发表机构
Brooks College of Health, University of North Florida; College of Computing, Engineering & Construction, University of North Florida（北佛罗里达大学布鲁克斯健康学院; 北佛罗里达大学计算、工程与建设学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种结合卷积降秩自编码与前馈回归的潜在结构信息框架，从视频重建三维人体运动，在辅助坐站转换、无辅助坐站转换和多向跳跃中验证，误差低至毫米级，连接了运动重建与生物力学特征。
AI 中文摘要
三维人体运动学的量化是神经康复和肌肉骨骼研究的基础，然而非典型姿态、物理辅助和快速运动造成了部分可观测的条件。在此，我们评估了一种结合卷积降秩自编码和前馈回归的潜在结构信息框架，用于从同步视频中重建运动。三个基于案例的演示涵盖了脑瘫儿童在治疗师辅助下的坐站转换、典型发育儿童的无辅助坐站转换以及年轻成人的多向单腿跳跃。通过位置轨迹、投影角度描述符和髋膝耦合来检查重建结果。在排除于模型训练之外的测试重复中，辅助坐站转换期间预测标记位置的平均绝对误差范围为12.02至57.11毫米。在典型发育案例中，矢状面膝关节和躯干描述符误差分别为5.32°和5.38°。髋膝周期图捕捉了两种案例中耦合运动模式的方面，其中典型发育案例的轨迹对应更接近。十六次测试跳跃产生了垂直膝关节标记的平均绝对误差为15.58毫米。这些发现将潜在结构信息重建与生物力学可解释的运动特征联系起来，为跨越临床非典型运动和高动态运动表现的视频衍生评估提供了基础。
英文摘要
The quantification of three-dimensional human kinematics is fundamental to neurorehabilitation and musculoskeletal research, yet atypical posture, physical assistance, and rapid movement create conditions of partial observability. Here, we evaluate a latent-structure-informed framework combining convolutional rank-reduction autoencoding with feedforward regression to reconstruct movement from synchronized video. Three case-based demonstrations span therapist-assisted sit-to-stand in a child with cerebral palsy, unassisted sit-to-stand in a typically developing child, and multidirectional single-leg hopping in a young adult. Reconstruction was examined through positional trajectories, projected angular descriptors, and hip-knee coupling. Across test repetitions excluded from model training, mean absolute errors in a predicted marker position during assisted sit-to-stand ranged from 12.02 to 57.11 mm. Sagittal knee and trunk descriptor errors were 5.32° and 5.38° in the typically developing case. Hip-knee cyclograms captured aspects of coupled-motion patterns in both cases, with closer trajectory correspondence in the typically developing case. Sixteen test hops yielded a vertical knee-marker mean absolute error of 15.58 mm. These findings connect latent-structure-informed reconstruction with biomechanically interpretable movement features, providing a foundation for video-derived assessment spanning clinically atypical movement and high-dynamic athletic movement.
