1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
Green, S.; Buchbinder, R.; Glazier, R.; Forbes, A. Systematic review of randomized controlled trials of interventions for painful
shoulder: Selection criteria, outcome assessment, and efficacy. BMJ 1998, 316, 354–360. [CrossRef]
Muir, S.W.; Corea, C.L.; Beaupre, L. Evaluating change in clinical status: Reliability and measures of agreement for the assessment
of glenohumeral range of motion. N. Am. J. Sports Phys. Ther. 2010, 5, 98–110. [PubMed]
Vecchio, P.C.; Kavanagh, R.T.; Hazleman, B.L.; King, R.H. Community survey of shoulder disorders in the elderly to assess the
natural history and effects of treatment. Ann. Rheum. Dis. 1995, 54, 152–154. [CrossRef]
Milanese, S.; Gordon, S.J.; Buettner, P.; Flavell, C.; Ruston, S.; Coe, D.; O’Sullivan, W.; McCormack, S. Reliability and concurrent
validity of knee angle measurement: Smart phone app versus universal goniometer used by experienced and novice clinicians.
Man. Ther. 2014, 19, 569–574. [CrossRef] [PubMed]
Brosseau, L.; Tousignant, M.; Budd, J.; Chartier, N.; Duciaume, L.; Plamondon, S.; O’Sullivan, J.P.; O’Donoghue, S.; Balmer, S.
Intratester and intertester reliability and criterion validity of the parallelogram and universal goniometers for active knee flexion
in healthy subjects. Physiother. Res. Int. 1997, 2, 150–166. [CrossRef]
El-Zayat, B.F.; Efe, T.; Heidrich, A.; Wolf, U.; Timmesfeld, N.; Heyse, T.J.; Lakemeier, S.; Fuchs-Winkelmann, S.; Schofer, M.D.
Objective assessment of shoulder mobility with a new 3D gyroscope—A validation study. BMC Musculoskelet Disord. 2011, 12, 168.
[CrossRef]
El-Zayat, B.F.; Efe, T.; Heidrich, A.; Anetsmann, R.; Timmesfeld, N.; Fuchs-Winkelmann, S.; Schofer, M.D. Objective assessment,
repeatability, and agreement of shoulder ROM with a 3D gyroscope. BMC Musculoskelet Disord. 2013, 14, 72. [CrossRef] [PubMed]
Cheng, S.Y.; Trivedi, M.M. Human posture estimation using voxel data for “Smart” airbag systems: Issues and framework. In
Proceedings of the IEEE Intelligent Vehicles Symposium, Parma, Italy, 14–17 June 2004; pp. 84–89.
Yahya, M.; Shah, J.A.; Kadir, K.A.; Yusof, Z.M.; Khan, S.; Warsi, A. Motion capture sensing techniques used in human upper limb
motion: A review. Sensor Rev. 2019, 39, 504–514. [CrossRef]
Wu, G.; van der Helm, F.C.T.; Veeger, H.E.J.; Makhsous, M.; Van Roy, P.; Anglin, C.; Nagels, J.; Karduna, A.R.; McQuade, K.;
Wang, X.; et al. ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint
motion—Part II: Shoulder, elbow, wrist and hand. J. Biomech. 2005, 38, 981–992. [CrossRef]
Picerno, P. 25 years of lower limb joint kinematics by using inertial and magnetic sensors: A review of methodological approaches.
Gait Posture. 2017, 51, 239–246. [CrossRef]
Parel, I.; Cutti, A.G.; Fiumana, G.; Porcellini, G.; Verni, G.; Accardo, A.P. Ambulatory measurement of the scapulohumeral
rhythm: Intra- and inter-operator agreement of a protocol based on inertial and magnetic sensors. Gait Posture. 2012, 35, 636–640.
[CrossRef]
Yamaura, K.; Mifune, Y.; Inui, A.; Nishimoto, H.; Kataoka, T.; Kurosawa, T.; Mukohara, S.; Hoshino, Y.; Niikura, T.; Nagamune, K.;
et al. Accuracy and reliability of tridimensional electromagnetic sensor system for elbow ROM measurement. J. Orthop. Surg. Res.
