Generative AI Wearable Assistant for Simulated Reach-Back Support

Abstract

This research investigates the development of a generative AI wearable assistant designed to provide simulated reach-back support for maintenance and troubleshooting applications. Reach-back support refers to accessing expertise remotely to assist individuals in challenging situations. In various domains such as healthcare, emergency response, and technical troubleshooting, reaching out to subject matter experts for real-time guidance can be crucial. Leveraging the capabilities of generative AI, we aim to create a wearable hardware and software device that serves as an assistant that simulates expert knowledge and provides personalized, context-aware (via object detection and a natural language interface) assistance. This poster presents preliminary findings from efforts to demonstrate the technical feasibility of this concept through the design, fabrication, and demonstration of an initial wearable prototype. Future research will seek to develop a deep learning model trained on extensive domain-specific data to generate relevant and accurate responses for maintenance and troubleshooting of specific equipment and systems. The wearable assistant incorporates speech recognition, natural language understanding, speech synthesis, and image-based object detection technologies for seamless communication and contextualization of reach-back requests. The findings from this research have the potential to enhance decision-making, problem-solving, and support capabilities in various professional and emergency scenarios where access to real-time expertise is limited.

Keywords: Generative AI, Artificial Intelligence, Wearables, Maintenance

DOI: 10.54941/ahfe1004431