{"id":9321,"date":"2026-07-25T19:41:26","date_gmt":"2026-07-25T11:41:26","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=9321"},"modified":"2026-07-25T22:26:34","modified_gmt":"2026-07-25T14:26:34","slug":"jase-202610-33-056","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202610-33-056","title":{"rendered":"Sports training posture simulation based on optical imaging sensor and motion trajectory prediction algorithm"},"content":{"rendered":"\n
\n
\n

<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

\n

<\/i> Volume 33<\/a><\/p>\n<\/div>\n\n\n\n

\n
\n
\n
\n

Qu Meili1<\/sup>, Chang Ning1<\/sup>, Xing Enqian1<\/sup>, and Gao Xuan2<\/sup><\/i><\/a><\/p>\n\n\n\n

1<\/sup>College of Physical Education, Yanching Institute of Technology, Langfang, Hebei 065201, China<\/p>\n\n\n\n

2<\/sup>P.E. Department, Hebei University of Water Resources and Electric Engineering, Cangzhou, Hebei, 061000, China<\/p>\n<\/div>\n\n\n\n

\n

Received: November 3, 2025
Accepted: January 3, 2026
Publication Date: July 25, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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

Schematic diagram of the Kinematic-Aware Neural Projection Network (KANet) 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.202610_33.056<\/a>\u00a0\u00a0<\/p>\n\n\n\n

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

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

In the evolving landscape of intelligent computing and digital modeling, simulating complex human activities requires a synthesis of physical accuracy and data-driven generalization, particularly in fields such as athletic performance analysis. Within the broader context of trustworthy intelligent systems and applied AI frameworks, this work focuses on the computational modeling of structured human motion through an integrated architecture that simulates and adapts physical postures in dynamic environments. Traditional approaches to motion simulation often lack adaptability across diverse morphological profiles and task semantics, resulting in reduced biomechanical plausibility and limited contextual alignment. These methods frequently overlook higher-order kinematic constraints, rely excessively on frame-wise prediction, and exhibit poor cross-domain generalization. To address these challenges, a hybrid framework is proposed, comprising a Kinematic-Aware Neural Projection Network (KANet) and a Biomechanically-Constrained Latent Adaptation (BCLA) module. KANet preserves temporal and anatomical dependencies via a dual-stream attention mechanism and graph-based encoding, enabling precise motion sequence synthesis within a symbolic latent space. By embedding structural priors and joint-specific constraints directly into the modeling process, it enhances interpretability and physical fidelity. BCLA complements this by introducing a contextual adaptation layer that integrates biomechanical heuristics and soft constraints to couple motion trajectories with personalized physical parameters and task conditions. Expressing adaptation as a residual transformation across the latent motion manifold, BCLA enhances robustness and zero-shot generalizability. Experimental results demonstrate significant improvements in biomechanical plausibility, motion smoothness, and contextual adaptability compared to state-of-the-art generative models.<\/p>\n\n\n\n

Keywords: Symbolic Modeling, Biomechanical Constraint, Motion Adaptation, Generative Learning, Intelligent Systems<\/em><\/p>\n\n\n\n

