TY  - JOUR
A1  - Kappattanavar, Arpita Mallikarjuna
A1  - Hecker, Pascal
A1  - Moontaha, Sidratul
A1  - Steckhan, Nico
A1  - Arnrich, Bert
T1  - Food choices after cognitive load
T2  - Sensors
N2  - Psychology and nutritional science research has highlighted the impact of negative emotions and cognitive load on calorie consumption behaviour using subjective questionnaires. Isolated studies in other domains objectively assess cognitive load without considering its effects on eating behaviour. This study aims to explore the potential for developing an integrated eating behaviour assistant system that incorporates cognitive load factors. Two experimental sessions were conducted using custom-developed experimentation software to induce different stimuli. During these sessions, we collected 30 h of physiological, food consumption, and affective states questionnaires data to automatically detect cognitive load and analyse its effect on food choice. Utilising grid search optimisation and leave-one-subject-out cross-validation, a support vector machine model achieved a mean classification accuracy of 85.12% for the two cognitive load tasks using eight relevant features. Statistical analysis was performed on calorie consumption and questionnaire data. Furthermore, 75% of the subjects with higher negative affect significantly increased consumption of specific foods after high-cognitive-load tasks. These findings offer insights into the intricate relationship between cognitive load, affective states, and food choice, paving the way for an eating behaviour assistant system to manage food choices during cognitive load. Future research should enhance system capabilities and explore real-world applications.
KW  - cognitive load
KW  - eating behaviour
KW  - machine learning
KW  - physiological signals
KW  - photoplethysmography
KW  - electrodermal activity
KW  - sensors
Y1  - 2023
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/64328
SN  - 1424-8220
VL  - 23
IS  - 14
PB  - MDPI
CY  - Basel
ER  -