Roadmap

This project lays a solid foundation for the future development of 'Health-Metaverse', a novel ecosystem enabled by Large AI Models and Extended Reality (XR) tailored for personalized home-centric healthcare.

As the world becomes increasingly interconnected and digitalized, healthcare systems have not been left behind. The traditional model of healthcare, characterized by in-person consultations, centralized hospital care, and one-size-fits-all treatment approaches, is undergoing a significant transformation. This is driven by the exponential growth of technologies such as artificial intelligence (AI), extended reality (XR), and telemedicine, which are contributing to a shift towards more personalized, efficient, and accessible care.

Grounded in the combination of AI's predictive analytics and XR's immersive experiences, the Health-Metaverse represents an advanced solution that addresses geographical and accessibility challenges in the current healthcare landscape.   The potential of the Health-Metaverse is to revolutionize the patient-doctor interface, by mitigating the constraints of space and time. 

Metaverse, a 3D virtual world that can enable users to interact with highly customizable avatars through the internet via extended reality (XR) based human-robot equipment, is attracting growing attention nowadays. Metaverse can help shorten the gap between humans and the virtual world, and is increasingly consolidating its position in various applications, including industrial workspace, healthcare systems, etc.  Here, we mainly focus on healthcare applications, which leads to the construction of Health-Metaverse, an ecosystem for personalized home-centric healthcare.

The emergence of the Health-Metaverse concept, a fusion of Large AI Models and XR technology, represents a unique, multidimensional approach to healthcare. The idea of creating a digital, interactive ecosystem where patients can receive personalized care from the comfort of their homes is revolutionary. This paradigm shift not only promises improved healthcare outcomes but also enhances the overall patient experience by breaking down geographical barriers and offering more flexible care schedules.

In the healthcare domain, effective communication and access to reliable medical information are essential for delivering quality care and empowering individuals to make well-informed decisions about their health. However, the complexities of medical knowledge and the limitations of traditional communication channels often impede efficient information exchange. This necessitates the development of innovative solutions that harness technology to bridge the communication gap in healthcare. With the rapid advancement of artificial intelligence (AI), chat-based models such as CHATGPT have emerged as invaluable tools for intelligent interactions. Built upon large-scale pre-trained language models, CHATGPT demonstrates impressive capabilities in generating human-like responses and understanding the semantics of natural language conversations. This technology opens up new avenues for the development of intelligent solutions in the field of healthcare.

To offer users a more intuitive robot control experience, we have integrated natural language-based robot control into the HuBotVerse framework. Utilizing the capabilities of Large Language Models (LLMs), such as GPT3.5 Turbo, robots can now comprehend users' spoken instructions. Furthermore, by transmitting audio information to ChatGPT, the system formulates a response containing Python code commands for robot movement. Once these codes are executed, users can effectively direct the robots.

More specifically, we have embedded Speech-to-Text and Text-to-Speech functions into our proposed framework, leveraging Azure Cognitive Services and AudioSource components. The procedure commences with users voicing commands, which are then recorded. This audio information is subsequently transcribed into text format. Following this, the content of the transcribed text undergoes processing via the APIs of GPT3.5. 

Before applying these APIs, we need to tune parameters, such as temperature, max tokens, presence penalty, etc.  The parameter tuning and configuration of prompts essentially tailor the model's response generation. To facilitate the easy integration of new robotic systems, we can produce a Python file for robot repositioning using ChatGPT based on pre-established prompts.