用于病理步态分类的深度学习模型可靠性基准测试
Benchmarking Reliability of Deep Learning Models for Pathological Gait Classification
AI总结：
本文分析了深度学习在病理步态分类中阻碍实际应用的泛化问题，通过跨数据集实验揭示了现有方法的错误来源，并提出强基线模型 AMS-GCN 以可靠区分多类病理步态。
摘要：
神经退行性疾病的早期检测是一个重要的开放性问题，因为早期诊断和治疗可能会带来更好的预后。研究人员最近试图利用机器学习算法的进展来检测步态改变的症状，这可能对应于神经退行性病因学的出现。然而，尽管近期文献中使用各种传感器和算法提出了许多准确检测的积极声明，但这些解决方案距离在实际中实现还相差甚远。本文分析了现有方法，以找出阻碍其转化的差距。通过在三个 Kinect 模拟数据集和一个真实帕金森病患者数据集上进行的一系列实验，我们突出了这些方法中可能的错误来源和泛化失败。基于这些观察，我们提出了一个强大的基线，称为异步多流图卷积网络（AMS-GCN），它能够可靠地跨数据集区分多类病理步态。
英文摘要：
Early detection of neurodegenerative disorders is an important open problem, since early diagnosis and treatment may yield a better prognosis. Researchers have recently sought to leverage advances in machine learning algorithms to detect symptoms of altered gait, possibly corresponding to the emergence of neurodegenerative etiologies. However, while several claims of positive and accurate detection have been made in the recent literature, using a variety of sensors and algorithms, solutions are far from being realized in practice. This paper analyzes existing approaches to identify gaps inhibiting translation. Using a set of experiments across three Kinect-simulated and one real Parkinson's patient datasets, we highlight possible sources of errors and generalization failures in these approaches. Based on these observations, we propose our strong baseline called Asynchronous Multi-Stream Graph Convolutional Network (AMS-GCN) that can reliably differentiate multiple categories of pathological gaits across datasets.
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