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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.
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]
Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: 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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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Human-Computer+Interaction%22">International Journal of Human-Computer Interaction</searchLink>. Dec2025, Vol. 41 Issue 23, p15040-15055. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+load%22">Cognitive load</searchLink><br /><searchLink fieldCode="DE" term="%22Blood+pressure%22">Blood pressure</searchLink><br /><searchLink fieldCode="DE" term="%22Distraction%22">Distraction</searchLink><br /><searchLink fieldCode="DE" term="%22Cell+phones%22">Cell phones</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+safety%22">Traffic safety</searchLink><br /><searchLink fieldCode="DE" term="%22Heart+beat%22">Heart beat</searchLink>
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  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1080/10447318.2025.2492804
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      – Code: eng
        Text: English
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        PageCount: 16
        StartPage: 15040
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      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Cognitive load
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      – SubjectFull: Blood pressure
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      – SubjectFull: Distraction
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      – SubjectFull: Cell phones
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      – SubjectFull: Traffic safety
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      – SubjectFull: Heart beat
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      – TitleFull: 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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            NameFull: Sharif, Mhd Saeed
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              M: 12
              Text: Dec2025
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              Y: 2025
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