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.

翻译：步态分析对于婴儿运动神经障碍的早期检测与干预至关重要。尽管其重要性显著，传统方法往往难以有效建模婴儿步态固有的高变异性和快速发育变化。为应对这些挑战，我们提出一种概率式高斯过程驱动的隐马尔可夫模型，以捕捉婴儿步态周期的复杂时序动态，并实现步态异常的自动识别。我们采用多输出高斯过程框架来建模多个步态信号间的相互依赖关系，其复合核函数专为刻画步态周期中表现出的平滑、非平滑及周期性行为而设计。隐马尔可夫模型将步态相位分割为正常与异常状态，从而精准识别站立相与摆动相中的病理性运动模式。通过留一受试者交叉验证，使用包含有无运动神经障碍婴儿的数据集对所提模型进行训练与评估。结果表明，在步态动态建模方面，多输出高斯过程优于基于长短期记忆的神经网络，在准确性、方差解释和时序对齐方面均有提升。此外，多输出高斯过程的预测性能为不确定性量化提供了理论框架，可实现对步态轨迹预测的置信度估计。同时，隐马尔可夫模型通过显式建模步态相位转换增强了可解释性，提升了跨多个步态周期的细微异常检测能力。这些发现凸显了多输出高斯过程-隐马尔可夫模型框架作为鲁棒自动步态分析工具的价值，为患有神经运动障碍的婴儿提供了早期诊断与干预策略。