Preprint has been submitted for publication in journal
Preprint / Version 1

A webcam-based machine learning approach for the three-dimensional range of motion evaluation

##article.authors##

DOI:

https://doi.org/10.51224/SRXIV.219

Keywords:

Range of motion, Physical therapy, Rehabilitation, Machine learning, Computer vision, Pose estimation

Abstract

Joint range of motion (ROM) is an important quantitative measure for physical therapy. Commonly relying on a goniometer, accurate and reliable ROM measurement requires extensive training and practice. This, in turn, imposes a significant barrier for those who have limited in-person access to healthcare. The current study presents and evaluates an alternative machine learning-based ROM evaluation method that could be remotely accessed via a webcam. To evaluate its reliability, the ROM measurements for a diverse set of joints (neck, spine, and upper and lower extremities) derived using this method were compared to those obtained from a state-of-the-art marker-based, optical motion capture system. Results showed that the webcam-based solution provides high test-retest reliability and inter-rater reliability at a fraction of the cost of the marker-based system. More importantly, the machine-learning-based method has been shown to be more consistent in tracking joint positions during movements, making it more reliable than the optical motion capture system. The proposed webcam-based ROM evaluation method could be easily adapted for clinical practice and shows tremendous potential for the tele-implementation of physical therapy and rehabilitation.

References

Garfan S, Alamoodi AH, Zaidan B, et al. Telehealth utilization during the Covid-19 pandemic: A systematic review. Comput Biol Med. 2021;138:104878.

Snoswell CL, Chelberg G, De Guzman KR, et al. The clinical effectiveness of telehealth: a systematic review of meta-analyses from 2010 to 2019. J Telemed Telecare. Published online 2021:1-16.

Tsou C, Robinson S, Boyd J, et al. Effectiveness of Telehealth in Rural and Remote Emergen-cy Departments: Systematic Review. J Med Internet Res. 2021;23(11):e30632. doi:10.2196/30632

Muir SW, Corea CL, Beaupre L. Evaluating change in clinical status: reliability and measures of agreement for the assessment of glenohumeral range of motion. North Am J Sports Phys Ther NAJSPT. 2010;5(3):98.

Mullaney MJ, McHugh MP, Johnson CP, Tyler TF. Reliability of shoulder range of motion comparing a goniometer to a digital level. Physiother Theory Pract. 2010;26(5):327-333.

Riddle DL, Rothstein JM, Lamb RL. Goniometric reliability in a clinical setting: shoulder measurements. Phys Ther. 1987;67(5):668-673.

Rothstein JM, Miller PJ, Roettger RF. Goniometric reliability in a clinical setting: elbow and knee measurements. Phys Ther. 1983;63(10):1611-1615.

van de Pol RJ, van Trijffel E, Lucas C. Inter-rater reliability for measurement of passive physiological range of motion of upper extremity joints is better if instruments are used: a sys-tematic review. J Physiother. 2010;56(1):7-17.

Meislin MA, Wagner ER, Shin AY. A comparison of elbow range of motion measurements: smartphone-based digital photography versus goniometric measurements. J Hand Surg. 2016;41(4):510-515.

Li R, Jiang Q. A photogrammetric method for the measurement of three-dimensional cervical range of motion. IEEE J Biomed Health Inform. Published online 2021.

Reese NB, Bandy WD. Joint Range of Motion and Muscle Length Testing-E-Book. Elsevier Health Sciences; 2016.

Kolber MJ, Hanney WJ. The reliability and concurrent validity of shoulder mobility measure-ments using a digital inclinometer and goniometer: a technical report. Int J Sports Phys Ther. 2012;7(3):306-313.

Pérez-de la Cruz S, de León ÓA, Mallada NP, Rodríguez AV. Validity and intra-examiner reliability of the Hawk goniometer versus the universal goniometer for the measurement of range of motion of the glenohumeral joint. Med Eng Phys. 2021;89:7-11.

Dent Jr PA, Wilke B, Terkonda S, Luther I, Shi GG. Validation of teleconference-based goniometry for measuring elbow joint range of motion. Cureus. 2020;12(2).

Cejnog LWX, Cesar RM, de Campos TE, Elui VMC. Hand range of motion evaluation for Rheumatoid Arthritis patients. In: 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019). IEEE; 2019:1-5.

