Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects both motor and non-motor functions. These implementations can significantly interfere with daily activities, particularly those that require coordination, attention, and decision-making such as driving. For older adults, the ability to drive is closely related to autonomy and quality of life, as it provides access to essential resources, social interaction, and personal independence. Therefore, understanding how PD influences emotional behavior in driving contexts is essential. In this study, the emotional responses of people with PD and a control group of healthy older adults are analyzed while driving simulated scenarios using deep learning facial expression recognition. Emotional probabilities were extracted frame by frame and analyzed to identify group differences. The findings of these experiments suggest that although general emotional patterns may appear similar, specific high-demand situations can elicit different affective states in individuals with PD. This highlights the usefulness of facial expression recognition systems in studying human behavior during complex driving tasks and supports their potential role for the design of adaptive driver assistance technologies for neurologically vulnerable populations.
