A Novel Augmented Reality Mobile-Based Application for Biomechanical Measurement
Abstract
:1. Introduction
2. BAR-M System Development
2.1. AprilTag
2.2. BAR-M Application Design
2.3. BAR-M Adapter Design
- Flat mount: Square mount with AprilTag on one side and various mounting options on the other side (post-adapter, Velcro, caliper). The plastic mount can be held in square or diamond orientations, with the diamond approach enabling point contact between the mount and the anatomical location.
- Post adapter: Orients the AprilTag normal to a pointed post. The post can be placed on an anatomical landmark, especially for landmarks like the superior iliac spine that can be obscured from the camera.
- Velcro adapter: A Velcro band can pass through the flat mount, to secure the mount to the body or limb (i.e., upper arm, chest, etc.).
3. Methods
3.1. Body Opponent Bag
3.2. Human Testing
4. Results
Arm Abduction
5. Discussion
5.1. Pelvis and Shoulder Measurement
5.2. Arm Abduction
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Paušić, J.; Pedišić, Ž.; Dizdar, D. Reliability of a Photographic Method for Assessing Standing Posture of Elementary School Students. J. Manip. Physiol. Ther. 2010, 33, 425–431. [Google Scholar] [CrossRef] [PubMed]
- Saleh, M.; Murdoch, G. In Defence of Gait Analysis. Observation and Measurement in Gait Assessment. J. Bone Joint Surg. Br. 1985, 67, 237–241. [Google Scholar] [CrossRef] [Green Version]
- Carse, B.; Meadows, B.; Bowers, R.; Rowe, P. Affordable Clinical Gait Analysis: An Assessment of the Marker Tracking Accuracy of a New Low-Cost Optical 3D Motion Analysis System. Physiotherapy 2013, 99, 347–351. [Google Scholar] [CrossRef] [PubMed]
- Fortin, C.; Feldman, D.E.; Cheriet, F.; Labelle, H. Validity of a Quantitative Clinical Measurement Tool of Trunk Posture in Idiopathic Scoliosis. Spine 2010, 35, 988–994. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Song, M.-H.; Godøy, R.I. How Fast Is Your Body Motion? Determining a Sufficient Frame Rate for an Optical Motion Tracking System Using Passive Markers. PLoS ONE 2016, 11, e0150993. [Google Scholar] [CrossRef] [PubMed]
- Herda, L.; Fua, P.; Plänkers, R.; Boulic, R.; Thalmann, D. Using Skeleton-Based Tracking to Increase the Reliability of Optical Motion Capture. Hum. Mov. Sci. 2001, 20, 313–341. [Google Scholar] [CrossRef] [Green Version]
- Chiari, L.; Della Croce, U.; Leardini, A.; Cappozzo, A. Human Movement Analysis Using Stereophotogrammetry: Part 2: Instrumental Errors. Gait Posture 2005, 21, 197–211. [Google Scholar] [CrossRef]
- Ezeh, C.; Holloway, C.; Carlson, T. MoRe-T2 (Mobility Research Trajectory Tracker): Validation and Application. J. Rehabil. Assist. Technol. Eng. 2016, 3, 205566831667055. [Google Scholar] [CrossRef]
- Prakash, C.; Mittal, A.; Kumar, R.; Mittal, N. Identification of Spatio-Temporal and Kinematics Parameters for 2-D Optical Gait Analysis System Using Passive Markers. In Proceedings of the 2015 International Conference on Advances in Computer Engineering and Applications, Ghaziabad, India, 19–20 March 2015; IEEE: Ghaziabad, India; pp. 143–149. [Google Scholar]
- Abu-Faraj, Z.O. Handbook of Research on Biomedical Engineering Education and Advanced Bioengineering Learning; Medical Information Science Reference: Hershey, PA, USA, 2013. [Google Scholar]
- Measurement Sciences Aurora—Measurement Sciences. Available online: https://www.ndigital.com/msci/applications/biomechanics/ (accessed on 22 May 2019).
