TY - GEN
T1 - Video Analysis of Hand Gestures for Distinguishing Patients with Carpal Tunnel Syndrome
AU - Matsui, Ryota
AU - Ibara, Takuya
AU - Tsukamoto, Kazuya
AU - Koyama, Takafumi
AU - Fujita, Koji
AU - Sugiura, Yuta
N1 - Funding Information:
Thiswork was supported by JST PRESTOGrant Number JPMJPR2134
Funding Information:
This work was supported by JST PRESTO Grant Number JPMJPR2134.
Publisher Copyright:
© 2022 ACM.
PY - 2022/11/20
Y1 - 2022/11/20
N2 - Carpal tunnel syndrome (CTS) is a common condition characterized by hand dysfunction due to median nerve compression. Orthopedic surgeons often detect signs of the symptoms to screen for CTS; however, it is difficult to distinguish other diseases with symptoms similar to those of CTS. We previously introduced a method of evaluating fine hand movements to screen for cervical myelopathy (CM). The present work applies this method to screen for CTS, using videos of specific hand gestures to measure their quickness. Machine learning models are used to evaluate the gestures to estimate the probability that a patient has CTS. We cross-validated the models to evaluate our method's effectiveness in screening for CTS. The results showed that the sensitivity and specificity were 90.0% and 85.3%, respectively. Furthermore, we found that our method can also be used to distinguish CTS and CM and may enable earlier detection and treatment of similar neurological diseases.
AB - Carpal tunnel syndrome (CTS) is a common condition characterized by hand dysfunction due to median nerve compression. Orthopedic surgeons often detect signs of the symptoms to screen for CTS; however, it is difficult to distinguish other diseases with symptoms similar to those of CTS. We previously introduced a method of evaluating fine hand movements to screen for cervical myelopathy (CM). The present work applies this method to screen for CTS, using videos of specific hand gestures to measure their quickness. Machine learning models are used to evaluate the gestures to estimate the probability that a patient has CTS. We cross-validated the models to evaluate our method's effectiveness in screening for CTS. The results showed that the sensitivity and specificity were 90.0% and 85.3%, respectively. Furthermore, we found that our method can also be used to distinguish CTS and CM and may enable earlier detection and treatment of similar neurological diseases.
KW - gesture classification
KW - grip and release test
KW - medical application
KW - screening method
UR - http://www.scopus.com/inward/record.url?scp=85146266924&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146266924&partnerID=8YFLogxK
U2 - 10.1145/3532104.3571461
DO - 10.1145/3532104.3571461
M3 - Conference contribution
AN - SCOPUS:85146266924
T3 - ISS 2022 - Companion Proceedings of the 2022 Conference on Interactive Surfaces and Spaces
SP - 27
EP - 31
BT - ISS 2022 - Companion Proceedings of the 2022 Conference on Interactive Surfaces and Spaces
A2 - Anslowx, Craig
A2 - Kay, Judy
PB - Association for Computing Machinery, Inc
T2 - 2022 ACM International Conference on Interactive Surfaces and Spaces, ISS 2022
Y2 - 20 November 2022 through 23 November 2022
ER -