WH2014 Session: Unsupervised head impact identification using inertial body sensors based on linear...
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WLSA CONVERGENCE SUMMIT UNSUPERVISED HEAD IMPACT IDENTIFICATION USING INERTIAL BODY SENSORS BASED ON LINEAR DYNAMICAL MODEL JIAQI GONG, JOHN LACH CHARLES L. BROWN DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING UNIVERSITY OF VIRGINIA
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From Wireless Health 2014 Demo and Abstract Presentation Session 2, featuring speaker Jiaqi Gong.
Transcript of WH2014 Session: Unsupervised head impact identification using inertial body sensors based on linear...
WLSACONVERGENCE SUMMIT
UNSUPERVISED HEAD IMPACT IDENTIFICATION USING INERTIAL BODY SENSORS BASED ON LINEAR DYNAMICAL MODEL JIAQI GONG, JOHN LACH
CHARLES L. BROWN DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERINGUNIVERSITY OF VIRGINIA
UNSUPERVISED HEAD IMPACT IDENTIFICATION USING INERTIAL BODY SENSORS BASED ON
LINEAR DYNAMICAL MODEL
Wireless Health, October 30th, 2014
Jiaqi Gong, Sriram Raju Dandu, John
LachCharles L. Brown Department of Electrical and
Computer EngineeringUniversity of Virginia
Bryson ReynoldsDepartment of Neuroscience
University of Virginia
Jason DruzgalDepartment of
Radiology and Medical Imaging
University of Virginia
UVA CENTER FORWIRELESS HEALTH
Neurological Studies on Head Impact
MRI ScanMRI Scan Game Season
Correlation between head Impacts during the games and the MRI changes
Goal and Method
Providing more representative data sets for neurological studies
Proof of Concept
Athletes from 5 collegiate and 1 high school teams wore
impact sensing skin patches on the skin covering their mastoid process during all
official practices and games. Identifying Features
Modeling, feature extraction and
clustering
Purpose Method
Aim to provide an accurate unsupervised method to
identify the true head impact data from the raw inertial data
Results
In order to validate the accuracy of the proposed method, we manually labeled 14,176 data sessions (9185 positive and 4991 negative) to compare the manual labels and unsupervised classifications.
Using the manual labels as reference, the proposed unsupervised method achieved 85% accuracy, 88% sensitivity, 80% specificity, and 89% precision.PERCENTAGE Identified as Real
ImpactsIdentified as Fake Impacts
Real Impacts 88.5 11.5
Fake Impacts 19.5 80.5
SAMPLES Identified as Real Impacts
Identified as Fake Impacts
Real Impacts (9185 sessions)
8125 1060
Fake Impacts(4991 sessions)
973 4018
Contact Us
Contact us with any question you have Jiaqi Gong ([email protected]) Sriram Raju Dandu, ([email protected])
UVA CENTER FORWIRELESS HEALTH
WLSACONVERGENCE SUMMIT
www.wirelesshealth2014.org
