Delay of emergency services and lack of instantaneous reporting of accidents is the main contributor to the serious injuries or death in two-wheeler accidents. A Smart Helmet system has been created to solve this acute problem through intelligent detection of accidents during the accident and automatic notification of emergency services. The helmet has an Inertial Measurement Unit (IMU), an accelerator and a gyroscopic device that constantly reads the movement of the riders and matches any abrupt impact or unusual movement patterns that are signs of a collision. When an accident happens, the system sends the live position of the rider through SMS to a GSM/GPS receiver that triggers an automatic emergency call to predefined contacts or closest police department. The system has a cancel window to avoid false alarms when the bike is suddenly braking or making a minor slip. The rider is free to remain on alert. In addition, it has machine learning (TinyML) that improves the precision of the detection of various motion patterns, including actual collisions, potholes, or regular riding to improve false positives. This use of AI will make sure that there are real accidents that cause the alarm. The Smart Helmet is an IoT-enabled safety solution that was designed as a low-cost solution to safety, guaranteeing not only the speed of medical help but also making roads smarter.
ASHWINI A, N. Nalini, A. Rosi et al.· International Conference on...· 0 citations
The early detection of individuals with Mild Cognitive Impairment (MCI) who are at high risk of developing Alzheimer’s Disease (AD) is important for proper clinical intervention and disease management. Studies show that there is a strong link between brain age, which is estimated from brain scan data, and actual chronological age. This difference is called the Brain Age Gap (BAG), and it acts as a useful biological marker for identifying people at risk of brain degeneration. This study aims to create an automated dual-network framework that predicts MCI subjects’ likelihood of converting to AD based on their structural magnetic resonance imaging (MRI) brain scans. The two networks will consist of (i) a three-dimensional convolutional neural network (CNN) model that predicts an estimated brain age based on the inputted MRI scan of a subject, and (ii) a risk prediction network that predicts probability of MCI-to-AD conversion by incorporating estimated brain age, BAG, and deep feature representations. The framework will be trained in sequential order using a longitudinal neuroimaging dataset. Results from the experiment indicate higher classification performance can be attained with the proposed dual-network architecture over current state-of-the-art single-stage classification methods or models without brain age. Furthermore, these results strongly support the use of brain age-based predictive features for early prediction of AD risk. The proposed method offers a fully automated and clinically interpretable solution for supporting early diagnosis and personalized intervention planning in Alzheimer’s disease.
B. Stanley, K. Sindhubala, J. S. Shemona et al.· International Conference on...· 0 citations
Traffic signs are road facilities that communicate, direct, limit, caution or teach information, whether in the form of words or symbols. As the demand for the intelligence of vehicles is on the rise, there is a great need to invent and identify traffic signs automatically using technology. Nonetheless, the identification of traffic signs is not that easy, as a number of negative parameters exist, such as bad weather, change of perspective, physical impairment, and others. Currently, most of the available text mining algorithms help in processing the whole data to identify the traffic sign images. In this proposed research, an extensive sign board detection algorithm is developed where AlexNet image classification algorithm forms the premier stage. It is mainly focussed on the process of detection with the improvement of the traffic signs using a boundary enhancement algorithm along with the average filter. This helps in reducing the noise and enhances the sign to be fed into the classifier system. This approach enhances precision of 99.27%, sensitivity of 99.41% and specificity of 99.47%. Thus the proposed algorithm minimizes the time taken to detect the traffic sign in misty weather.
ASHWINI A, G. Santhiya, L. Suresh et al.· International Conference on...· 0 citations
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