Journal of Cognitive Human-Computer Interaction JCHCI 2771-1463 2771-1471 10.54216/JCHCI https://www.americaspg.com/journals/show/3734 2021 2021 Assistant Professor, School of Electronics, Vellore Institute of Technology, Chennai, Tamil Nadu, India Sindhuja Sindhuja Assistant Professor, Department of ECE, Paavai Engineering College, Namakkal, Tamil Nadu, India Vijay Murugan. S. Assistant Professor, Department of ECE, Knowledge Institute of Technology, Salem, Tamil Nadu , India Elarmathi. S. Road accidents and emergency services delay are the main significant issues. To overcome these issues need to develop a system. Efficient handling of accidents through the immediate detection and provide timely aid are more crucial. Accident detection and emergency system depends on IoT (Internet of things) with minimum delay are gaining significant attention towards industry and academic literature. Several researches are investigated using IOT technology to detect accidents. In this work, we proposed an effective accident detection method by employing five sensors not only to detect accident but also to report type of accident such as collision, no accident, roll over or fall off. In addition to that, the status of the accident is communicated to the IBM Watson Cloud platform. The incoming data received in the node red platform is integrated with the Google Maps to show location and other information about the accident that can be accessed by the hospital through website and sending alert messages to victim acquaintances. In addition, two Machine Learning (ML) models based on K-Nearest Neighbor (KNN) model and the Naïve Bayes (NB) model are compared to find out the best accident detection model. It is noticed that the KNN model is the very effective ML model, which employed to know the accident status and to enhance the system by providing patient’s details, a kill switch and sending messages often until acknowledgement is received. 2025 2025 45 56 10.54216/JCHCI.090104 https://www.americaspg.com/articleinfo/25/show/3734