Accuracy of key point matrix technology based contactless automatic measurement for joint motion of hand_Chinese Journal of Reparative and Reconstructive Surgery

Authors：

LÜ Lulu ¹ , YANG Jiantao ^1,2,3 , GU Fanbin ¹ , FAN Jingyuan ¹ , WANG Chaoyang ¹ ,  ZHU Qingtang ^1,2,3 ,  LIU Xiaolin ^1,2,3

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;

Corresponding author：

ZHU Qingtang, Email: zhuqingt@mail.sysu.edu.cn; LIU Xiaolin, Email: gzxiaolinliu@hotmail.com

Keywords：

Finger; joint motion; contactless automatic evaluation; key point matrix technology

DOI：

10.7507/1002-1892.202201078

Video：

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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.

Citation： LÜ Lulu, YANG Jiantao, GU Fanbin, FAN Jingyuan, WANG Chaoyang, ZHU Qingtang, LIU Xiaolin. Accuracy of key point matrix technology based contactless automatic measurement for joint motion of hand. Chinese Journal of Reparative and Reconstructive Surgery, 2022, 36(5): 540-547. doi: 10.7507/1002-1892.202201078 Copy

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1. Rondinelli RD, Genovese E, Katz RT, et al. AMA guides^® to the evaluation of permanent impairment. 6th ed. USA: American Medical Association, 2008: 432-456.
2. Swanson AB, Hagert CG, Swanson GD. Evaluation of impairment of hand function. J Hand Surg (Am), 1983, 8(5 Pt 2): 709-722.
3. Duncan SF, Saracevic CE, Kakinoki R. Biomechanics of the hand. Hand Clin, 2013, 29(4): 483-492.
4. Dipietro L, Sabatini AM, Dario P. A survey of glove-based systems and their applications. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2008, 38(4): 461-482.
5. Ellis B, Bruton A. A study to compare the reliability of composite finger flexion with goniometry for measurement of range of motion in the hand. Clin Rehabil, 2002, 16(5): 562-570.
6. Fang Q, Mahmoud SS, Gu X, et al. A novel multistandard compliant hand function assessment method using an infrared imaging device. IEEE J Biomed Health Inform, 2019, 23(2): 758-765.
7. Chen W, Yu C, Tu C, et al. A survey on hand pose estimation with wearable sensors and computer-vision-based methods. Sensors (Basel), 2020, 20(4): 1074. doi: 10.3390/s20041074.
8. Henderson J, Condell J, Connolly J, et al. Reliability and validity of clinically accessible smart glove technologies to measure joint range of motion. Sensors (Basel), 2021, 21(5): 1555. doi: 10.3390/s21051555.
9. Arman N, Oktay AB, Tarakci D, et al. The validity of an objective measurement method using the Leap Motion Controller for fingers wrist, and forearm ranges of motion. Hand Surg Rehabil, 2021, 40: 394-399.
10. Zabatani A, Surazhsky V, Sperling E, et al. Intel^® RealSense^TM SR300 Coded Light Depth Camera. IEEE Trans Pattern Anal Mach Intell, 2020, 42(10): 2333-2345.
11. Ahad MAR, Mahbub U, Rahman T. Contactless human activity analysis. Switzerland: Springer Nature, 2021: 43-81.
12. Gustus A, Stillfried G, Visser J, et al. Human hand modelling: kinematics, dynamics, applications. Biol Cybern, 2012, 106(11-12): 741-755.
13. Colombini G, Duradoni M, Carpi F, et al. LEAP motion technology and psychology: A mini-review on hand movements sensing for neurodevelopmental and neurocognitive disorders. Int J Environ Res Public Health, 2021, 18(8): 4006. doi: 10.3390/ijerph18084006.
14. Cave EF, Roberts SM. A method for measuring and recording joint function. J Bone Joint Surg, 1936, (18): 455-465.
15. Hass JR, Heil CE, Weir MD. Thomas calculus early transcendentals. 14th ed. Boston: Pearson, 2018: 714-762.
16. Swanson AB, Hagert CG, Swanson GD. Evaluation of impairment of hand function. J Hand Surg (Am), 1983, 8(5 Pt 2): 709-722.
17. Clinical and Laboratory Standards Institute. Evaluation of Precision of Quantitative Measurement Procedures; Approved Guidline-Third Edition. EPO5-A3. Wayne, PA. : Clinical and Laboratory Standards Institute, 2014: 1-69.
18. Clinical and Laboratory Standards Institute. User Vertification of performance for Precision and Trueness; Approved Guidline-Second Edition. Wayne, PA. : Clinical and Laboratory Standards Institute, 2008: 1-48.
19. Pham T, Pathirana PN, Trinh H, et al. A non-contact measurement system for the range of motion of the hand. Sensors (Basel), 2015, 15(8): 18315-18333.
20. Norkin CC, White DJ. Measurement of joint motion: a guide to goniometry. Philadelphia: FA Davis, 2016: 187-252.
21. Zhao JZ, Blazar PE, Mora AN, et al. Range of motion measurements of the fingers via smartphone photography. Hand (N Y), 2020, 15(5): 679-685.
22. Seo NJ, Fathi MF, Hur P, et al. Modifying Kinect placement to improve upper limb joint angle measurement accuracy. J Hand Ther, 2016, 29(4): 465-473.
23. Guna J, Jakus G, Pogačnik M, et al. An analysis of the precision and reliability of the leap motion sensor and its suitability for static and dynamic tracking. Sensors (Basel), 2014, 14(2): 3702-3720.
24. Hume MC, Gellman H, Mckellop H, et al. Functional range of motion of the joints of the hand. J Hand Surg, 1990, 15(2): 240-243.
25. Nizamis K, Rijken NHM, Mendes A, et al. A novel setup and protocol to measure the range of motion of the wrist and the hand. Sensors (Basel), 2018, 18(10): 3230. doi: 10.3390/s18103230.

Chinese Journal of Reparative and Reconstructive Surgery

Accuracy of key point matrix technology based contactless automatic measurement for joint motion of hand

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