The research project aims to develop a comprehensive artificial intelligence (AI) and virtual reality (VR) based collaborative learning platform for theorizing risk and situational awareness (SA) under man-made disruptions (e.g., accident or disaster) in socio-technical systems and implementing collaborative learning for people and community. To meet this goal, the four integrated objectives are (i) development of AI-based models for hazard identification, risk assessment, and analysis of high-risk accident-paths, (ii) construction and re-creation of abnormal scenarios and high-risk accident-paths using AI and VR technologies, (iii) measuring and modelling of situational awareness among people using AI, VR, and human sensing technologies, and (iv) development of a collaborative VR platform for learning and implementation of the same in real-time. In order to execute the project a few safety-critical work systems from industries such as steelmaking, coal mining and automobiles will be selected. Necessary information and data will be collected. Proper study designs will be made involving the participation of designers, policymakers, managers, operators/workers, and other stakeholders. The exponential technologies like AI, VR, and eye tracking will be used for the development of the models, simulators, and collaborative learning platform. Experiments will be conducted in the Safety Analytics and Virtual reality laboratory situated in the PI’s department. The outcomes of the project can be copyrighted, patented, commercialized, and published. The implementation of the outcomes of the project will help industries and service organizations manifold. For example, designers can use it to enhance their design knowledge and accordingly design/redesign for safety into the systems. Operators/workers can learn individually or in groups using the collaborative VR platform for learning.
Managing safety is a perennial problem for every organization. Today, lead industries spend a huge amount of money and efforts on improving safety standards and building competency among the employees in safety. Safety management systems are in place which is the primary source for safety-related data. Second, the use of IT and sensors increases the number of data manifolds. Even then, the list of problems associated with managing safety is endless. One of the major problems which acted as the root of the motivation behind this project is that even though there is the rapid influx of a large amount of data in the area of safety management, there lacks the capacity or skill to analyses the same. Without appropriate analysis of data, the entire exercise of extensive data collection, however robust it may be, is nothing but a waste of resource. This project attempts to bridge the gap between the industry data collection systems and academic analytical skills by facilitating the creation of a centralized safety database with off-line and on-line data pertaining to safety management systems of various industries.
The ultimate aim of this project is developing a sustainable safety management system (SMS) for industries with a primary focus on steel sectors for eliminating fatalities and reducing lost-time injuries to people at work. Three integrated safety modules namely, (i) Module-1: the creation of safety analytics database, (ii) Module-2: development of prediction models for predicting accidents/injuries across employee demographics, such as job-wise, location-wise, function-wise, age-wise, experience-wise, etc., and (iii) Module-3: design of prevention and mitigation programs and technologies based on the analysis of safety data will be developed.
The list of projects below were done on safety engineering, occupational health, ergonomics, and analytcs by IIT Kharagpur for various customers.