{'fraction_non_alphanumeric': 0.01872399445214979, 'fraction_numerical': 0.0, 'mean_word_length': 5.413333333333333, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Detecting and Visualizing Context and Stress via a Fuzzy Rule-Based System During Commuter Driving

Stress is a negative emotion that occurs in everyday life, such as driving. Recurrent exposure to stress can be detrimental to cardiovascular health in the long term. Nevertheless, the development of adaptive coping strategies can mitigate the influence of everyday stress on cardiovascular health. Understanding context is essential to modelling the occurrence of stress and other negative emotions during everyday life. However, driving is a highly dynamic environment, whereby the context is often described using ambiguous linguistic terms, which can be difficult to quantify. This paper proposes a Fuzzy Logic Mamdani Model to automatically estimate different categories of driving context. The system is comprised of two Membership Functions (MFs), which converts the inputs of speed and traffic density into linguistic variables. Our approach then uses these data to identify six states of driving – Idling, Journey Impedance, High Urban Workload, Low Urban Workload, High Non-Urban Workload and Low Non-Urban Workload. An interactive visualization has then been implemented that links this fuzzy logic model with psychophysiological data to identify the context of stress experienced on the road. The system has been validated using real-world data that has been collected from eight participants during their daily commuter journeys.