Feng M, Liang L, Sun W, et al. Measurements of cervical range of motion using an optical motion capture system: Repeatability and validity. Exp Ther Med. 2019;18(6):4193-4202.

Nagymáté G, Kiss RM. Application of OptiTrack motion capture systems in human movement analysis: A systematic literature review. Recent Innov Mechatron. 2018;5(1.):1-9.

Furtado JS, Liu HH, Lai G, Lacheray H, Desouza-Coelho J. Comparative analysis of optitrack motion capture systems. In: Advances in Motion Sensing and Control for Robotic Applications. Springer; 2019:15-31.

Toshev A, Szegedy C. Deeppose: Human pose estimation via deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. ; 2014:1653-1660.

Cao Z, Simon T, Wei SE, Sheikh Y. Realtime multi-person 2d pose estimation using part affinity fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. ; 2017:7291-7299.

Mehta D, Sridhar S, Sotnychenko O, et al. Vnect: Real-time 3d human pose estimation with a single rgb camera. ACM Trans Graph TOG. 2017;36(4):1-14.

Wang J, Tan S, Zhen X, et al. Deep 3D human pose estimation: A review. Comput Vis Image Underst. 2021;210:103225. doi:10.1016/j.cviu.2021.103225

Lugaresi C, Tang J, Nash H, et al. Mediapipe: A framework for building perception pipelines. ArXiv Prepr ArXiv190608172. Published online 2019.

Bazarevsky V, Grishchenko I, Raveendran K, Zhu T, Zhang F, Grundmann M. BlazePose: On-device Real-time Body Pose tracking. CoRR. 2020;abs/2006.10204. https://arxiv.org/abs/2006.10204

Kulikajevas A, Maskeliūnas R, Damaševičius R, et al. Exercise Abnormality Detection Using BlazePose Skeleton Reconstruction. In: International Conference on Computational Science and Its Applications. Springer; 2021:90-104.

Mohammed SW, Garrapally V, Manchala S, Reddy SN, Naligenti SK. Recognition of Yoga Asana from Real-Time Videos using Blaze-pose. Int J Comput Digit Syst. 2022;5(3).

Rojas-Arce JL, Jimenez-Angeles L, Marmolejo-Saucedo JA. Telerehabilitation Prototype for Postural Disorder Monitoring in Parkinson Disease. In: International Conference on Comput-er Science and Health Engineering. Springer; 2021:129-142.

Singha RG, Lad M, Shipurkar GM, Rohekar A, Chauhan C, Rathod N. Dynamic Pose Diagnosis with BlazePose and LSTM for Spinal Dysfunction Risk Estimation. In: 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE; 2022:1547-1552.

Bradski G. The OpenCV Library. Dr Dobbs J Softw Tools. Published online 2000.

Kluwak K, Klempous R, Chaczko Z, Rozenblit JW, Kulbacki M. People Lifting Patterns—A Reference Dataset for Practitioners. Sensors. 2021;21(9):3142. doi:10.3390/s21093142

Gallivan JP, Chapman CS. Three-dimensional reach trajectories as a probe of real-time deci-sion-making between multiple competing targets. Front Neurosci. 2014;8. Accessed July 13, 2022. https://www.frontiersin.org/articles/10.3389/fnins.2014.00215

Ramsay JO, Silverman BW. Functional Data Analysis. İnternet Adresi Http. Published online 2008.

McGraw KO, Wong SP. Forming inferences about some intraclass correlation coefficients. Psychol Methods. 1996;1(1):30.

Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15(2):155-163.

Wolak ME, Fairbairn DJ, Paulsen YR. Guidelines for Estimating Repeatability. Methods Ecol Evol. 2012;3(1):129-137.

Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. Published online 1977:159-174.

Stratford PW, Goldsmith CH. Use of the standard error as a reliability index of interest: an applied example using elbow flexor strength data. Phys Ther. 1997;77(7):745-750.

Hall T, Briffa K, Hopper D, Robinson K. Long-term stability and minimal detectable change of the cervical flexion-rotation test. J Orthop Sports Phys Ther. 2010;40(4):225-229.

Perez-Grau F, Ragel R, Caballero F, Viguria A, Ollero A. Semi-autonomous teleoperation of UAVs in search and rescue scenarios. In: 2017 International Conference on Unmanned Air-craft Systems (ICUAS). IEEE; 2017:1066-1074.

Downloads

Posted

2022-11-14