<\/div>\n\n\n\n
\n
\n
\n
\n
\n
    \n
  1. [1] H. Liu, K. Chen, Y. Li, Z. Huang, J. Duan, and J. Ma. \u201cIntegrated behavior planning and motion control for autonomous vehicles with traffic rules compliance\u201d. In: 2023 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE. 2023, 1\u20137. DOI: 10.1109\/ROBIO58561.2023.10354858.<\/li>\n
  2. [2] M. A. Ur Rahman Efti and M. I. Hosen, (2023) \u201c3DOF Intelligent Rehabilitation Robot Design for Knee and Ankle\u201d Journal of Artificial Intelligence and System Modelling 1(01): 15\u201331. DOI: 10.22034\/jaism.2023.423292.1005.<\/li>\n
  3. [3] J. Shi, T. Zhang, J. Zhan, S. Chen, J. Xin, and N. Zheng. \u201cEfficient lane-changing behavior planning via reinforcement learning with imitation learning initialization\u201d. In: 2023 IEEE Intelligent Vehicles Symposium (IV). IEEE. 20<\/span>23, 1\u20138. DOI: 10.1109\/IV55152.2023.10186577.<\/li>\n
  4. [4] Z. Huang, H. Liu, J. Wu, and C. Lv, (2023) \u201cConditional predictive behavior planning with inverse reinforcement learning for human-like autonomous driving\u201d IEEE Transactions on Intelligent Transportation Systems 24(7): 7244\u20137258. DOI: 10.1109\/TITS.2023.3254579.<\/li>\n
  5. [5] M. Klimke, B. V\u00f6lz, and M. Buchholz. \u201cCooperative behavior planning for automated driving using graph neural networks\u201d. In: 2022 IEEE Intelligent Vehicles Symposium (IV). IEEE. 2022, 167\u2013174. DOI: 10.1109\/IV51971.2022.9827230.<\/li>\n
  6. [6] Z. Qiao, J. Schneider, and J. M. Dolan. \u201cBehavior planning at urban intersections through hierarchical reinforcement learning\u201d. In: 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE. 2021, 2667\u20132673. DOI: 10.1109\/ICRA48506.2021.9561095.<\/li>\n
  7. [7] J. Li, L. Sun, W. Zhan, and M. Tomizuka. \u201cInteraction-aware behavior planning for autonomous vehicles validated with real traffic data\u201d. In: Dynamic Systems and Control Conference. 84287. American Society of Mechanical Engineers. 2020, V002T31A005. DOI: 10.1115\/DSCC2020-3328.<\/li>\n
  8. [8] K. Esterle, T. Kessler, and A. Knoll. \u201cOptimal behavior planning for autonomous driving: A generic mixed-integer formulation\u201d. In: 2020 IEEE Intelligent Vehicles Symposium (IV). IEEE. 2020, 1914\u20131921. DOI: 10.1109\/IV47402.2020.9304743.<\/li>\n
  9. [9] M. Janner, Y. Du, J. B. Tenenbaum, and S. Levine, (2022) \u201cPlanning with diffusion for flexible behavior synthesis\u201d arXiv preprint arXiv:2205.09991: DOI: 10.48550\/arXiv.2205.09991.<\/li>\n
  10. [10] N. Ahmed, C. Li, A. Khan, S. A. Qalati, S. Naz, and F. Rana, (2021) \u201cPurchase intention toward organic food among young consumers using theory of planned behavior: role of environmental concerns and environmental awareness\u201d Journal of Environmental Planning and Management 64(5): 796\u2013822. DOI: 10.1080\/09640568.2020.1785404.<\/li>\n
  11. [11] M. Chignoli, D. Kim, E. Stanger-Jones, and S. Kim. \u201cThe MIT humanoid robot: Design, motion planning, and control for acrobatic behaviors\u201d. In: 2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids). IEEE. 2021, 1\u20138. DOI: 10.1109\/HUMANOIDS47582.2021.9555782.<\/li>\n
  12. [12] R. Lavuri, (2022) \u201cExtending the theory of planned behavior: factors fostering millennials’ intention to purchase eco-sustainable products in an emerging market\u201d Journal of Environmental Planning and Management 65(8): 1507\u20131529. DOI: 10.1080\/09640568.2021.1933925.<\/li>\n
  13. [13] W. Ding, L. Zhang, J. Chen, and S. Shen, (2021) \u201cEpsilon: An efficient planning system for automated vehicles in highly interactive environments\u201d IEEE Transactions on Robotics 38(2): 1118\u20131138. DOI: 10.1109\/TRO.2021.3104254.<\/li>\n
  14. [14] K. Hamilton, A. van Dongen, and M. S. Hagger, (2020) \u201cAn extended theory of planned behavior for parent-for-child health behaviors: A meta-analysis.\u201d Health psychology 39(10): 863. DOI: 10.1037\/hea0000940.<\/li>\n
  15. [15] S. Zhu and B. Aksun-Guvenc, (2020) \u201cTrajectory planning of autonomous vehicles based on parameterized control optimization in dynamic on-road environments\u201d Journal of Intelligent & Robotic Systems 100(3): 1055\u20131067. DOI: 10.1007\/s10846-020-01215-y.<\/li>\n
  16. [16] T. Salzmann, B. Ivanovic, P. Chakravarty, and M. Pavone. \u201cTrajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data\u201d. In: European Conference on Computer Vision. Springer. 2020, 683\u2013700. DOI: 10.1007\/978-3-030-58523-5_40.<\/li>\n
