{'fraction_non_alphanumeric': 0.017057569296375266, 'fraction_numerical': 0.0, 'mean_word_length': 5.257777777777778, '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}}
AI Approach In Monitoring The Physical And Psychological State Of Car Drivers And Remedial Action For Safe Driving

Road Accidents and casualties incited by drowsiness are an overall important social and monetary issue. The connection between drowsiness and accidents is bolstered by logical confirmations that relate to small-scale sleep. This project has focused on Driver drowsiness detection by using ECG signal extraction. This work expects to extract and arrange the basic four types of sleep through Wavelet Transform and machine learning calculations. The report covers a short theoretical introduction about the medicinal topic, features the extraction, filtering techniques, and afterward trains the extracted information through machine learning software. After that is covered, it demonstrates the results with two types of machine learning algorithms (active or drowsiness status) with WEKA software. The main benefit of this system is it will send a notification to the driver's mobile every second when he goes to sleeping status. Nowadays artificial intelligence cars are available with sleep assistance, however, the devices used on these cars are very expensive. So, our approach is to develop a system to predict the driver's drowsiness to reduce accidents caused by sleepiness at a low cost. The sleep / awake status is determined by both the factors RR peak's distance and R's amplitude