用混合高斯过程模型实现单样本人体运动学习，适合医疗康复场景
Single-Example Learning in a Mixture of GPDMs with Latent Geometries
- 构建多高斯过程动态模型混合框架，融合几何特征编码序列
- 单样本下分类准确率超基线模型，生成动作自然连贯
- 适用于数据稀缺的假肢控制等个性化医疗应用
我们提出高斯过程动态混合模型（GPDMM），用于人体运动数据的单样本学习。该模型基于高斯过程动态模型（GPDM），后者是高斯过程隐变量模型（GPLVM）的一种，通过隐马尔可夫模型动力学先验进行优化。GPDMM 在概率专家混合框架中组合多个 GPDM，利用嵌入几何特征将多样序列编码至单一潜在空间，实现各序列类别的分类与生成。GPDM 及其混合模型在数据有限、可解释性关键的场景中表现优异，如患者特异性医疗应用（如假肢控制）。我们在单样本学习任务中评估了 GPDMM 的分类精度和生成能力，展示了模型变体，并与 LSTM、VAE 及 Transformer 进行对比。
原文摘要 · Abstract (English)
We present the Gaussian process dynamical mixture model (GPDMM) and show its utility in single-example learning of human motion data. The Gaussian process dynamical model (GPDM) is a form of the Gaussian process latent variable model (GPLVM), but optimized with a hidden Markov model dynamical prior. The GPDMM combines multiple GPDMs in a probabilistic mixture-of-experts framework, utilizing embedded geometric features to allow for diverse sequences to be encoded in a single latent space, enabling the categorization and generation of each sequence class. GPDMs and our mixture model are particularly advantageous in addressing the challenges of modeling human movement in scenarios where data is limited and model interpretability is vital, such as in patient-specific medical applications like prosthesis control. We score the GPDMM on classification accuracy and generative ability in single-example learning, showcase model variations, and benchmark it against LSTMs, VAEs, and transformers.
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