提出新模型提升帕金森步态识别可靠性，揭示现有方法缺陷
Benchmarking Reliability of Deep Learning Models for Pathological Gait Classification
- 设计异步多流图卷积网络，融合多源步态数据
- 在4个数据集上实现跨域稳定分类，准确率超90%
- 揭露传感器模拟与真实数据差异导致的泛化问题
早期发现神经退行性疾病是重要科学挑战，因早期干预可能改善预后。近年来研究尝试利用机器学习算法检测异常步态，或可指示神经退行性病因出现。然而尽管文献中已有诸多关于高精度检测的宣称，实际应用仍远未实现。本文通过在三个基于Kinect模拟和一个真实帕金森患者数据集上的实验，分析现有方法的局限，揭示其误差来源与泛化失败原因。基于此，提出强基线模型Asynchronous Multi-Stream Graph Convolutional Network (AMS-GCN)，可在多个数据集间可靠区分多种病理步态类别。
原文摘要 · Abstract (English)
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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