Real-Time Musculoskeletal Surrogates for Pediatric Cerebral Palsy: a Credibility Pilot

2026-08-28

AI Summary

研究开发了一种基于因果神经网络的实时肌肉骨骼替代模型，用于儿童脑瘫个性化康复，解决了模型可信度、低延迟和不确定性校准问题。

Abstract

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.

Problem

• real-time musculoskeletal surrogates
• cerebral palsy
• personalized rehabilitation
• credibility
• inference latency

Innovation

• real-time musculoskeletal surrogates
• causal neural surrogate
• low inference latency
• personalized rehabilitation
• pediatric cerebral palsy