Human motion analysis provides useful information for the diagnosis and recovery assessment of people suffering from pathologies, such as those affecting the way of walking, i.e., gait. With recent developments in deep learning, state-of-the-art performance can now be achieved using a single 2D-RGB-camera-based gait analysis system, offering an objective assessment of gait-related pathologies. Such systems provide a valuable complement/alternative to the current standard practice of subjective assessment. Most 2D-RGB-camera-based gait analysis approaches rely on compact gait representations, such as the gait energy image, which summarize the characteristics of a walking sequence into one single image. However, such compact representations do not fully capture the temporal information and dependencies between successive gait movements. This limitation is addressed by proposing a spatiotemporal deep learning approach that uses a selection of key frames to represent a gait cycle. Convolutional and recurrent deep neural networks were combined, processing each gait cycle as a collection of silhouette key frames, allowing the system to learn temporal patterns among the spatial features extracted at individual time instants. Trained with gait sequences from the GAIT-IT dataset, the proposed system is able to improve gait pathology classification accuracy, outperforming state-of-the-art solutions and achieving improved generalization on cross-dataset tests.

中文翻译：

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自动病理步态分类的时空深度学习方法

人体运动分析为患有疾病的人的诊断和康复评估提供了有用的信息，例如影响步行方式的人，即步态。随着深度学习的最新发展，现在可以使用单个基于 2D-RGB 相机的步态分析系统来实现最先进的性能，提供对步态相关病理的客观评估。此类系统为当前主观评估的标准实践提供了有价值的补充/替代方案。大多数基于 2D-RGB 相机的步态分析方法依赖于紧凑的步态表示，例如步态能量图像，它将步行序列的特征汇总为一张图像。然而，这种紧凑的表示不能完全捕捉连续步态运动之间的时间信息和依赖性。通过提出使用关键帧的选择来表示步态周期的时空深度学习方法来解决此限制。卷积和循环深度神经网络相结合，将每个步态周期作为轮廓关键帧的集合进行处理，使系统能够学习在各个时刻提取的空间特征中的时间模式。使用来自 GAIT-IT 数据集的步态序列进行训练，所提出的系统能够提高步态病理分类的准确性，优于最先进的解决方案，并在跨数据集测试中实现更好的泛化。将每个步态周期处理为轮廓关键帧的集合，允许系统学习在各个时间点提取的空间特征中的时间模式。使用来自 GAIT-IT 数据集的步态序列进行训练，所提出的系统能够提高步态病理分类的准确性，优于最先进的解决方案，并在跨数据集测试中实现更好的泛化。将每个步态周期处理为轮廓关键帧的集合，允许系统学习在各个时间点提取的空间特征中的时间模式。使用来自 GAIT-IT 数据集的步态序列进行训练，所提出的系统能够提高步态病理分类的准确性，优于最先进的解决方案，并在跨数据集测试中实现更好的泛化。