{"id":7371,"date":"2026-05-27T18:41:37","date_gmt":"2026-05-27T10:41:37","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=7371"},"modified":"2026-05-27T20:25:49","modified_gmt":"2026-05-27T12:25:49","slug":"jase-202609-32-056","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202609-32-056","title":{"rendered":"Adaptive IMU Dynamic Calibration for Intelligent Perception of High-Dynamic Sports-Like Motions"},"content":{"rendered":"\n
\n
\n

<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

\n

<\/i> Volume 32<\/a><\/p>\n<\/div>\n\n\n\n

\n
\n
\n
\n

Dong Xiaoou1<\/sup>, Guan Xinru2<\/sup>, Feng Yongxu1<\/sup>, and Lu Jiawei1<\/sup><\/i><\/a> <\/p>\n\n\n\n

1<\/sup>School of Physical Education, Jiamusi University, Jiamusi 154007, China<\/p>\n\n\n\n

2<\/sup>College of Information and Electronic Technology, Jiamusi University, Jiamusi 154007, China<\/p>\n<\/div>\n\n\n\n

\n

Received: April 26, 2026
Accepted: May 18, 2026
Publication Date: May 27, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

System Architecture <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202609_32.056<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

<\/div>\n\n\n\n

In intelligent perception of high-dynamic sports-like motions, inertial measurement units (IMUs) are susceptible to sensor slippage, mounting misalignment, and global drift during prolonged high-intensity motion, which progressively degrades the validity of one-time static calibration. To address this issue, this paper proposes an adaptive IMU dynamic calibration method for high-dynamic sports-like motion perception. The proposed
framework takes rotation matrices and acceleration sequences from six body-worn IMUs as input and constructs an online calibration architecture based on a Transformer Encoder backbone. Through sliding-buffer inference, motion-diversity-triggered updating, and dual-branch parameter estimation, the method recursively updates the drift matrix RDG and the mounting offset matrix RBS. Compared with static-only calibration, the proposed method is better suited for continuous correction under motion-intensive patterns such as turning, punching-like arm swings, jumping-like bursts, and abrupt direction changes. Experimental results indicate stable convergence and effective parameter estimation. The experimental protocol includes static baseline comparison, original dynamic calibration reproduction, validation on a high-dynamic sports-like proxy subset constructed from the TIC test set, trigger ablation, and downstream proxy action recognition verification. These results demonstrate that the proposed framework provides a practical solution for online calibration in intelligent sensing systems for motion-intensive sports-like movements.<\/p>\n\n\n\n

Keywords: high-dynamic sports-like motions; IMU dynamic calibration; Transformer; online calibration; intelligent perception<\/em><\/p>\n\n\n\n

