The CAPABLE project aims to improve the wellbeing of cancer patients managed at home via a mobile Coaching System recommending 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.
|Title of host publication
|Proceedings - 2021 IEEE 34th International Symposium on Computer-Based Medical Systems, CBMS 2021
|Joao Rafael Almeida, Alejandro Rodriguez Gonzalez, Linlin Shen, Bridget Kane, Agma Traina, Paolo Soda, Jose Luis Oliveira
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - Jun 2021
|34th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2021 - Virtual, Online
Duration: 7 Jun 2021 → 9 Jun 2021
|Proceedings - IEEE Symposium on Computer-Based Medical Systems
|34th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2021
|7/06/21 → 9/06/21
Bibliographical notePublisher Copyright:
© 2021 IEEE.
- Cognitive load
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
- Computer Science Applications