Intelligent accident mitigation system by mining vital signs using wireless body sensor

Abstract

Recent surveys show that the number of road accidents has increased predominantly. One of the major causes to which increased accidents are attributed to is physical ailment of drivers. Continuous monitoring of driver's health condition is essential in order to reduce car accidents that occur due to health abnormality. A non-intrusive method is demanded so as to prevent hindering of driving activity. This paper presents a mobile health monitoring system which is an application running in Android based smart phone. The mobile phone acquires vital parameters such as Heart Rate (HR) and Respiration Rate (RR) from a wearable body sensor through Bluetooth. As faster heart beat and shortness of breath are the most common symptoms of heart problem, the proposed system considers heart rate and respiration rate for deciding abnormal health status. A Bayesian Belief network (BBN) is designed to analyze the vital parameters along with the driver's health history and decide whether the health status of the driver is normal or abnormal in real time. Bayesian Network is a powerful representation for uncertain domains like human health status. Also, it improves classification accuracy as it allows seamless integration of additional information such as the driver's health records with sensed vital information for deciding abnormality and thus avoids false alarms. When abnormality is detected, immediately the driver gets a beep alert call in his mobile and call alert is generated to the caregiver in the case of emergency. The proposed system is tested in real time and it gives reasonable accuracy.