Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item An IoT-Enabled Stress Detection Scheme Using Facial Expression(Institute of Electrical and Electronics Engineers Inc., 2022) Angalakuditi, A.; Bhowmik, B.R.Depression is a significant problem in our society, as it is the cause of many health problems. The ongoing burden of intellectual function and continuous technological development, leading to constant change and the need for flexibility, makes the situation even more significant for people. It is necessary to see it early to prevent stress from becoming chronic and irreversible irritability. Unfortunately, a way to detect automatic, continuous, invisible pressure does not exist. This work involves monitoring a person's attention and emotional state across the ages. An IoT-enabled unobtrusive real-time monitoring system is developed to detect the person's emotional states by analyzing facial expression videos. The proposed method identifies individual emotions in each video frame, and a decision on the level of stress is made at the sequence level. © 2022 IEEE.Item IoT-Enabled Driver Drowsiness Detection Using Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2022) Guria, M.; Bhowmik, B.R.The most important procedure for preventing traffic accidents in recent years, maybe on a global scale, is the identification of sleepy drivers. Every day, over 350 people are killed in traffic accidents, and almost 1,000 more suffer injuries. Recent technological advancements could reduce this tendency by 40%. It is still possible to get these benefits despite significant challenges. This paper develops an intelligent alerting method to prevent accidents caused by drivers falling asleep at the wheel. As part of smart cars, the proposed method with the total capacity prevents sleepy driver impairment automatically. The proposed approach detects drowsiness in analyzing the live streaming of drivers' videos. Eye Aspect Ratio (EAR) and the Euclidean distance of the eye are used to analyze the input video stream to identify sleepy drivers. Experimental results show that the proposed scheme can lower dangerous accidents and injuries caused by road traffic. © 2022 IEEE.