Design and implementation of a countermovement jump performance estimation system using a wearable device with IMUs based on machine learning algorithms
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References
- sklearn.feature_select ion.selectkbest. Available at:
https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html . Last Accessed: 2021-10-9. - What is an inertial measurement unit? Available at:
https://www.vectornav.com/resources/inertial-navigation-articles/what-is-an-inertial-measurement-unit-imu . Last Accessed: 2021-10-9. - Arnel Aguinaldo and Andrew Mahar. Impact loading in running shoes with cushioning column systems. Journal of Applied Biomechanics, 19(4):353–360, 2003.
- Gobinath Aroganam, Nadarajah Manivannan, and David Harrison. Review on wearable technology sensors used in consumer sport applications. Sensors, 19(9):1983, 2019.
- VV Bauman and SCE Brandon. Preliminary investigation of predicting time-to-next heel-strike using accelerometers and machine learning. In 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), pages 827–832. IEEE, 2020.
- Jamin Casselman, Nicholas Onopa, and Lara Khansa. Wearable healthcare: Lessons from the past and a peek into the future. Telematics and Informatics, 34(7):1011–1023, 2017.
- Courtney R Chaaban, Nathaniel T Berry, Cortney Armitano-Lago, Adam W Kiefer, Michael J Mazzoleni, and Darin A Padua. Combining inertial sensors and machine learning to predict vgrf and knee biomechanics during a double limb jump landing task. Sensors, 21(13):4383, 2021.
- Girish Chandrashekar and Ferat Sahin. A survey on feature selection methods. Computers & Electrical Engineering, 40(1):16–28, 2014.
- Tianqi Chen and Carlos Guestrin. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 785–794, 2016.
- Yi-Yu Chiang. Replacing force plate with wearable imu for measuring countermovement jump performance with machine learning methods. Master Thesis of National Chiao Tung University, 2020.
- Yi-Yu Chiang, Wen-Yueh Shih, Wei-Han Chen, Jiun-Long Huang, and Tzyy-Yuang Shiang. A machine learning-based countermovement performance measurement method using a wearable imu. In Proceedings of International Conference on Pervasive Artificial Intelligence (ICPAI), 2020.
- Luis A Durán-Vega, Pedro C Santana-Mancilla, Raymundo Buenrostro-Mariscal, Juan Contreras-Castillo, Luis E Anido-Rifón, Miguel A García-Ruiz, Osval A Montesinos-López, and Fermín Estrada-González. An iot system for remote health monitoring in elderly adults through a wearable device and mobile application. Geriatrics, 4(2):34, 2019.
- Raffaele Iervolino, Francesco Bonavolontà, and Adolfo Cavallari. A wearable device for sport performance analysis and monitoring. In 2017 IEEE International Workshop on Measurement and Networking (M&N), pages 1–6. IEEE, 2017.
- Daniel A James and Nicola Petrone. Sensors and wearable technologies in Sport: Technologies, trends and approaches for implementation. Springer, 2016.
- William R Johnson, Ajmal Mian, Mark A Robinson, Jasper Verheul, David G Lloyd, and Jacqueline A Alderson. Multidimensional ground reaction forces and moments from wearable sensor accelerations via deep learning. IEEE Transactions on Biomedical Engineering, 68(1):289–297, 2020.
- Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30:3146–3154, 2017.
- Suwon Kim and Seongcheol Kim. A multi-criteria approach toward discovering killer iot application in korea. Technological Forecasting and Social Change, 102:143–155, 2016.
- Kristof Kipp, John Krzyszkowski, and Daniel Kant-Hull. Use of machine learning to model volume load effects on changes in jump performance. International Journal of Sports Physiology and Performance, 15(2):285–287, 2020.
- Tyler J Kirby, Jeffrey M McBride, Tracie L Haines, and Andrea M Dayne. Relative net vertical impulse determines jumping performance. Journal of Applied Biomechanics, 27(3):207–214, 2011.
- Jason Lake, Peter Mundy, Paul Comfort, John J McMahon, Timothy J Suchomel, and Patrick Carden. Concurrent validity of a portable force plate using vertical jump force–time characteristics. Journal of Applied Biomechanics, 34(5):410–413, 2018.
- Jason P Lake, Peter D Mundy, and Paul Comfort. Power and impulse applied during push press exercise. The Journal of Strength & Conditioning Research, 28(9):2552–2559, 2014.
- Huang-Chen Lee, Soun-Cheng Wang, and Zih-Hua Lin. An open-source wearable sensor system for measuring the duty factor of runners. IEEE Transactions on Instrumentation and Measurement, 73, 2024.
- He Li, Jing Wu, Yiwen Gao, and Yao Shi. Examining individuals’ adoption of healthcare wearable devices: An empirical study from privacy calculus perspective. International Journal of Medical Informatics, 88:8–17, 2016.
- Hyerim Lim, Bumjoon Kim, and Sukyung Park. Prediction of lower limb kinetics and kinematics during walking by a single imu on the lower back using machine learning. Sensors, 20(1):130, 2020.
- Christopher P McLellan, Dale I Lovell, and Gregory C Gass. The role of rate of force development on vertical jump performance. The Journal of Strength & Conditioning Research, 25(2):379–385, 2011.
- John J McMahon, Shannon Murphy, Sophie JE Rej, and Paul Comfort. Countermovement-jump-phase characteristics of senior and academy rugby league players. International Journal of Sports Physiology and Performance, 12(6):803–811, 2017.
- Pietro Picerno, Valentina Camomilla, and Laura Capranica. Countermovement jump performance assessment using a wearable 3d inertial measurement unit. Journal of Sports Sciences, 29(2):139–146, 2011.
- Juliano Dal Pupo, Daniele Detanico, and Saray Giovana dos Santos. Kinetic parameters as determinants of vertical jump performance. Revista Brasileira de Cineantropometria & Desempenho Humano, 14:41–51, 2012.
- Paige E Rice, Courtney L Goodman, Christopher R Capps, N Travis Triplett, Travis M Erickson, and Jeffrey M McBride. Force–and power–time curve comparison during jumping between strength-matched male and female basketball players. European Journal of Sport Science, 17(3):286–293, 2017.
- Omer Sagi and Lior Rokach. Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4):e1249, 2018.
- Paul Swinton, Justin Keogh, and Jason Lake. Practical applications of biomechanical principles in resistance training: The use of bands and chains. Journal of Fitness Research, 3:26–41, 2014.
- Kuniharu Takei, Wataru Honda, Shingo Harada, Takayuki Arie, and Seiji Akita. Toward flexible and wearable human-interactive health-monitoring devices. Advanced Healthcare Materials, 4(4):487–500, 2015.
- Dong Wen, Xingting Zhang, and Jianbo Lei. Consumers’ perceived attitudes to wearable devices in health monitoring in china: A survey study. Computer Methods and Programs in Biomedicine, 140:131–137, 2017.
- Zhaoxian Zhou, Sarbagya Shakya, and Zhanxin Sha. Predicting countermovement jump heights by time domain, frequency domain, and machine learning algorithms. In 2017 10th International Symposium on Computational Intelligence and Design (ISCID), volume 2, pages 167–170. IEEE, 2017.
DOI: https://doi.org/10.2478/ijssis-2025-0053 | Journal eISSN: 1178-5608
Language: English
Submitted on: Apr 4, 2025
Published on: Oct 13, 2025
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
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© 2025 Jhe-Sheng Yang, Jun-Zhe Wang, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.