- Lou, E.; Hill, D.L.; Raso, V.J.; Durdle, N.G. A Posture Measurement System for the Treatment of Scoliosis. In Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conference, Venice, Italy, 24–26 May 1999. [Google Scholar]
- Jung, E.S.; Park, S. Prediction of Human Reach Posture Using a Neural Network for Ergonomic Man Models. Comput. Ind. Eng. 1994, 27, 369–372. [Google Scholar] [CrossRef]
- Poppe, R. Vision-Based Human Motion Analysis: An Overview. Comput. Vis. Image Underst. 2007, 108, 4–18. [Google Scholar] [CrossRef]
- Moeslund, T.B.; Hilton, A.; Krüger, V. A Survey of Advances in Vision-Based Human Motion Capture and Analysis. Comput. Vis. Image Underst. 2006, 104, 90–126. [Google Scholar] [CrossRef]
- Mathis, A.; Schneider, S.; Lauer, J.; Mathis, M.W. A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives. Neuron 2020, 108, 44–65. [Google Scholar] [CrossRef]
- Krause, D.A.; Boyd, M.S.; Hager, A.N.; Smoyer, E.C.; Thompson, A.T.; Hollman, J.H. Reliability and Accuracy of a Goniometer Mobile Device Application for Video Measurement of the Functional Movement Screen Deep Squat Test. Int. J. Sports Phys. Ther. 2015, 10, 37–44. [Google Scholar] [PubMed]
- Lemaire, E. A Shockwave Approach for Web-Based Clinical Motion Analysis. Telemed. J. e-Health 2004, 10, 39–43. [Google Scholar] [CrossRef] [PubMed]
- Lemaire, E. Biomechanics Augmented Reality—Apps on Google Play. Available online: https://play.google.com/store/apps/details?id=ca.irrd.bar&hl=en (accessed on 24 May 2019).
- Viswakumar, A.; Rajagopalan, V.; Ray, T.; Gottipati, P.; Parimi, C. Development of a Robust, Simple, and Affordable Human Gait Analysis System Using Bottom-Up Pose Estimation with a Smartphone Camera. Front. Physiol. 2022, 12, 2424. [Google Scholar] [CrossRef] [PubMed]
- Mroz, S.; Baddour, N.; McGuirk, C.; Juneau, P.; Tu, A.; Cheung, K.; Lemaire, E. Comparing the Quality of Human Pose Estimation with BlazePose or OpenPose. In Proceedings of the 4th International. Conference on Bio-Engineering on Smart Technology, Paris, France, 8–10 December 2021. [Google Scholar] [CrossRef]
- Wang, J.; Olson, E. AprilTag 2: Efficient and Robust Fiducial Detection. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Daejeon, Korea, 9–14 October 2016; pp. 4193–4198. [Google Scholar]
- Basiratzadeh, S.; Lemaire, E.D.; Dorrikhteh, M.; Baddour, N. Fiducial Marker Approach for Biomechanical Smartphone-Based Measurements. In Proceedings of the 2019 3rd International Conference on Bio-engineering for Smart Technologies (BioSMART), Paris, France, 24–26 April 2019; pp. 1–4. [Google Scholar]
- Olson, E. AprilTag: A Robust and Flexible Visual Fiducial System. In Proceedings of the IEEE International Conference on Robotics and Automation, Shanghai, China, 9–13 May 2011; pp. 3400–3407. [Google Scholar]
- Khan, D.; Ullah, S.; Yan, D.M.; Rabbi, I.; Richard, P.; Hoang, T.; Billinghurst, M.; Zhang, X. Robust Tracking Through the Design of High Quality Fiducial Markers: An Optimization Tool for ARToolKit. IEEE Access 2018, 6, 22421–22433. [Google Scholar] [CrossRef]