<\/div>\n\n\n\n
\n
    \n
  1. [1] A. \u00c7. Se\u00e7kin, B. Ate\u015f, and M. Se\u00e7kin, (2023) \u201cReview on wearable technology in sports: concepts, challenges and opportunities\u201d Applied Sciences 13(18): 10399. DOI: 10.3390\/app131810399.<\/li>\n
  2. [2] P. Picerno, M. Iosa, C. D\u2019Souza, M. G. Benedetti, S. Paolucci, and G. Morone, (2021) \u201cWearable inertial sensors for human movement analysis: a five-year update\u201d Expert Review of Medical Devices 18(sup1): 79\u201394. DOI: 10.1080\/17434440.2021.1988849.<\/li>\n
  3. [3] W. Hailong, G. Xinru, J. Sheng, and Z. Lijun, (2026) \u201cMulti-Task Learning for Sports Training Monitoring Via Multimodal Fusion of IMU and Human-Body Capacitance Signals\u201d Journal of Applied Science and Engineering 31: 1\u201313. DOI: 10.6180\/jase.202608_31.038.<\/li>\n
  4. [4] M. Ekdahl, A. Loewen, A. Erdman, S. Sahin, and S. Ulman, (2023) \u201cInertial measurement unit sensor-to-segment calibration comparison for sport-specific motion analysis\u201d Sensors 23(18): 7987. DOI: 10.3390\/s23187987.<\/li>\n
  5. [5] Y.-t. Zhao, X. Wang, K.-x. Yang, L.-d. Wang, C. Yang, R. Feng, L.-l. Zheng, and Z.-p. Zhou, (2025) \u201cEffects of various static calibration postures on knee mechanics during locomotor tasks using statistical parametric mapping analysis\u201d Scientific Reports 15(1): 29833. DOI: 10.1038\/s41598-025-15311-2.<\/li>\n
  6. [6] J. Wu, Z. Mo, X. Gao, W. Xin, W. Shi, and J. Park, (2025) \u201cArtificial intelligence assisted wearable flexible sensors for sports: research progress in technology integration and application\u201d International Journal of Smart and Nano Materials 16(3): 510\u2013548. DOI: 10.1080\/19475411.2025.2519582.<\/li>\n
  7. [7] J. Ok, S. Park, Y. H. Jung, and T.-i. Kim, (2024) \u201cWearable and implantable cortisol-sensing electronics for stress monitoring\u201d Advanced Materials 36(1): 2211595. DOI: 10.1002\/adma.202211595.<\/li>\n
  8. [8] X. Xuan, C. Chen, A. Molinero-Fernandez, E. Ekelund, D. Cardinale, M. Swar\u00e9n, L. Wedholm, M. Cuartero, and G. A. Crespo, (2023) \u201cFully integrated wearable device for continuous sweat lactate monitoring in sports\u201d ACS Sensors 8(6): 2401\u20132409. DOI: 10.1021\/acssensors.3c00708.<\/li>\n
  9. [9] F. Criscuolo, I. N. Hanitra, S. Aiassa, I. Taurino, N. Oliva, S. Carrara, and G. De Micheli, (2021) \u201cWearable multifunctional sweat-sensing system for efficient healthcare monitoring\u201d Sensors and Actuators B: Chemical 328: 129017. DOI: 10.1016\/j.snb.2020.129017.<\/li>\n
  10. [10] A. Bonfiglio, D. Tacconi, R. M. Bongers, and E. Farella, (2024) \u201cEffects of IMU sensor-to-segment calibration on clinical 3D elbow joint angles estimation\u201d Frontiers in Bioengineering and Biotechnology 12: 1385750. DOI: 10.3389\/fbioe.2024.1385750.<\/li>\n
  11. [11] S. Chidambaram, Y. Maheswaran, K. Patel, V. Sounderajah, D. A. Hashimoto, K. P. Seastedt, A. H. McGregor, S. R. Markar, and A. Darzi, (2022) \u201cUsing artificial intelligence-enhanced sensing and wearable technology in sports medicine and performance optimisation\u201d Sensors 22(18): 6920. DOI: 10.3390\/s22186920.<\/li>\n
  12. [12] C. Yi, S. Zhang, F. Jiang, J. Liu, Z. Ding, C. Yang, and H. Zhou, (2021) \u201cEnable fully customized assistance: A novel IMU-based motor intent decoding scheme\u201d IEEE Transactions on Cognitive and Developmental Systems 15(4): 2089\u20132098. DOI: 10.1109\/tcds.2021.3126001.<\/li>\n
  13. [13] T. Li and H. Yu, (2023) \u201cUpper body pose estimation using a visual\u2013inertial sensor system with automatic sensor-to-segment calibration\u201d IEEE Sensors Journal 23(6): 6292\u20136302. DOI: 10.1109\/jsen.2023.3241084.<\/li>\n
  14. [14] J. Ghattas and D. N. Jarvis, (2024) \u201cValidity of inertial measurement units for tracking human motion: a systematic review\u201d Sports Biomechanics 23(11): 1853\u20131866. DOI: 10.1080\/14763141.2021.1990383.<\/li>\n
