Lulu Lü1, Jiantao Yang1,2,3, Fanbin Gu1, Jingyuan Fan1, Chaoyang Wang1, Qingtang Zhu1,2,3, Xiaolin Liu1,2,3. 1. Department of Microsurgery, Orthopedic Trauma and Hand Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou Guangdong, 510080, P. R. China. 2. Guangdong Provincial Center for Peripheral Nerve Tissue Engineering and Technology Research, Guangzhou Guangdong, 510080, P. R. China. 3. Guangdong Province Engineering Laboratory for Soft Tissue Biofabrication, Guangzhou Guangdong, 510080, P. R. China.
Abstract
Objective: To validate the use of key point matrix technology based contactless automatic measurement for evaluation of joint motion of hand. Methods: Thirty-three volunteers were enrolled to evaluate the extension and flexion of hand joints between May 2021 and November 2021. There were 20 males and 13 females, the age ranged from 16 to 70 years with an average of 30.2 years. The extension angles of 14 joints of 5 fingers (including hyperextension) and the flexion angles of 12 joints of 4 fingers (excluding thumb) of volunteers were measured by key point matrix technology and manual goniometer, respectively. Then 5 participants and repeated measurement experiment were employed to test the system repeatability and accuracy; 28 participants and paired measurement experiment were employed to test the system accuracy. Results: The average repeatability of finger joint motion measured by the key point matrix technology was 1.801° (extension) and 7.823° (flexion), respectively. Compared with manual measurement, the average differences of each finger joint measured by the key point matrix technology were 3.225° in extension and 14.145° in flexion, respectively. The key point matrix technology based contactless automatic evaluation system offered excellent consistency with the manual goniometers ( ICC=0.875). While most of the consistency with manual goniometer of individual joints were at moderate levels (median of ICC, 0.440). The correlation coefficients between the measurement results of the two methods were mainly positive in the extension of the joint ( P<0.05) and negative in the flexion of the joints ( P<0.05). Conclusion: The key point matrix technology based contactless automatic evaluation provides sufficient measurement repeatability and accuracy in evaluation for the joint motion of hand.
Objective: To validate the use of key point matrix technology based contactless automatic measurement for evaluation of joint motion of hand. Methods: Thirty-three volunteers were enrolled to evaluate the extension and flexion of hand joints between May 2021 and November 2021. There were 20 males and 13 females, the age ranged from 16 to 70 years with an average of 30.2 years. The extension angles of 14 joints of 5 fingers (including hyperextension) and the flexion angles of 12 joints of 4 fingers (excluding thumb) of volunteers were measured by key point matrix technology and manual goniometer, respectively. Then 5 participants and repeated measurement experiment were employed to test the system repeatability and accuracy; 28 participants and paired measurement experiment were employed to test the system accuracy. Results: The average repeatability of finger joint motion measured by the key point matrix technology was 1.801° (extension) and 7.823° (flexion), respectively. Compared with manual measurement, the average differences of each finger joint measured by the key point matrix technology were 3.225° in extension and 14.145° in flexion, respectively. The key point matrix technology based contactless automatic evaluation system offered excellent consistency with the manual goniometers ( ICC=0.875). While most of the consistency with manual goniometer of individual joints were at moderate levels (median of ICC, 0.440). The correlation coefficients between the measurement results of the two methods were mainly positive in the extension of the joint ( P<0.05) and negative in the flexion of the joints ( P<0.05). Conclusion: The key point matrix technology based contactless automatic evaluation provides sufficient measurement repeatability and accuracy in evaluation for the joint motion of hand.
Authors: Aviad Zabatani; Vitaly Surazhsky; Erez Sperling; Sagi Ben Moshe; Ohad Menashe; David H Silver; Tzachi Karni; Alexander M Bronstein; Michael M Bronstein; Ron Kimmel Journal: IEEE Trans Pattern Anal Mach Intell Date: 2019-05-10 Impact factor: 6.226
Authors: Agneta Gustus; Georg Stillfried; Judith Visser; Henrik Jörntell; Patrick van der Smagt Journal: Biol Cybern Date: 2012-11-07 Impact factor: 2.086