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




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


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.


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.

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.

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.