Remote Industrial Technician Training Through Real-Time XR Assistance

Development of the industrial techs in remote industries is quite important to grant efficiency and security under complex industrial cases. Conventional forms of remote training support environment has since been found not to add any value other than being stagnant general-purpose training, which is not aware of the real time variations in technician skill level and cognitive strain and training that happens to be considerate to variations of the environment. This unbending can lead to high levels of errors, extended classes and incompetence of the skills. Then, the remote guidance realization with the use of the experts and the visual instructions are more oriented on commercial XR-based training systems which do not suggest the possibility to monitor the physiology parameters, to fulfill the personalization. To eliminate these weaknesses, the paper is proposed to implement a pioneering AI-enhanced adaptive realtime Extended Reality (XR) training system that will include a contextual feed-back loop. The system is the initial to integrate wearable biometric sensors with both a system to constantly determine technician heart rate, skin conductance, and contact with the environment as well as AI algorithms capable of gauging cognitive load, emotional state and gauge the task performance of technicians in real time. Based on this information the contents of the XR instruction (visual overlays and auditory and haptic indicators) are provided dynamically according to the current capacity, stress and the rate at which the trainee is performing the skillsLong distance expert interface with real-time bio and haptic sensor feedforward makes the expert deliver his or her advice in a more explicit manner and experience unmatched sense of the situation and empathy. The 5G / 6G network with ultra-low latency and edges computing platform will provide a seamless interaction of both systems that is highly important as far as operational environments are concerned. Early testing will indicate that the human-AI adaptive training model has the capacity to reduce errors in training processes, reduce the time within which the trainees perform the training activity, and obtain large trust and satisfaction with the training process as compared to the conventional XR assistance gadgets. Personalization of the instruction and the real-time modulation of instruction through biometric and performance data provide transformative efficiency, scalability and security of the mammoth training needed in industry to unlock the potential of the technicians. The paper determines the capacity to form synergies among the AI potential, biometric sensors and XR technologies to transform the realm of distantly applied training modes, specifically in the industrial areas.

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