Abstract
In this study, we describe a system to automatically analyze rowing kinematic parameters using video capturing and processing and using a single RGB camera. Useful rowing 2D joint angles in the sagittal plane are estimated using the OpenPose API combined with an offline filter to overcome frame loss and oscillations. Rowing key moments are identified using a time series comparison between the joint angles curves from the movement performed and a manually labeled reference joint angles curves, representing a desired rowing stroke. The comparison is realized using the Dynamic Time Warping method. All the obtained data is displayed in a user-friendly interface to monitor the movement and provide offline feedback. The proposed approach enables automatic analysis of video-recorded training sessions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Güler RA, Neverova N, Kokkinos I (2018) Densepose: Dense human pose estimation in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7297–7306
Berndt D, Clifford J (1994) DTW (dynamic time warping). Workshop on knowledge knowledge discovery in databases 398:359–370
Bingul BM et al (2014) Two-dimensional kinematic analysis of catch and finish positions during a 2000 m rowing ergometer time trial. S Afr J Res Sport Phys Educ Recreat 36(3):1–10. ISSN: 03799069
Bosch S et al (2015) Analysis of indoor rowing motion using wearable inertial sensors. In: Proceedings of the 10th EAI international conference on body area networks. ICST, pp 233–239. ISBN: 978- 1-63190-084-6. https://doi.org/10.4108/eai.28-9-2015. 2261465
Cao Z et al (2018) OpenPose: realtime multi-person 2D pose estimation using part affinity fields. http://arxiv.org/abs/1812.08008
Cerne T, Kamnik R, Munih M (2011) The measurement setup for real-time biomechanical analysis of rowing on an ergometer. Measurement 44(10 ):1819–1827. ISSN: 02632241
Cerne T et al (2013) Differences between elite, junior and non-rowers in kinematic and kinetic parameters during ergometer rowing. Hum Movem Sci 32(4):691–707. ISSN: 01679457. https://doi.org/10.1016/j.humov.2012.11.006
de Souza Baptista R, Bo AP, Hayashibe M (2017) Automatic human movement assessment with switching linear dynamic system: motion segmentation and motor performance. IEEE Trans Neural Syst Rehabil Eng 25(6):628–640. ISSN: 15344320. https://doi.org/10.1109/TNSRE.2016.2591783
Fothergill S, Harle R, Holden S (2008) Modeling the model athlete: automatic coaching of rowing technique. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5342 LNCS, pp 372–381. ISSN: 03029743. https://doi.org/10.1007/978-3-540-89689-0_41
Franke T, Pieringer C, Lukowicz P (2011) How should a wearable rowing trainer look like? A user study. In: Proceedings of international symposium on wearable computers, ISWC, pp 15–18. ISSN: 15504816. https://doi.org/10.1109/ISWC.2011.15
Gravenhorst F et al (2012) Sonicseat: a seat position tracker based on ultrasonic sound measurements for rowing technique analysis. In: BODYNETS 2012—7th international conference on body area networks. https://doi.org/10.4108/icst.bodynets.2012.249917
Gravenhorst F et al (2014) Strap and row: rowing technique analysis based on inertial measurement units implemented in mobile phones. In: IEEE ISSNIP 2014—2014 IEEE 9th international conference on intelligent sensors, sensor networks and information processing, conference proceedings, Apr 2014, pp 21–24. https://doi.org/10.1109/ISSNIP.2014.6827677
Ishiko T (2015) Biomechanics of rowing, pp 249–252. https://doi.org/10.1159/000392181
Lin T-Y et al (204) Microsoft COCO: common objects in context. http://arxiv.org/abs/1405.0312
Raaj Y et al (2019) Efficient online multi-person 2D pose tracking with recurrent spatio-temporal affinity fields. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4620–4628
Sforza C (2012) A three-dimensional study of body motion during ergometer rowing. Open Sports Med J 6(1):22–28. ISSN: 18743870. https://doi.org/10.2174/1874387001206010022
Skublewska-Paszkowska M et al (2016) Motion capture as a modern technology for analysing ergometer rowing. Adv Sci Technol Res J 10(29):132–140. ISSN: 2080–4075. https://doi.org/10.12913/22998624/61941
Tessendorf B et al (2011) An IMU-based sensor network to continuously monitor rowing technique on the water. In: Proceedings of the 2011 7th international conference on intelligent sensors, sensor networks and information processing, ISSNIP 2011, pp 253–258. https://doi.org/10.1109/ISSNIP.2011.6146535
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
The authors declare that they have no conflict of interest.
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Macedo, V., Santos, J., Baptista, R.S. (2022). Automatic Rowing Kinematic Analysis Using OpenPose and Dynamic Time Warping. In: Bastos-Filho, T.F., de Oliveira Caldeira, E.M., Frizera-Neto, A. (eds) XXVII Brazilian Congress on Biomedical Engineering. CBEB 2020. IFMBE Proceedings, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-030-70601-2_93
Download citation
DOI: https://doi.org/10.1007/978-3-030-70601-2_93
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-70600-5
Online ISBN: 978-3-030-70601-2
eBook Packages: EngineeringEngineering (R0)