The continuous iteration of virtual reality （VR） technology has driven the digital transformation of education，and its application in physics experiment teaching has gradually become a research hotspot. However，existing consumer-grade VR devices are limited by their tracking nodes； they fail to realize natural and smooth full-body motion reconstruction，leading to insufficient user immersion，discontinuous interaction experiences，and even visual discomfort （e.g.，motion sickness caused by conflicts between visual and vestibular perceptions）. These issues severely restrict the application of VR systems in high-demand immersive physics experiment scenarios that require precise interactions and strong sense of presence. This study aims to 1） address the technical bottlenecks of poor full-body motion reconstruction and low interaction fidelity in current VR-based physics experiments； 2） optimize the biomechanical rationality，motion naturalness，and interaction accuracy of virtual embodiments while achieving dynamic proportion calibration for users with different body types and consistency adjustment for cross-platform models； 3） explore the mechanism by which virtual embodied interaction influences user experience，realizing the synergistic optimization of immersive experience enhancement and physiological comfort （i.e.，reducing motion sickness）； and 4） provide a theoretical basis and practical guidance for the future implementation of multiuser collaborative virtual embodied interaction in the field of immersive physics education.

To achieve these objectives，this study proposes a virtual embodied motion-solving framework that integrates three core technologies： bone retargeting，inverse kinematics （IK），and data-driven motion matching. First，a hierarchical 3D human skeletal structure model is constructed. This model divides the human body into two biomechanically distinct subsystems，namely，spinal composite chains （pelvis-vertebrae-neck-head） and limb chains （upper limbs： shoulder-upper arm-lower arm-hand； lower limbs： pelvis-thigh-shank-foot），and adopts vertex skinning technology with blend shapes to avoid unnatural joint deformation caused by traditional linear blend skinning. For bone retargeting，a dual-calibration mechanism is designed. Global proportion calibration uses position data from head-mounted displays and hand controllers to calculate vertical and horizontal scaling factors； it adjusts the root joint position to match the user’s overall proportion while maintaining foot-ground contact. Local skeletal chain calibration adjusts the length of individual segments in five key chains （spinal chain，two arm chains，and two leg chains） while preserving internal length ratios to adapt to local body differences. For IK solving，a segmented strategy is adopted. An improved Gauss-Seidel algorithm （with joint angle constraints and multithreaded acceleration） is used for the head-spine composite chain to ensure stable convergence. A geometric analytical method with physiological limits （e.g.，elbow flexion 0°-150°） is applied to limb chains to avoid nonphysical phenomena，such as limb penetration. Meanwhile，a hybrid hand control scheme （integrating repositioning and motion correction） on the basis of the OpenXR skeleton system is implemented to ensure fine interaction accuracy. For data-driven motion matching，an animation database is built using an Xsens motion capture system； this database covers diverse gaits，speeds，and turning motions with key features，such as joint position and foot contact state. Real-time frame matching （via hierarchical indexing） and motion smoothing （via inertial interpolation） are realized to compensate for insufficient lower-limb tracking data. For evaluation，64 healthy college students from Beijing Normal University （31 males and 33 females aged 18-40，with no prior virtual embodiment experience） are stratified into an experimental group （using the improved virtual embodied interaction model） and a control group （using meta movement-based virtual embodiment） on the basis of scores from the visually induced motion sickness susceptibility questionnaire （VIMSSQ）. The experiment is conducted on a hardware platform that has an Intel i7-12700F processor，NVIDIA RTX 3070 graphics card，and Meta Quest 3 HMD，and scenes are developed in Unity 2021.3.32f1c1 （frame rate > 90 frame/s to meet real-time requirements）. Participants are asked to complete three basic tasks （i.e.，sitting on a chair，picking up a cup，button interaction with left/right hands） and a momentum-conservation physics experiment. Objective data （task completion time and completion rate） and subjective data （obtained via the embodiment questionnaire （EQ），igroup presence questionnaire （IPQ），and simulator sickness questionnaire （SSQ）） are collected and analyzed using MATLAB R2022b，Origin 2024b，and SPSS 27 （including independent samples t-test，Mann-Whitney U test，Pearson correlation，and multiple linear regression）.

Comprehensive experimental results verify the effectiveness of the proposed framework. In terms of quantitative task performance，Task 1 （sitting） has both groups achieve 100% completion rate，with similar average times （experimental group： 32.25 s； control group： 32.85 s）. In Task 2 （cup picking），the experimental group has a shorter average time compared with the control group （30.78 s vs. 32.00 s） and a stable completion rate （90% vs. 95%）. Task 3 （button interaction） reveals the experimental group’s advantage，especially in nondominant left-hand operations （average time： 36.45 s vs. 38.36 s； completion rate： 70% vs. 55%）. In terms of subjective experience，the EQ results show that the experimental group has significantly high scores in agency （p = 0.003，Cohen’s d = 1.028） and self-location （p = 0.037，Cohen’s d = 1.272） but no significant difference in ownership. The IPQ results indicate that the experimental group scores significantly high in general presence （p = 0.032），involvement （p = 0.012），and overall immersion （p = 0.045）. Multiple linear regression （R² = 0.543，p < 0.001） shows that the experimental group has low SSQ scores （β = -0.495，p < 0.001）； the IPQ scores negatively predict motion sickness （β = -0.493，p = 0.017）. The interaction terms （VIMSSQ × Group，β = -0.365，p = 0.024； IPQ × Group，β = -0.326，p = 0.026） confirm that the framework weakens the effect of motion sickness susceptibility and strengthens immersion’s mitigating effect on discomfort.

The proposed virtual embodied motion solving framework effectively addresses the technical limitations of current VR-based immersive physics experiments. By integrating bone retargeting，IK，and data-driven methods，it substantially enhances the biomechanical rationality （avoiding nonphysical phenomena，such as limb penetration），motion naturalness （via hierarchical skeletal modeling and inertial interpolation），and interaction accuracy （through hand optimization and local chain calibration） of virtual embodiments. The framework improves users’ embodiment sense （especially agency and self-location），immersion，and spatial motion perception and reduces visual discomfort via a dual-path mechanism （direct experience enhancement through improved presence； compensatory mitigation of motion sickness via strengthened immersion）. Future research may focus on three directions： exploring high-precision and personalized virtual embodiment generation （e.g.，physics experiment-specific motion libraries driven by large models），integrating physical laws and environmental factors to enhance interaction authenticity，and optimizing real-time performance for multiuser collaborative scenarios. This technology has broad application prospects in advancing immersive physics education and promoting the popularization of high-quality VR-based experimental teaching.