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| Hauptverfasser: | , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2603.17767 |
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| _version_ | 1866915872190234624 |
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| author | Sale, Carter Stolar, Melissa N. Patil, Gaurav Gostelow, Michael J. Wallier, Julia Macpherson, Margaret C. Kruger, Jan-Louis Dras, Mark Hosking, Simon G. Kallen, Rachel W. Richardson, Michael J. |
| author_facet | Sale, Carter Stolar, Melissa N. Patil, Gaurav Gostelow, Michael J. Wallier, Julia Macpherson, Margaret C. Kruger, Jan-Louis Dras, Mark Hosking, Simon G. Kallen, Rachel W. Richardson, Michael J. |
| contents | Real-time cognitive workload monitoring is crucial in safety-critical environments, yet established measures are intrusive, expensive, or lack temporal resolution. We tested whether facial movement dynamics from a standard webcam could provide a low-cost alternative. Seventy-two participants completed a multitasking simulation (OpenMATB) under varied load while facial keypoints were tracked via OpenPose. Linear kinematics (velocity, acceleration, displacement) and recurrence quantification features were extracted. Increasing load altered dynamics across timescales: movement magnitudes rose, temporal organisation fragmented then reorganised into complex patterns, and eye-head coordination weakened. Random forest classifiers trained on pose kinematics outperformed task performance metrics (85% vs. 55% accuracy) but generalised poorly across participants (43% vs. 33% chance). Participant-specific models reached 50% accuracy with minimal calibration (2 minutes per condition), improving continuously to 73% without plateau. Facial movement dynamics sensitively track workload with brief calibration, enabling adaptive interfaces using commodity cameras, though individual differences limit cross-participant generalisation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_17767 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Facial Movement Dynamics Reveal Workload During Complex Multitasking Sale, Carter Stolar, Melissa N. Patil, Gaurav Gostelow, Michael J. Wallier, Julia Macpherson, Margaret C. Kruger, Jan-Louis Dras, Mark Hosking, Simon G. Kallen, Rachel W. Richardson, Michael J. Human-Computer Interaction Computer Vision and Pattern Recognition Real-time cognitive workload monitoring is crucial in safety-critical environments, yet established measures are intrusive, expensive, or lack temporal resolution. We tested whether facial movement dynamics from a standard webcam could provide a low-cost alternative. Seventy-two participants completed a multitasking simulation (OpenMATB) under varied load while facial keypoints were tracked via OpenPose. Linear kinematics (velocity, acceleration, displacement) and recurrence quantification features were extracted. Increasing load altered dynamics across timescales: movement magnitudes rose, temporal organisation fragmented then reorganised into complex patterns, and eye-head coordination weakened. Random forest classifiers trained on pose kinematics outperformed task performance metrics (85% vs. 55% accuracy) but generalised poorly across participants (43% vs. 33% chance). Participant-specific models reached 50% accuracy with minimal calibration (2 minutes per condition), improving continuously to 73% without plateau. Facial movement dynamics sensitively track workload with brief calibration, enabling adaptive interfaces using commodity cameras, though individual differences limit cross-participant generalisation. |
| title | Facial Movement Dynamics Reveal Workload During Complex Multitasking |
| topic | Human-Computer Interaction Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.17767 |