脑瘫儿童个性化康复的新模型，预测准确且响应快，但肌肉力量建模仍是挑战。
研究人员开发了一种针对儿童脑瘫的实时肌肉骨骼代理模型，使用OpenSim静态参数、关节运动学数据和真实肌肉容量。该模型在9名儿童数据集上测试，肌腱长度预测准确率R平方达0.92-0.95，nRMSE低于8%。模型推理时间仅需亚毫秒至几毫秒，远低于100毫秒的康复交互目标。蒙特卡洛可信度测试显示，仅±5%的人体测量和肌肉容量变化会导致过度自信的区间估计。
Real-Time Musculoskeletal Surrogates for Pediatric Cerebral Palsy: a Credibility Pilot
Real-time musculoskeletal (MSK) surrogates could support personalized rehabilitation for children with cerebral palsy (CP), but their credibility depends on subject-wise evaluation, low inference latency, and calibrated uncertainty. We develop a subject-conditioned causal neural surrogate using OpenSim-derived static parameters, temporal joint kinematics, true muscle capacities, and training-only perturbations. On a real pediatric CP gait dataset comprising nine children, we use leave-one-subject-out validation on six development subjects and evaluate a frozen configuration once on three locked test subjects. The surrogate accurately reproduces musculotendon lengths (R-square = 0.92 in development validation and approximately 0.95 on locked subjects; nRMSE < 8%) while requiring only sub-millisecond to few-millisecond neural inference, well below a 100 ms interactive-rehabilitation target. In contrast, direct muscle-force estimation remains unstable at this small, heterogeneous scale: pooled metrics can overstate within-subject, per-muscle accuracy. A Monte Carlo credibility pilot further shows that propagating only +/-5% anthropometry and muscle-capacity variation produces severely overconfident nominal 90% intervals (approximately 4% force coverage and below 1% MT-length coverage). These results establish a leakage-free evaluation and credibility framework for pediatric MSK surrogates, while identifying force modeling and epistemic uncertainty as the central next challenges for clinically credible digital twins.