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035 a| (Sirsi) o1332541672;

035 a| (OCoLC)1332541672;

049 a| EREE;

090 a| R859.7.C67;

100 1 a| Hall, Linwood Earl,c| Jr.,e| author.?| UNAUTHORIZED;

245 10 a| Human organ real-time localization using HTC Vive tracking system and machine learning models /c| by Linwood Earl Hall, Jr.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2022.;

300 a| 1 online resource (49 pages) :b| illustrations (chiefly color);

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text file;

347 b| PDF;

347 c| 8.361 MB;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| M.S.c| East Carolina Universityd| 2022;

500 a| Presented to the Faculty of the Department of Computer Science;

500 a| Advisor: Rui Wu;

500 a| Title from PDF t.p. (viewed October 19, 2023).;

520 3 a| 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.;

504 a| Includes bibliographical references.;

650 0 a| Virtual reality in medicine.=| ^A567855;

650 0 a| Tracking (Engineering)=| ^A1361100;

650 0 a| Machine learning.=| ^A145211;

653 a| HTC Vive;

655 7 a| dissertations.2| aat0| (CStmoGRI)aatgf300028029;

655 7 a| Academic theses.2| fast0| (OCoLC)fst01726453;

655 7 a| Academic theses.2| lcgft;

655 7 a| Thèses et écrits académiques.2| rvmgf0| (CaQQLa)RVMGF-000001173;

700 1 a| Wu, Rui,e| degree supervisor.=| ^A1412862;

710 2 a| East Carolina University.b| Department of Computer Science.?| UNAUTHORIZED;

856 40 u| http://hdl.handle.net/10342/10704z| Access via ScholarShip;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 5831717;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5831717-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 6/23/2022x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5831717-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 6/23/2022x| ETDz| HERESOURCE;

Human organ real-time localization using HTC Vive tracking system and machine learning models / by Linwood Earl Hall, Jr.

Author/creator	Hall, Linwood Earl author.
Other author	Wu, Rui, degree supervisor.
Other author	East Carolina University. Department of Computer Science.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2022.
Description	1 online resource (49 pages) : illustrations (chiefly color)
Supplemental Content	Access via ScholarShip
Subjects	Virtual reality in medicine. Tracking (Engineering) Machine learning.

Summary	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.
General note	Presented to the Faculty of the Department of Computer Science
General note	Advisor: Rui Wu
General note	Title from PDF t.p. (viewed October 19, 2023).
Dissertation note	M.S. East Carolina University 2022
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.
Genre/form	dissertations.
Genre/form	Academic theses.
Genre/form	Academic theses.
Genre/form	Thèses et écrits académiques.

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Human organ real-time localization using HTC Vive tracking system and machine learning models / by Linwood Earl Hall, Jr.

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