  17. [17] Z. Liu and A. Cui, (2025) \u201cAn effective evaluation method of college students\u2019 innovation and entrepreneurship education based on data mining algorithm\u201d International Journal of High Speed Electronics and Systems: 2540405. DOI: 10.1142\/S012915642540405X.<\/li>\n
  18. [18] C.-Q. Zhang, R. Fang, R. Zhang, M. S. Hagger, and K. Hamilton, (2020) \u201cPredicting hand washing and sleep hygiene behaviors among college students: Test of an integrated social-cognition model\u201d International journal of environmental research and public health 17(4): 1209. DOI: 10.3390\/ijerph17041209.<\/li>\n
  19. [19] J. S. Park, J. O\u2019Brien, C. J. Cai, M. R. Morris, P. Liang, and M. S. Bernstein. \u201cGenerative agents: Interactive simulacra of human behavior\u201d. In: Proceedings of the 36th annual acm symposium on user interface software and technology. 2023, 1\u201322. DOI: 10.1145\/3586183.3606763.<\/li>\n
  20. [20] L. Wang, (2025) \u201cA Method for Pushing Interest Points of English Learning Resources Based on Mobile Terminal User Profiles\u201d International Journal of High Speed Electronics and Systems: 2540406. DOI: 10.1142\/S0129156425404061.<\/li>\n
  21. [21] I. Ajzen, (2020) \u201cThe theory of planned behavior: Frequently asked questions\u201d Human behavior and emerging technologies 2(4): 314\u2013324. DOI: 10.1002\/hbe2.195.<\/li>\n
  22. [22] M. S. Hagger, M. W.-L. Cheung, I. Ajzen, and K. Hamilton, (2022) \u201cPerceived behavioral control moderating effects in the theory of planned behavior: A meta-analysis.\u201d Health Psychology 41(2): 155. DOI: 10.1037\/hea0001153.<\/li>\n
  23. [23] H. Han, (2021) \u201cConsumer behavior and environmental sustainability in tourism and hospitality: A review of theories, concepts, and latest research\u201d Journal of Sustainable Tourism: 1\u201322. DOI: 10.1080\/09669582.2021.1903019.<\/li>\n
  24. [24] M. Bosnjak, I. Ajzen, and P. Schmidt, (2020) \u201cThe theory of planned behavior: Selected recent advances and applications\u201d Europe\u2019s journal of psychology 16(3): 352. DOI: 10.5964\/ejop.v16i3.3107.<\/li>\n
  25. [25] A. Yuriev, M. Dahmen, P. Paill\u00e9, O. Boiral, and L. Guillaumie, (2020) \u201cPro-environmental behaviors through the lens of the theory of planned behavior: A scoping review\u201d Resources, Conservation and Recycling 155: 104660. DOI: 10.1016\/j.resconrec.2019.104660.<\/li>\n
  26. [26] F. La Barbera and I. Ajzen, (2020) \u201cControl interactions in the theory of planned behavior: Rethinking the role of subjective norm\u201d Europe\u2019s Journal of Psychology 16(3): 401. DOI: 10.5964\/ejop.v16i3.2056.<\/li>\n
  27. [27] S. Karaman, M. R. Walter, A. Perez, E. Frazzoli, and S. Teller. \u201cAnytime motion planning using the RRT*\u201d. In: 2011 IEEE international conference on robotics and automation. ieee. 2011, 1478\u20131483. DOI: 10.1109\/ICRA.2011.5980479.<\/li>\n
  28. [28] A. Sadat, S. Casas, M. Ren, X. Wu, P. Dhawan, and R. Urtasun. \u201cPerceive, predict, and plan: Safe motion planning through interpretable semantic representations\u201d. In: European Conference on Computer Vision. Springer. 2020, 414\u2013430. DOI: 10.1007\/978-3-030-58592-1_25.<\/li>\n
  29. [29] H. B. Taing and Y. Chang, (2021) \u201cDeterminants of tax compliance intention: Focus on the theory of planned behavior\u201d International journal of public administration 44(1): 62\u201373. DOI: 10.1080\/01900692.2020.1728313.<\/li>\n
  30. [30] P. Hang, C. Lv, C. Huang, J. Cai, Z. Hu, and Y. Xing, (2020) \u201cAn integrated framework of decision making and motion planning for autonomous vehicles considering social behaviors\u201d IEEE transactions on vehicular technology 69(12): 14458\u201314469. DOI: 10.1109\/TVT.2020.3040398.<\/li>\n
  31. [31] L. Canova, A. Bobbio, and A. M. Manganelli, (2020) \u201cBuying organic food products: the role of trust in the theory of planned behavior\u201d Frontiers in psychology 11: 575820. DOI: 10.3389\/fpsyg.2020.575820.<\/li>\n
  32. [32] F. La Barbera and I. Ajzen, (2021) \u201cModerating role of perceived behavioral control in the theory of planned behavior: A preregistered study\u201d Journal of Theoretical Social Psychology 5(1): 35\u201345. DOI: 10.1002\/jts5.83.<\/li>\n
  33. [33] E. N. \u00c7oker and S. van der Linden, (2022) \u201cFleshing out the theory of planned behavior: Meat consumption as an environmentally significant behavior\u201d Current Psychology 41(2): 681\u2013690. DOI: 10.1007\/s12144-019-00593-3.<\/li>\n
