TY - GEN
T1 - Kinect v2 accuracy as a body segment measuring tool
AU - Bemal, V. Espinoza
AU - Satterthwaite, N. A.
AU - Napoli, A.
AU - Glass, S. M.
AU - Tucker, C. A.
AU - Obeid, I.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - Mild traumatic brain injury (mTBI) and/or concussion is the leading cause of injury related to recent U.S. military conflicts. Among other symptoms, injured service members often present with impaired balance. Several field-expedient test batteries have been developed to evaluate balance deficits in this context; however, such tests suffer from limitations associated with human observation and scoring. In order to address these limitations, we have developed the Automated Assessment of Postural Stability (AAPS) system, a computerized balance measurement tool which automates the frequently-administered Balance Error Scoring System [1], [2]. The AAPS is based on the Microsoft Kinect v2, a markerless, portable and low-cost alternative to laboratory-grade motion tracking systems. This hardware integrates an array of sensors including an HD camera, infrared, and depth sensors. The Microsoft proprietary skeletal tracking algorithm [3] estimates coordinates for up to 25 body joint centers which are used to recreate a stick model representation of the subject. All tracking data is encapsulated in a 'body frame' stream generated at a variable frame rate of up to 30 fps.
AB - Mild traumatic brain injury (mTBI) and/or concussion is the leading cause of injury related to recent U.S. military conflicts. Among other symptoms, injured service members often present with impaired balance. Several field-expedient test batteries have been developed to evaluate balance deficits in this context; however, such tests suffer from limitations associated with human observation and scoring. In order to address these limitations, we have developed the Automated Assessment of Postural Stability (AAPS) system, a computerized balance measurement tool which automates the frequently-administered Balance Error Scoring System [1], [2]. The AAPS is based on the Microsoft Kinect v2, a markerless, portable and low-cost alternative to laboratory-grade motion tracking systems. This hardware integrates an array of sensors including an HD camera, infrared, and depth sensors. The Microsoft proprietary skeletal tracking algorithm [3] estimates coordinates for up to 25 body joint centers which are used to recreate a stick model representation of the subject. All tracking data is encapsulated in a 'body frame' stream generated at a variable frame rate of up to 30 fps.
UR - http://www.scopus.com/inward/record.url?scp=85050546998&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050546998&partnerID=8YFLogxK
U2 - 10.1109/SPMB.2017.8257050
DO - 10.1109/SPMB.2017.8257050
M3 - Conference contribution
AN - SCOPUS:85050546998
T3 - 2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017 - Proceedings
SP - 1
EP - 3
BT - 2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017
Y2 - 2 December 2017
ER -