- Shabalina, K.; Magid, E.; Sagitov, A.; Li, H.; Sabirova, L. ARTag, AprilTag and CALTag Fiducial Marker Systems: Comparison in a Presence of Partial Marker Occlusion and Rotation. In Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2017), Madrid, Spain, 26–28 June 2017; Volume 2, pp. 182–191. [Google Scholar]
- Basiratzadeh, S.; Lemaire, E.D.; Baddour, N. Augmented Reality Approach for Marker-Based Posture Measurement on Smartphones. Augmented Reality Approach for Marker-Based Posture Measurement on Smartphones. In Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada, 20–24 July 2020; IEEE: Piscataway, NJ, USA, 2020. ISBN 9781728119908. [Google Scholar]
- Kim, J.; Jun, H. Implementation of Image Processing and Augmented Reality Programs for Smart Mobile Device. In Proceedings of the 6th International Forum on Strategic Technology, IFOST 2011, Harbin, China, 22–24 August 2011; Volume 2, pp. 1070–1073. [Google Scholar]
- Merriaux, P.; Dupuis, Y.; Boutteau, R.; Vasseur, P.; Savatier, X.; Merriaux, P.; Dupuis, Y.; Boutteau, R.; Vasseur, P.; Savatier, X. A Study of Vicon System Positioning Performance. Sensors 2017, 17, 1591. [Google Scholar] [CrossRef]
- Olsen, A.M. Posture and Body Movement Perception. Acta Psychol. 2007, 19, 820–821. [Google Scholar] [CrossRef]
- Della Croce, U.; Leardini, A.; Chiari, L.; Cappozzo, A. Human Movement Analysis Using Stereophotogrammetry Part 4: Assessment of Anatomical Landmark Misplacement and Its Effects on Joint Kinematics. Gait Posture 2005, 21, 226–237. [Google Scholar] [CrossRef]
- Peters, A.; Galna, B.; Sangeux, M.; Morris, M.; Baker, R. Quantification of Soft Tissue Artifact in Lower Limb Human Motion Analysis: A Systematic Review. Gait Posture 2010, 31, 1–8. [Google Scholar] [CrossRef]
- McGinley, J.L.; Baker, R.; Wolfe, R.; Morris, M.E. The Reliability of Three-Dimensional Kinematic Gait Measurements: A Systematic Review. Gait Posture 2009, 29, 360–369. [Google Scholar] [CrossRef] [PubMed]
- Tsushima, H.; Morris, M.E.; McGinley, J. Test-Retest Reliability and Inter-Tester Reliability of Kinematic Data from a Three-Dimensional Gait Analysis System. J. Jpn. Phys. Ther. Assoc. 2003, 6, 9–17. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Miranda, D.L.; Rainbow, M.J.; Crisco, J.J.; Fleming, B.C. Kinematic Differences between Optical Motion Capture and Biplanar Videoradiography during a Jump–Cut Maneuver. J. Biomech. 2013, 46, 567–573. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wade, L.; Needham, L.; McGuigan, P.; Bilzon, J. Applications and Limitations of Current Markerless Motion Capture Methods for Clinical Gait Biomechanics. PeerJ 2022, 10, e12995. [Google Scholar] [CrossRef]
- Kessler, S.E.; Rainbow, M.J.; Lichtwark, G.A.; Cresswell, A.G.; D’andrea, S.E.; Konow, N.; Kelly, L.A. A Direct Comparison of Biplanar Videoradiography and Optical Motion Capture for Foot and Ankle Kinematics. Front. Bioeng. Biotechnol. 2019, 7, 199. [Google Scholar] [CrossRef] [Green Version]