2022, 17, 60. [CrossRef]
Mukohara, S.; Mifune, Y.; Inui, A.; Nishimoto, H.; Kurosawa, T.; Yamaura, K.; Yoshikawa, T.; Shinohara, I.; Hoshino, Y.; Nagamune,
K.; et al. A new quantitative evaluation system for distal radioulnar joint instability using a three-dimensional electromagnetic
sensor. J. Orthop. Surg. Res. 2021, 16, 452. [CrossRef]
Morton, N.A.; Maletsky, L.P.; Pal, S.; Laz, P.J. Effect of variability in anatomical landmark location on knee kinematic description.
J. Orthop. Res. 2007, 25, 1221–1230. [CrossRef] [PubMed]
Faisal, A.I.; Majumder, S.; Mondal, T.; Cowan, D.; Naseh, S.; Deen, M.J. Monitoring Methods of Human Body Joints: State-of-theArt and Research Challenges. Sensors 2019, 19, 2629. [CrossRef] [PubMed]
Stenum, J.; Cherry-Allen, K.M.; Pyles, C.O.; Reetzke, R.D.; Vignos, M.F.; Roemmich, R.T. Applications of Pose Estimation in
Human Health and Performance across the Lifespan. Sensors 2021, 21, 7315. [CrossRef]
Toshev, A.; Szegedy, C. DeepPose: Human pose estimation via deep neural networks. In Proceedings of the IEEE Computer
Society Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 23–28 June 2014; pp. 1653–1660.
Pishchulin, L.; Insafutdinov, E.; Tang, S.; Andres, B.; Andriluka, M.; Gehler, P.; Schiele, B. DeepCut: Joint Subset Partition and
Labeling for Multi Person Pose Estimation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and
Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 4929–4937.
Insafutdinov, E.; Andriluka, M.; Pishchulin, L.; Tang, S.; Levinkov, E.; Andres, B.; Schiele, B. ArtTrack: Articulated Multi-Person
Tracking in the Wild. In Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI,
USA, 21–26 July 2017; pp. 6457–6465.
Sensors 2023, 23, 6445
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
15 of 15
Martinez, G.H.; Raaj, Y.; Idrees, H.; Xiang, D.; Joo, H.; Simon, T.; Sheikh, Y. Single-network whole-body pose estimation. In
Proceedings of the IEEE International Conference on Computer Vision, Seoul, Republic of Korea, 27 October–2 November 2019;
pp. 6982–6991.
Drazan, J.F.; Phillips, W.T.; Seethapathi, N.; Hullfish, T.J.; Baxter, J.R. Moving outside the lab: Markerless motion capture accurately
quantifies sagittal plane kinematics during the vertical jump. J. Biomech. 2021, 125, 110547. [CrossRef]
Tanaka, R.; Ishikawa, Y.; Yamasaki, T.; Diez, A. Accuracy of classifying the movement strategy in the functional reach test using a
markerless motion capture system. J. Med. Eng. Technol. 2019, 43, 133–138. [CrossRef]
Wochatz, M.; Tilgner, N.; Mueller, S.; Rabe, S.; Eichler, S.; John, M.; Völler, H.; Mayer, F. Reliability and validity of the Kinect V2
for the assessment of lower extremity rehabilitation exercises. Gait. Posture 2019, 70, 330–335. [CrossRef] [PubMed]
Yeung, L.-F.; Yang, Z.; Cheng, K.C.-C.; Du, D.; Tong, R.K.-Y. Effects of camera viewing angles on tracking kinematic gait patterns
using Azure Kinect, Kinect v2 and Orbbec Astra Pro v2. Gait Posture 2021, 87, 19–26. [CrossRef]
Qu, Y.; Hwang, J.; Lee, K.S.; Jung, M.C. The effect of camera location on observation-based posture estimation. Ergonomics 2012,
55, 885–897. [CrossRef]
Bao, S.; Howard, N.; Spielholz, P.; Silverstein, B. Two posture analysis approaches and their application in a modified rapid upper
limb assessment evaluation. Ergonomics 2007, 50, 2118–2136. [CrossRef] [PubMed]
Armitano-Lago, C.; Willoughby, D.; Kiefer, A.W. A SWOT Analysis of Portable and Low-Cost Markerless Motion Capture Systems
to Assess Lower-Limb Musculoskeletal Kinematics in Sport. Front. Sports Act. Living. 2022, 3, 809898. [CrossRef]
