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首页> 外文会议>2016 IEEE First Conference on Connected Health: Applications, Systems and Engineering Technologies >Real-Time Data-Driven Gait Phase Detection Using Infinite Gaussian Mixture Model and Parallel Particle Filter
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Real-Time Data-Driven Gait Phase Detection Using Infinite Gaussian Mixture Model and Parallel Particle Filter

机译：使用无限高斯混合模型和并行粒子滤波的实时数据驱动步态相位检测

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摘要

The world is experiencing an unprecedented, enduring, and pervasive aging process. With more people who need walking assistance, the demand for gait rehabilitation has increased rapidly over the years. Effective gait rehabilitation requires a comprehensive gait analysis, in which gait phase detection plays an important role. Although many specialized sensing systems have been developed for gait monitoring, most existing gait phase detection algorithms rely on significant input from medical professionals, which are subjective, manual and inaccurate. To address these problems, this paper presents a datadriven approach for real-time gait phase detection. The approach combines an infinite Gaussian mixture model (IGMM) to classify different gait phases based on the ground contact force (GCF) measurement, and a parallel particle filter to estimate and update the model parameters. Effective particle sharing mechanisms are further designed to distribute particles among different working nodes judiciously and thus strike a good balance between computational overhead and estimation accuracy. The proposed algorithm is implemented in our gait monitoring and analysis platform developed on Microsoft Azure, and examined using the data trace collected from a healthy human subject. The algorithm effectiveness is validated through extensive experiments.
机译：世界正经历着前所未有的，持久的和普遍的衰老过程。多年来，随着更多需要步行帮助的人，步态康复的需求迅速增加。有效的步态康复需要全面的步态分析，其中步态阶段检测起着重要的作用。尽管已经开发了许多专门的步态监测传感系统，但是大多数现有的步态相位检测算法都依赖于医疗专业人员的大量输入，这些输入是主观，手动和不准确的。为了解决这些问题，本文提出了一种用于实时步态相位检测的数据驱动方法。该方法结合了无限高斯混合模型（IGMM）以基于地面接触力（GCF）测量对不同的步态相位进行分类，以及并行粒子滤波器以估计和更新模型参数。还设计了有效的粒子共享机制，以合理地在不同的工作节点之间分配粒子，从而在计算开销和估计精度之间取得良好的平衡。拟议的算法在我们基于Microsoft Azure开发的步态监视和分析平台中实现，并使用从健康人类受试者收集的数据跟踪进行了检查。通过大量实验验证了算法的有效性。

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