  15. [15] J. Zhu, Z. Ye, R. Liu, and J. Liu, (2026) \u201cInertial measurement units (IMUs) for biomechanical analysis in sport: A review of applications, challenges and future directions\u201d Sensor Review 46(1): 88\u2013104. DOI: 10.1108\/SR-04-2025-0261.<\/li>\n
  16. [16] M. McInne, D. Blana, A. Starkey, and E. K. Chadwick, (2025) \u201cA practical sensor-to-segment calibration method for upper limb inertial motion capture in a clinical setting\u201d IEEE Journal of Translational Engineering in Health and Medicine: DOI: 10.1109\/JTEHM.2025.3565986.<\/li>\n
  17. [17] L. Wolski, M. Halaki, C. E. Hiller, E. Pappas, and A. Fong Yan, (2024) \u201cValidity of an inertial measurement unit system to measure lower limb kinematics at point of contact during incremental high-speed running\u201d Sensors 24(17): 5718. DOI: 10.3390\/s24175718.<\/li>\n
  18. [18] M. V. Potter, (2025) \u201cSimulating effects of sensor-to-segment alignment errors on IMU-based estimates of lower limb joint angles during running\u201d Sports Engineering 28(1): 1. DOI: 10.1007\/s12283-024-00483-3.<\/li>\n
  19. [19] B. Fan, L. Zhang, S. Cai, M. Du, T. Liu, Q. Li, and P. Shull, (2025) \u201cInfluence of sampling rate on wearable IMU orientation estimation accuracy for human movement analysis\u201d Sensors 25(7): 1976. DOI: 10.3390\/s25071976.<\/li>\n
  20. [20] S. Suh, V. F. Rey, and P. Lukowicz, (2023) \u201cTasked: transformer-based adversarial learning for human activity recognition using wearable sensors via self-knowledge distillation\u201d Knowledge-Based Systems 260: 110143. DOI: 10.1016\/j.knosys.2022.110143.<\/li>\n
  21. [21] X. Guo, Y. Kim, X. Ning, and S. D. Min, (2025) \u201cEnhancing the transformer model with a convolutional feature extractor block and vector-based relative position embedding for human activity recognition\u201d Sensors 25(2): 301. DOI: 10.3390\/s25020301.<\/li>\n
  22. [22] D. Kim, Y. Jin, H. Cho, T. Jones, Y. M. Zhou, A. Fadaie, D. Popov, K. Swaminathan, and C. J. Walsh, (2025) \u201cLearning-based 3D human kinematics estimation using behavioral constraints from activity classification\u201d Nature communications 16(1): 3454. DOI: 10.1038\/s41467-025-58624-6.<\/li>\n
  23. [23] D. Fruet, A. Tauro, D. Di Liberto, C. Pedrotti, I. Bracci, G. Martinelli, and G. Nollo. \u201cComparing IMU and Optical Motion Capture System for Sport Biomechanics: Static and Dynamic Analysis\u201d. In: 2025 IEEE International Workshop on Sport, Technology and Research (STAR). IEEE. 2025, 43\u201348. DOI: 10.1109\/star66750.2025.11264765.<\/li>\n
  24. [24] C. Zuo, J. Huang, X. Jiang, Y. Yao, X. Shi, R. Cao, X. Yi, F. Xu, S. Guo, and Y. Qin, (2025) \u201cTransformer IMU calibrator: dynamic on-body IMU calibration for inertial motion capture\u201d ACM Transactions on Graphics (TOG) 44(4): 1\u201314. DOI: 10.1145\/3730937.<\/li>\n<\/ol>\n<\/div>\n\n\n\n

    <\/p>\n","protected":false},"author":3,"template":"wp-custom-template-detail-4-aricles","meta":{"_uag_custom_page_level_css":""},"categories":[12,720,6],"tags":[1465],"acf":[],"uagb_featured_image_src":[],"uagb_author_info":{"display_name":"\u6797\u923a\u6db5","author_link":"\/jase\/?author=3"},"uagb_comment_info":0,"uagb_excerpt":" Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited. Download Citation:\u00a0 BibTeX | http:\/\/dx.doi.org\/10.6180\/jase.202609_32.056\u00a0\u00a0 Download PDF In intelligent perception of high-dynamic sports-like motions, inertial measurement units (IMUs)…","_links":{"self":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope\/7371"}],"collection":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope"}],"about":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/types\/tkuisotope"}],"author":[{"embeddable":true,"href":"\/jase\/index.php?rest_route=\/wp\/v2\/users\/3"}],"wp:attachment":[{"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7371"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7371"},{"taxonomy":"post_tag","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7371"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}