高斯过程驱动的隐马尔可夫模型用于婴儿步态异常的早期诊断
Gaussian Process-driven Hidden Markov Models for Early Diagnosis of Infant Gait Anomalies
AI总结：
本文提出高斯过程驱动的隐马尔可夫模型（MoGP-HMM），通过多输出高斯过程建模婴儿步态信号间依赖关系，并结合HMM分割正常与异常步态阶段，在婴儿运动神经障碍早期诊断中优于LSTM基线，实现了更准确的异常检测与不确定性量化。
摘要：
步态分析在婴儿运动神经障碍的早期检测和干预中至关重要。尽管其重要性不言而喻，传统方法往往难以对婴儿步态固有的高变异性和快速发育变化进行建模。为应对这些挑战，我们提出了一种概率性的高斯过程（GP）驱动的隐马尔可夫模型（HMM），以捕捉婴儿步态周期的复杂时间动态，并实现步态异常的自动识别。我们使用多输出GP（MoGP）框架来建模多个步态信号之间的相互依赖关系，并设计了一个复合核函数，以考虑步态周期中表现出的平滑、非平滑和周期性行为。HMM将步态阶段分割为正常和异常状态，有助于精确识别站立期和摆动期中的病理性运动模式。所提出的模型使用包含有和无运动神经障碍婴儿的数据集，通过留一被试交叉验证进行训练和评估。结果表明，MoGP在建模步态动态方面优于基于长短期记忆（LSTM）的神经网络，在准确性、方差解释和时间对齐方面均有提升。此外，MoGP的预测性能为不确定性量化提供了一个有原则的框架，允许对步态轨迹预测进行置信度估计。另外，HMM通过显式建模步态阶段转换增强了可解释性，提高了对多个步态周期中细微异常的检测能力。这些发现凸显了MoGP-HMM框架作为一种稳健的自动步态分析工具的价值，可为患有神经运动障碍的婴儿提供早期诊断和干预策略。
英文摘要：
Gait analysis is critical in the early detection and intervention of motor neurological disorders in infants. Despite its importance, traditional methods often struggle to model the high variability and rapid developmental changes inherent to infant gait. To address these challenges, we propose a probabilistic Gaussian Process (GP)-driven Hidden Markov Model (HMM) to capture the complex temporal dynamics of infant gait cycles and enable automatic recognition of gait anomalies. We use a Multi-Output GP (MoGP) framework to model interdependencies between multiple gait signals, with a composite kernel designed to account for smooth, non-smooth, and periodic behaviors exhibited in gait cycles. The HMM segments gait phases into normal and abnormal states, facilitating the precise identification of pathological movement patterns in stance and swing phases. The proposed model is trained and assessed using a dataset of infants with and without motor neurological disorders via leave-one-subject-out cross-validation. Results demonstrate that the MoGP outperforms Long Short-Term Memory (LSTM) based neural networks in modeling gait dynamics, offering improved accuracy, variance explanation, and temporal alignment. Further, the predictive performance of MoGP provides a principled framework for uncertainty quantification, allowing confidence estimation in gait trajectory predictions. Additionally, the HMM enhances interpretability by explicitly modeling gait phase transitions, improving the detection of subtle anomalies across multiple gait cycles. These findings highlight the MoGP-HMM framework as a robust automatic gait analysis tool, allowing early diagnosis and intervention strategies for infants with neurological motor disorders.
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