Driver Health Monitoring System to Identify Cardiac Problems in Drivers using Live ECG Signal

According to contemporary estimations, a significant proportion of elderly drivers are prone to experiencing accidents or collisions due to their diverse health states during the act of driving. Currently, individuals in the middle-age and younger demographics are encountering comparable challenges. Research indicates that drivers who experienced crashes caused by medical emergencies are not influenced by vehicle design or the integrity of the road, as seen by the sort of crashes and the manner in which they occurred. This research presents a comparison and measurement of heart rate and heart variability data for the occurrence of heart disease, stroke, or any emergency situation. The methods employed include the use of electrocardiogram (ECG) and Photo Plethysmo Graphy (PPG) sensors.The enhanced performance of deep learning over conventional machine learning methods in addressing various artificial intelligence tasks, including image processing, computer vision, object detection, speech recognition, and medical imaging, has propelled deep learning to become the dominant term in the field of Artificial Intelligence applications