Lam, W.W.T.; Tang, Y.M.; Fong, K.N.K. A systematic review of the applications of markerless motion capture (MMC) technology
for clinical measurement in rehabilitation. J. Neuro Eng. Rehabil. 2023, 20, 57. [CrossRef]
Gauci, M.O.; Olmos, M.; Cointat, C.; Chammas, P.E.; Urvoy, M.; Murienne, A.; Bronsard, N.; Gonzalez, J.F. Validation of the
shoulder range of motion software for measurement of shoulder ranges of motion in consultation: Coupling a red/green/bluedepth video camera to artificial intelligence. Int. Orthop. 2023, 47, 299–307. [CrossRef]
Gritsenko, V.; Dailey, E.; Kyle, N.; Taylor, M.; Whittacre, S.; Swisher, A.K. Feasibility of using low-cost motion capture for
automated screening of shoulder motion limitation after breast cancer surgery. PLoS ONE 2015, 10, e0128809. [CrossRef]
Beshara, P.; Chen, J.F.; Read, A.C.; Lagadec, P.; Wang, T.; Walsh, W.R. The Reliability and validity of wearable inertial sensors
coupled with the microsoft kinect to measure shoulder range-of-motion. Sensors 2020, 20, 7238. [CrossRef]
Cao, Z.; Simon, T.; Wei, S.E.; Sheikh, Y. Realtime multi-person 2d pose estimation using part affinity fields. In Proceedings of the
IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 7291–7299.
Bazarevsky, V.; Grishchenko, I.; Raveendran, K.; Zhu, T.; Zhang, F.; Grundmann, M. Blazepose: On-device real-time body pose
tracking. arXiv 2020, arXiv:2006.10204.
Kishore, D.M.; Bindu, S.; Manjunath, N.K. Estimation of Yoga Postures Using Machine Learning Techniques. Int. J. Yoga 2022, 15,
137–143. [CrossRef] [PubMed]
Lafayette, T.B.G.; Kunst, V.H.L.; Melo, P.V.S.; Guedes, P.O.; Teixeira, J.M.X.N.; Vasconcelos, C.R.; Teichrieb, V.; da Gama, A.E.F.
Validation of Angle Estimation Based on Body Tracking Data from RGB-D and RGB Cameras for Biomechanical Assessment.
Sensors 2022, 23, 3. [CrossRef]
Clarkson, H.M. Joint Motion and Function Assessment: A Research Based Practical Guide; Lippincott Williams & Wilkins: Philadelphia,
PA, USA, 2005; pp. 60–61.
Yan, J.; Xu, Y.; Cheng, Q.; Jiang, S.; Wang, Q.; Xiao, Y.; Ma, C.; Yan, J.; Wang, X. LightGBM: Accelerated genomically designed crop
breeding through ensemble learning. Genome. Biol. 2021, 22, 271. [CrossRef] [PubMed]
Ji, P.; Wang, X.; Ma, F.; Feng, J.; Li, C. A 3D Hand Attitude Estimation Method for Fixed Hand Posture Based on Dual-View RGB
Images. Sensors 2022, 22, 8410. [CrossRef] [PubMed]
Fisher, A.; Rudin, C.; Dominici, F. All Models are Wrong, but Many are Useful: Learning a Variable’s Importance by Studying an
Entire Class of Prediction Models Simultaneously. J. Mach. Learn. Res. 2019, 20, 177. [PubMed]
Lundberg, S.M.; Lee, S. A unified approach to interpreting model predictions. In Proceedings of the Advances in Neural
Information Processing Systems 30 (NIPS), Long Beach, CA, USA, 4–9 December 2017.
Chicco, D.; Warrens, M.J.; Jurman, G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE,
MSE and RMSE in regression analysis evaluation. PeerJ Comput. Sci. 2021, 7, e623. [CrossRef]
Fusca, M.; Negrini, F.; Perego, P.; Magoni, L.; Molteni, F.; Andreoni, G. Validation of a wearable IMU system for gait analysis:
Protocol and application to a new system. Appl. Sci. 2018, 8, 1167. [CrossRef]
Sheu, R.K.; Pardeshi, M.S. A Survey on Medical Explainable AI (XAI): Recent Progress, Explainability Approach, Human
Interaction and Scoring System. Sensors 2022, 22, 8068. [CrossRef]
American Academy of Orthopaedic Surgeons. Joint Motion: Method of Measuring and Recording; American Academy of Orthopaedic
Surgeons: Chicago, IL, USA, 1965.
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