Localization of Human Organs with VR Tracking System and Machine Learning Techniques for Medical Purpose

Lin Hall, Rui Wu, Shawn Moore, Andrew Ju, Richard T Dalyai, Zhen Zhu

Peer Reviewed

Abstract:	Virtual reality and machine learning technologies have become focal points for research and development for medical studies in recent years. However, previous studies do not typically use virtual reality and machine learning in tandem. In this study, we propose a framework utilizing both virtual reality and machine learning to predict the localization of human organs in real-time. The HTC Vive Pro virtual reality system, while used originally for entertainment, is a viable, low-cost option for studies requiring precise measurements. Data collected by the virtual reality system is used as inputs for machine learning models for predictions of human organ localization in real-time. Further, data enhancement methods, such as data normalization and extreme event split, are leveraged to improve machine learning model performance. According to our experimental results, the gradient boosting regressor model performs accurately for almost every direction for either of the two tracker configurations, i.e., linear and triangular. The extreme event split can also improve machine learning performance, especially with rotational data. Overall, this framework is promising to be used as the localization basis for other surgical procedures, as well as other human organs.
Published in:	Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022) September 19 - 23, 2022 Hyatt Regency Denver Denver, Colorado
Pages:	1840 - 1848
Cite this article:	Hall, Lin, Wu, Rui, Moore, Shawn, Ju, Andrew, Dalyai, Richard T, Zhu, Zhen, "Localization of Human Organs with VR Tracking System and Machine Learning Techniques for Medical Purpose," Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022), Denver, Colorado, September 2022, pp. 1840-1848. https://doi.org/10.33012/2022.18507
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