DEVELOPMENT OF A MARKERLESS OPTICAL MOTION CAPTURE SYSTEM BY AN ACTION SPORTS CAMERA FOR RUNNING MOTION
Keywords:Action sports camera, Gopro, Kinematics, Markerless optical motion capture, Running
AbstractA marker-based optical motion capture system is often used to obtain the kinematics parameters of a running analysis. However, the attached marker could affect the participant's movement, and the system is costly because of the exclusive cameras. Due to its drawbacks, the present research aimed to develop an affordable markerless optical motion capture system for running motion. The proposed system used an action sports camera to acquire the running images of the participant. The images were segmented to get the silhouette of the participant. Then, a human body model was generated to provide a priori information to track participants' segment position. The subsequent procedure was image registration to estimate the pose of the participant's silhouette. The transformation parameters were estimated by particle swarm optimization. The optimization output in the form of the rotation angle of the body segment was then employed to identify right or left lower limbs. To validate the results of the optimization, a manual matching was conducted to obtain the actual rotation angle for all body segments. The correlation coefficient between the rotation angle from image registration and the actual rotation angle was then evaluated. It was found that the lowest correlation coefficient was 0.977 for the left foot. It implies that the accuracy of the developed system in the present work is acceptable. Furthermore, the results of the kinematics analysis have good agreement with the literature. Therefore, the developed system, not only yields acceptable running parameters, but also affordable since it uses an action sports camera and easy to use.
J. J. Andersen, "The State of Running 2019," 2021. [Online]. Available: https://runrepeat.com/state-of-running.
W. F. Major, 2001. "The Benefits and Costs of Serious Running," World Leisure Journal, 43(2): 12-25
L. L. Craft and F. M. Perna, 2004. "The benefits of exercise for the clinically depressed," Primary care companion to the Journal of clinical psychiatry, 6(3): 104-111,
V. E. Wilson, B. G. Berger and E. I. Bird, 1981. "Effects of Running and of an Exercise Class on Anxiety," Perceptual and Motor Skills, 53(2): 472-472,
Á. G.Lucas-Cuevas, J. I. Quesadaa, JoshGooding, M. G.C.Lewis, AlbertoEncarnación-Martínez and PedroPerez-Soriano, 2018. "The effect of visual focus on spatio-temporal and kinematic parameters of treadmill running," Gait & Posture, 59: 292-297
F. J. Wouda, M. Giuberti, G. Bellusci, E. Maartens, J. Reenalda, B.-J. K. v. Beijnum and P. H. Veltink, 2018. "On the validity of different motion capture technologies for the analysis of running," in IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), Enschede, The Netherlands
S. Mihradi, F. Ferryanto, T. Dirgantara and A. I. Mahyuddin, 2013. "Tracking of Markers for 2D and 3D gait analysis using home video cameras," International Journal of E-Health and Medical Communications (IJEHMC), 4(3): 36-52
R. Cross, 1999. "Standing, walking, running, and jumping on a force plate," American Journal of Physics, 67(4): 304-309
J. H. Challis, 2001. "The Variability in Running Gait Caused by Force Plate Targeting," Journal of Applied Biomechanics, 17(1): 77-83
E. Bergamini, P. Picerno, H. Pillet, F. Natta, P. Thoreux and V. Camomilla, 2012. "Estimation of temporal parameters during sprint running using a trunk-mounted inertial measurement unit," Journal of Biomechanics, 45(6): 1123-1126
T. H. Jeon and J. K. Lee, 2018. "IMU-Based Joint Angle Estimation Under Various Walking and Running," Journal of the Korean Society for Precision Engineering, 35(12): 1199-1204
G. R. D. Bernardina, T. Monnet, H. T. Pinto, R. M. L. d. Barros, P. Cerveri and A. P. Silvatti, 2019. "Are action sport cameras accurate enough for 3D motion analysis? A comparison with a commercial motion capture system," Journal of Applied Biomechanics, 35(1): 80-86,
S. Corazza, L. Mündermann, A. M. Chaudhari, T. Demattio, C. Cobelli and T. P. Andriacchi, 2006. "A markerless motion capture system to study musculoskeletal biomechanics: visual hull and simulated annealing approach," Annals Of Biomedical Engineering, 34(6): 1019-1029
A. Castelli, G. Paolini, A. Cereatti and U. D. Croce, 2015"A 2D Markerless Gait Analysis Methodology: Validation on Healthy Subjects," Computational and mathematical methods in medicine.
E. Ceseracciu, Z. Sawacha, S. Fantozzi, M. Cortesi, G. Gatta, S. Corazza and C. Cobelli, 2011. "Markerless analysis of front crawl swimming," Journal of Biomechanics. 44(12): 2236-2242
F. Ferryanto and M. Nakashima, 2016. "Development of a markerless optical motion capture system for daily use of training in swimming," Sports Engineering, 20: 63-72,
Z. Cao, G. Hidalgo, T. Simon, S.-E. Wei and Y. Sheikh, 2019. "OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields," IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(1): 172-186
M. Ota, H. Tateuchi, T. Hashiguchi and N. Ichihashi, 2021 "Verification of validity of gait analysis systems during treadmill walking and running using human pose tracking algorithm," Gait & Posture, 85: 290-297
E. D'Antonio, J. Taborri, E. Palermo, S. Rossi and F. Patanè, 2020. "A markerless system for gait analysis based on OpenPose library," in 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Dubrovnik, Croatia,
[N. R. Pal and S. K. Pal, 1993. "A Review on Image Segmentation Techniques," Pattern Recognition, 26(9): 1277-1294
C. Stauffer and W. Grimson, 1999. "Adaptive Background Mixture Models for Real-Time Tracking," Proceedings. 1999 IEEE computer society conference on computer vision and pattern recognition (Cat. No PR00149), 2: 246-252
P. KaewTraKulPong and R. Bowden, 2002"An improved adaptive background mixture model for real-time tracking with shadow detection," in Video-based surveillance systems, 135-144. Springer,Boston.
B. Zitova and J. Flusser, 2013. "Image registration methods: a survey Barbara," Image and Vision Computing, 21: 977-1000
R. C. Gonzalez, R. E. Woods and S. L. Eddins, 2009.Digital Image Processing using MATLAB, United Stalls of America: Gatesmark Publishing.
S. M, "Argmax and Max Calculus," 2016. [Online]. Available:https://www.cs.ubc.ca/*schmidtm/Documents/2016_540_Argmax. pdf. [Accessed 17 February 2021].
R. Poli, J. Kennedy and T. Blackwell, 2007. "Particle swarm optimization," Swarm Intelligence, 1(1): 33-57