TY - GEN
T1 - Video-Based Hand Tracking for Screening Cervical Myelopathy
AU - Matsui, Ryota
AU - Koyama, Takafumi
AU - Fujita, Koji
AU - Saito, Hideo
AU - Sugiura, Yuta
N1 - Funding Information:
This work was supported by JST PRESTO Grant Number JPMJPR17J4 and JST AIP-PRISM Grant Number JPMJCR18Y2.
Funding Information:
Acknowledgements. This work was supported by JST PRESTO Grant JPMJPR17J4 and JST AIP-PRISM Grant Number JPMJCR18Y2.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Cervical myelopathy (CM) is a pathology of the spinal cord that causes upper limb disorders. CM is often screened by conducting the 10-s grip and release (G&R) test, which mainly focuses on hand dysfunction caused by CM. This test has patients repeat gripping and releasing their hands as quickly as possible. Spine surgeons observe the quickness of this repetition to screen for CM. We propose an automatic screening method of CM that involves patients’ hands recorded as videos when they are performing the G&R test. The videos are used to estimate feature values, i.e., the positions of each part of the hand, which are obtained through image processing. A support vector machine classifier classifies CM patients and controls with these feature values after pre-processing. We validated our method with 10-fold cross-validation and the videos of 20 CM patients and 15 controls. The results indicate that sensitivity, specificity, and area under the receiver operating characteristic curve were 90.0 %, 93.3 %, and 0.947, respectively.
AB - Cervical myelopathy (CM) is a pathology of the spinal cord that causes upper limb disorders. CM is often screened by conducting the 10-s grip and release (G&R) test, which mainly focuses on hand dysfunction caused by CM. This test has patients repeat gripping and releasing their hands as quickly as possible. Spine surgeons observe the quickness of this repetition to screen for CM. We propose an automatic screening method of CM that involves patients’ hands recorded as videos when they are performing the G&R test. The videos are used to estimate feature values, i.e., the positions of each part of the hand, which are obtained through image processing. A support vector machine classifier classifies CM patients and controls with these feature values after pre-processing. We validated our method with 10-fold cross-validation and the videos of 20 CM patients and 15 controls. The results indicate that sensitivity, specificity, and area under the receiver operating characteristic curve were 90.0 %, 93.3 %, and 0.947, respectively.
KW - Cervical myelopathy
KW - Gesture classification
KW - Hand tracking
KW - Medical image processing
UR - http://www.scopus.com/inward/record.url?scp=85121901110&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85121901110&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-90436-4_1
DO - 10.1007/978-3-030-90436-4_1
M3 - Conference contribution
AN - SCOPUS:85121901110
SN - 9783030904357
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 14
BT - Advances in Visual Computing - 16th International Symposium, ISVC 2021, Proceedings
A2 - Bebis, George
A2 - Athitsos, Vassilis
A2 - Yan, Tong
A2 - Lau, Manfred
A2 - Li, Frederick
A2 - Shi, Conglei
A2 - Yuan, Xiaoru
A2 - Mousas, Christos
A2 - Bruder, Gerd
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th International Symposium on Visual Computing, ISVC 2021
Y2 - 4 October 2021 through 6 October 2021
ER -