ORIGINAL RESEARCH article
Front. Digit. Health
Sec. Digital Mental Health
Volume 7 - 2025 | doi: 10.3389/fdgth.2025.1650085
Generalised Machine Learning Models Outperform Personalised Models For Cognitive Load Classification In Real-Life Settings
Provisionally accepted- 1Hasso-Plattner-Institut fur Digital Engineering gGmbH, Potsdam, Germany
- 2Universitat Potsdam, Potsdam, Germany
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By issuing work-break reminders, for example, personal assistants for cognitive load could be beneficial in maintaining health and life satisfaction in society. Wearable sensors facilitate the necessary real-time collection of physiological data. Still, publicly available real-life data sets obtained with wearable sensors are scarce, especially considering multi-modal recordings. Furthermore, data is usually recorded in either completely controlled or uncontrolled environments, missing the opportunity to study participants across optimal laboratory and realistic real-life settings. This work collected data from ten university students during given and self-chosen cognitive load tasks, resembling typical working environments from over 40% of the OECD population, and investigated if commercially available sensors suffice for building cognitive load assistants. The study design accounted for a balanced distribution of eight working hours per participant, split between controlled and uncontrolled environments. Across participants, no single feature correlated significantly with cognitive load, but differences in smartwatch indices and biomarkers were identified between low- and high-load scenarios. Generalised machine learning models like Logistic Regression achieved F1 scores of up to 0.91, 0.77, and 0.54 for two, three, and five-class classification, respectively. The presented study design marks a step towards real-life mental state assistants, and the anonymised dataset was made publicly available.
Keywords: human-centered computing, wearable sensors, Cognitive Load Experiments, uncontrolled environment, WaveletDecomposition, Time series classification, machine learning, Personal assistant
Received: 19 Jun 2025; Accepted: 05 Sep 2025.
Copyright: © 2025 Anders, Bhaduri and Arnrich. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Christoph Anders, Hasso-Plattner-Institut fur Digital Engineering gGmbH, Potsdam, Germany
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