Assessing Driver Cognitive Load from Handsfree Mobile Phone Use: Innovative Analysis Approach Based on Heart Rate, Blood Pressure and Machine Learning.
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| Title: | Assessing Driver Cognitive Load from Handsfree Mobile Phone Use: Innovative Analysis Approach Based on Heart Rate, Blood Pressure and Machine Learning. |
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| Authors: | Sharif, Mhd Saeed (AUTHOR), Ossai, Boniface Ndubuisi (AUTHOR), Moncy, Jijomon Chettuthara (AUTHOR), Alblehai, Fahad (AUTHOR), Fu, Cynthia H.Y. (AUTHOR) |
| Source: | International Journal of Human-Computer Interaction. Dec2025, Vol. 41 Issue 23, p15040-15055. 16p. |
| Subjects: | Machine learning, Cognitive load, Blood pressure, Distraction, Cell phones, Traffic safety, Heart beat |
| Abstract: | Although using a handheld mobile phone while driving is illegal, hands-free (HF) use remains permitted, despite causing cognitive distraction. This study investigated the cognitive impact of HF phone use on drivers using real-time physiological data—heart rate (HR) and blood pressure (BP)—and applied machine learning to classify driver cognitive load. Participants performed complex tasks while driving and reversing, both with and without HF phone use. Results showed significant increases in HR and BP during HF phone conversations. A novel feedforward neural network model achieved 97% accuracy in classifying cognitive load. The study's real-time, naturalistic approach enhances its generalisability and validity. It uniquely applies advanced ML techniques to highlight the cognitive risks of HF phone use while driving. These findings provide crucial evidence for policymakers, particularly in the UK, supporting efforts to reconsider regulations and improve road safety. The study also offers insights for traffic safety experts and behavioural researchers. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Although using a handheld mobile phone while driving is illegal, hands-free (HF) use remains permitted, despite causing cognitive distraction. This study investigated the cognitive impact of HF phone use on drivers using real-time physiological data—heart rate (HR) and blood pressure (BP)—and applied machine learning to classify driver cognitive load. Participants performed complex tasks while driving and reversing, both with and without HF phone use. Results showed significant increases in HR and BP during HF phone conversations. A novel feedforward neural network model achieved 97% accuracy in classifying cognitive load. The study's real-time, naturalistic approach enhances its generalisability and validity. It uniquely applies advanced ML techniques to highlight the cognitive risks of HF phone use while driving. These findings provide crucial evidence for policymakers, particularly in the UK, supporting efforts to reconsider regulations and improve road safety. The study also offers insights for traffic safety experts and behavioural researchers. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10447318 |
| DOI: | 10.1080/10447318.2025.2492804 |
