The amount of road accidents caused by driver drowsiness is one of the world’s major challenges. These accidents lead to numerous fatal and non-fatal injuries which impose substantial financial strain on individuals and governments every year. As a result, it is critical to prevent catastrophic accidents and reduce the financial burden on society caused by driver drowsiness. The research community has primarily focused on two approaches to identify driver drowsiness during the last decade: intrusive and non-intrusive. The intrusive approach includes physiological measures, and the non-intrusive approach includes vehicle-based and behavioral measures. In an intrusive approach, sensors are used to detect driver drowsiness by placing them on the driver’s body, whereas in a non-intrusive approach, a camera is used for drowsiness detection by identifying yawning patterns, eyelid movement and head inclination. Noticeably, most research has been conducted in driver drowsiness detection methods using only single measures that failed to produce good outcomes. Furthermore, these measures were only functional in certain conditions. This paper proposes a model that combines the two approaches, non-intrusive and intrusive, to detect driver drowsiness. Behavioral measures as a non-intrusive approach and sensor-based physiological measures as an intrusive approach are combined to detect driver drowsiness. The proposed hybrid model uses AI-based Multi-Task Cascaded Convolutional Neural Networks (MTCNN) as a behavioral measure to recognize the driver’s facial features, and the Galvanic Skin Response (GSR) sensor as a physiological measure to collect the skin conductance of the driver that helps to increase the overall accuracy. Furthermore, the model’s efficacy has been computed in a simulated environment. The outcome shows that the proposed hybrid model is capable of identifying the transition from awake to a drowsy state in the driver in all conditions with the efficacy of 91%.
Driver drowsiness is one of the major causes of road accidents which leads to fatal and non-fatal injuries, sudden deaths, and substantial monetary losses. Due to advancements in technologies like Artificial Intelligence (AI), various approaches have been carried out to detect driver drowsiness at the early stage. The existing measures comprises certain issues like intrusiveness, variation in results, and tested in simulated environment only. A hybrid solution is the need for early detection of drowsiness of driver by amalgamation of multiple effective measures. Many researchers have concluded that developing a driver drowsiness detection system by using hybrid measures would be more efficient and highly recommended. The main contribution of this paper is to evaluate and identify the effective measures to detect driver drowsiness and choose the best measures to combine. It will help in early detection of the driver drowsiness in a more efficient manner and avoid crashes on the roads.
Background: In developing countries, the healthcare system is facing numerous challenges. One of the major challenges faced by the healthcare system is that the healthcare service providers are meager and geographically far from the densely populated area. Objective: To overcome the above challenge, the present research work proposes SHRMS (Smart Heart Rate Monitoring System) which provides the ad-hoc services to the patients who are in the transit mode in the emergency vehicle. Method: A pulse sensor is attached to the patient’s fingertip to fetch the heart rate of the patient. The patient’s data is further transmitted to the microcontroller which in turn transmits the data to the ThingSpeak cloud service. Result: SHRMS provides the real-time monitoring of the patient and helps to provide emergency aid as per the patient’s current situation. Conclusion: This device is beneficial for developing countries where the healthcare service providers are very less and geographically scattered.
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