Event-related driver stress detection with smartphones in an urban environment: a naturalistic driving study.
Ergonomics
; 67(10): 1371-1390, 2024 Oct.
Article
en En
| MEDLINE
| ID: mdl-38501496
ABSTRACT
Driving in urban areas can be challenging and encounter acute stress. To detect driver stress, collecting data on real roads without interfering the driver is preferred. A smartphone-based data collection protocol was developed to support a naturalistic driving study. Sixty-one participants drove on predetermined real road routes, and driving information as well as physiological, psychological, and facial data were collected. The algorithm identified potentially stressful events based on the collected data. Participants classified these events as low, medium, or highly stressful events by watching recorded videos after the experiment. These events were then used to train prediction models. The best model achieved an accuracy of 92.5% in classifying low/medium/highly stressful events. The contribution of physiological, psychological, and facial expression indices and individual profile information was evaluated. The method can be applied to visualise the geographical distribution of stressors, monitor driver behaviour, and help drivers regulate their driving habits.
The data collection protocol for driving on real roads and the stressful event identification method could potentially be applied for in-vehicle driver status monitoring and stress intervention.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Conducción de Automóvil
/
Estrés Psicológico
/
Teléfono Inteligente
Límite:
Adult
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Ergonomics
Año:
2024
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Reino Unido