  34. [34] A. Bagheri, N. Emami, and C. A. Damalas, (2021) \u201cFarmers’ behavior towards safe pesticide handling: An analysis with the theory of planned behavior\u201d Science of the total Environment 751: 141709. DOI: 10.1016\/j.scitotenv.2020.141709.<\/li>\n
  35. [35] M. A. Ur Rahman Efti and M. I. Hosen, (2023) \u201c3DOF Intelligent Rehabilitation Robot Design for Knee and Ankle\u201d Journal of Artificial Intelligence and System Modelling 1(01): 15\u201331. DOI: 10.22034\/jaism.2023.423292.1005.<\/li>\n
  36. [36] Z. Ren, M. Jin, H. Nie, J. Shen, A. Dong, and Q. Zhang, (2025) \u201cTowards Realistic Human Motion Prediction with Latent Diffusion and Physics-Based Models\u201d Electronics 14(3): 605. DOI: 10.3390\/electronics14030605.<\/li>\n
  37. [37] Y. Jia, (2025) \u201cTransformer-based video generation technique for biomechanical motion analysis\u201d Molecular & Cellular Biomechanics 22(4): DOI: 10.62617\/mcb1581.<\/li>\n
  38. [38] D. Bose, B. Arora, A. K. Srivastava, and P. Garg. \u201cA Computer Vision Based Framework for Posture Analysis and Performance Prediction in Athletes\u201d. In: 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE). IEEE. 2024, 942\u2013947. DOI: 10.1109\/IC3SE62002.2024.10593041.<\/li>\n
  39. [39] Z. Tian, F. Dong, D. Li, and C. Liu, (2024) \u201cRETRACTED ARTICLE: 3D motion trajectory prediction based on optical image remote sensing data in sports training simulation\u201d Optical and Quantum Electronics 56(3): 305. DOI: 10.1007\/s11082-023-05995-z.<\/li>\n
  40. [40] A. A. Phatak, F.-G. Wieland, K. Vempala, F. Volkmar, and D. Memmert, (2021) \u201cArtificial intelligence based body sensor network framework\u2014narrative review: proposing an end-to-end framework using wearable sensors, real-time location systems and artificial intelligence\/machine learning algorithms for data collection, data mining and knowledge discovery in sports and healthcar<\/span>e\u201d Sports Medicine-Open 7(1): 79. DOI: 10.1186\/s40798-021-00372-0.<\/li>\n
  41. [41] X. Lei, F. Xie, and C. Jin, (2024) \u201cGram Angle Field-Based Siamese Graph Convolutional Neural Network for Hyperspectral Images Classification\u201d IEEE Geoscience and Remote Sensing Letters: DOI: 10.1109\/LGRS.2024.3521461.<\/li>\n
  42. [42] B. C. Schwartzman. An apprenticeship-model employment program for adults with developmental disabilities: An exploratory study. University of California, Los Angeles, 2015.<\/li>\n
  43. [43] J. Xu, (2024) \u201cAnalysis and Improvement of the Application of Playground Sports Posture Detection Technology in Physical Education Teaching and Training\u201d EAI Endorsed Transactions on Pervasive Health & Technology 10(1): DOI: 10.4108\/eetpht.10.5161.<\/li>\n
  44. [44] Y. Li and W. Jing. \u201cHuman Motion Posture Recognition based on Spatiotemporal Graph CNN\u201d. In: 2025 3rd International Conference on Data Science and Information System (ICDSIS). IEEE. 2025, 1\u20135. DOI: 10.1109\/ICDSIS65355.2025.11070350.<\/li>\n
  45. [45] S. Wei and X. Li, (2024) \u201cRETRACTED ARTICLE: Simulation of optical fiber sensor in motion training image analysis system based on human posture tracking algorithm\u201d Optical and Quantum Electronics 56(3): 299. DOI: 10.1007\/s11082-023-05996-y.<\/li>\n
  46. [46] Z. Chen and Y. Pan. \u201cConstruction of Sports Training Evaluation System Based on Virtual Reality and Motion Capture\u201d. In: 2023 2nd International Conference on 3D Immersion, Interaction and Multi-sensory Experiences (ICDIIME). IEEE. 2023, 50\u201354. DOI: 10.1109\/ICDIIME59043.2023.00016.<\/li>\n
  47. [47] W. Zhang. \u201cResearch on Sports Video Image Teaching System Based on Computer 3D Technology\u201d. In: 2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). IEEE. 2023, 1721\u20131725. DOI: 10.1109\/EEBDA56825.2023.10090695.<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\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":[12,1483,6],"tags":[1670],"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.202610_33.056\u00a0\u00a0 Download PDF In the evolving landscape of intelligent computing and digital modeling, simulating…","_links":{"self":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope\/9321"}],"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=9321"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9321"},{"taxonomy":"post_tag","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9321"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}