WH2014 Session: Motion assessment for robotic surgery education using inertial body sensors

WLSACONVERGENCE SUMMIT

MOTION ASSESSMENT FOR ROBOTIC SURGERY EDUCATION USING INERTIAL BODY SENSORS

JIAQI GONG, JOHN LACHCHARLES L. BROWN DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERINGUNIVERSITY OF VIRGINIA

MOTION ASSESSMENT FOR ROBOTIC SURGERY EDUCATION USING

INERTIAL BODY SENSORS

Wireless Health, October 30th, 2014

Jiaqi Gong, John Lach

Charles L. Brown Department of Electrical and

Computer EngineeringUniversity of Virginia

Yanjun QiDepartment of

Computer ScienceUniversity of Virginia

Rebecca S. Zee, Sierra J. Seaman,

Noah S. Schenkman

Department of UrologyUniversity of Virginia

UVA CENTER FORWIRELESS HEALTH

Background and Purpose

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Score

Data

Feedback

Methods: (Modeling and Clustering)

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1. Segmentation based on 3D Curvature-Gradient Invariant to human factors during the mounting process2. Feature Extraction based on Control System Theory Invariant to observation variances during the sensing process3. Intra-subject Clustering Discover the motion pieces in each time-series data4. Inter-subject Clustering based on Dictionary Method and Prior Knowledge Discover the motion patterns which can be used to differentiate experts and novices

Results

Body-worn inertial sensors capturing motion and posture data provide better surgical skills assessments and more specific feedback than simulator performance alone. P Value Robotic

SimulatorInertial BSN

EIF P<0.5 P<0.05

EOM P<0.05 P<0.05

NSMP N/A P<0.0001

Overall P<0.05 N/A

Contact Us

Contact us with any question you have Jiaqi Gong (JGONG@VIRGINIA.EDU)

UVA CENTER FORWIRELESS HEALTH

WLSACONVERGENCE SUMMIT

www.wirelesshealth2014.org