- Fiorentino, N.M.; Atkins, P.R.; Kutschke, M.J.; Goebel, J.M.; Foreman, K.B.; Anderson, A.E. Soft Tissue Artifact Causes Significant Errors in the Calculation of Joint Angles and Range of Motion at the Hip. Gait Posture 2017, 55, 184–190. [Google Scholar] [CrossRef]
- Krogius, M.; Haggenmiller, A.; Olson, E. Flexible Layouts for Fiducial Tags. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 3–8 November 2019. [Google Scholar]
Participant | Average Angle Difference between Vicon and Saved BAR-M Data | Average Angle Difference between BAR-M AR Read by Human and BAR-M Saved Data | |||
---|---|---|---|---|---|
Pelvis | Shoulder | Arm | Pelvis | Shoulder | |
P1 | 1.97 ± 0.60° | 0.42 ± 0.49° | 3.51 ± 1.59° | 0.12 ± 0.14° | 0.07 ± 0.06° |
P2 | 1.09 ± 0.50° | 0.82 ± 0.21° | 19.08 ± 5.08° | 0.31 ± 0.24° | 0.07 ± 0.10° |
P3 | 2.65 ± 0.80° | 2.77 ± 0.30° | 27.92 ± 1.98° | 0.13 ± 0.09° | 0.06 ± 0.04° |
P4 | 2.73 ± 1.15° | 0.61 ± 0.18° | 8.37 ± 4.98° | 0.21 ± 0.18° | 0.23 ± 0.34° |
P5 | 0.64 ± 0.56° | 0.89 ± 0.68° | 6.80 ± 3.51° | 0.20 ± 0.29° | 0.18 ± 0.18° |
P6 | 0.78 ± 0.88° | 0.42 ± 0.31° | 28.46 ± 4.67° | 0.18 ± 0.19° | 0.09 ± 0.06° |
P7 | 1.42 ± 0.98° | 2.85 ± 0.98° | 7.74 ± 5.40° | 0.16 ± 0.10° | 0.03 ± 0.02° |
P8 | 1.78 ± 0.92° | 2.28 ± 0.61° | 7.37 ± 2.95° | 0.12 ± 0.11° | 0.08 ± 0.06° |
P9 | 0.62 ± 0.46° | 0.68 ± 0.42° | 11.19 ± 5.41° | 0.16 ± 0.14° | 0.07 ± 0.07° |
P10 | 1.68 ± 0.83° | 3.70 ± 0.53° | 13.33 ± 4.59° | 0.18 ± 0.13° | 0.12 ± 0.10° |
P11 | 1.98 ± 0.89° | 0.35 ± 0.21° | 11.16 ± 1.49° | 0.29 ± 0.33° | 0.20 ± 0.25° |
P12 | 0.59 ± 0.36° | 2.38 ± 1.05° | 2.71 ± 3.59° | 0.27 ± 0.26° | 0.15 ± 0.10° |
P13 | 3.37 ± 0.72° | 0.72 ± 0.39° | 13.07 ± 6.58° | 0.21 ± 0.31° | 0.15 ± 0.14° |
P14 | 1.72 ± 0.86° | 0.84 ± 0.69° | 1.93 ± 1.17° | 0.17 ± 0.13° | 0.07 ± 0.05° |
P15 | 2.52 ± 0.52° | 0.44 ± 0.33° | 4.99 ± 2.22° | 0.10 ± 0.07° | 0.19 ± 0.13° |
Average | 1.70 ± 0.23° | 1.34 ± 0.27° | 11.18 ± 3.68° | 0.19 ± 0.09° | 0.12 ± 0.09° |
p-value | <0.001 | <0.001 | 0.71 | <0.001 | 0.83 |
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Basiratzadeh, S.; Lemaire, E.D.; Baddour, N. A Novel Augmented Reality Mobile-Based Application for Biomechanical Measurement. BioMed 2022, 2, 255-269. https://doi.org/10.3390/biomed2020021
Basiratzadeh S, Lemaire ED, Baddour N. A Novel Augmented Reality Mobile-Based Application for Biomechanical Measurement. BioMed. 2022; 2(2):255-269. https://doi.org/10.3390/biomed2020021
Chicago/Turabian StyleBasiratzadeh, Shahin, Edward D. Lemaire, and Natalie Baddour. 2022. "A Novel Augmented Reality Mobile-Based Application for Biomechanical Measurement" BioMed 2, no. 2: 255-269. https://doi.org/10.3390/biomed2020021
APA StyleBasiratzadeh, S., Lemaire, E. D., & Baddour, N. (2022). A Novel Augmented Reality Mobile-Based Application for Biomechanical Measurement. BioMed, 2(2), 255-269. https://doi.org/10.3390/biomed2020021