Coaching System recomm ending physical and mental health interventions. Patient reported outcomes are important for evaluation of the efficacy of these interventions. Nevertheless a large number of surveys might be overwhelming to patients. To understand the cognitive demand caused by the surveys and to find the adequate time to prompt patients to complete them we carried out a feasibility study. In this study we developed a machine learning cognitive load detector from blood volume pulse (BVP) captured by a photoplethysmography (PPG) signal. PPG sensors are available on consumer-grade smartwatches, which we will use in our Coaching System. We found that personalised 1D convolutional neural networks trained on raw BVP signal performed better in binary high vs low cognitive load classification than the personalised Support Vector Machines trained with heart rate variability and BVP features. We investigated if the further improvements can be obtained by teacher-student semi-supervised model training, nevertheless the performance gains were not notable. In the future we will include additional context information that might aid cognitive load estimation and drive both survey design as well as the timing of the prompts. © 2021 IEEE.