Tracking Human Motion With Multichannel Interacting Multiple Model
Tracking Human Motion With Multichannel Interacting Multiple Model
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DOI：
10.1109/tii.2013.2257804
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发表时间：
2013-04
影响因子：
12.3
通讯作者：
S. Lee;Yuichi Motai;Hongsik Choi
中科院分区：
文献类型：
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作者：
S. Lee;Yuichi Motai;Hongsik Choi
使用多个身体传感器跟踪人体运动具有促进大量应用的潜力，例如检测患者的运动，以及监控基于家庭的应用。在多个传感器的情况下，由于数据关联的局限性，跟踪系统的体系结构和数据处理无法执行预期的结果。针对运动跟踪的协作性和智能化应用(Polhemus Liberty AC磁力跟踪器)，提出了一种基于多通道交互多模型估计器的人体运动跟踪系统(MC-IMME)。为了找出分布式传感器之间的交互关系，我们使用了高斯混合模型(GMM)进行聚类。利用一种基于GMM和期望最大化算法的分布式传感器协同分组方法，可以估计出与多个身体传感器的交互关系，并利用簇内的跟踪关系实现高效的目标估计。提出的MC-IMME算法利用多个带滤波发散的模型，可以有效地从人体感官数据的测量数据集中估计出测量值和速度。我们新开发了MC-IMME，通过马尔可夫切换概率和适当的分组方法来提高整体性能。实验结果表明，采用跟踪关系可以使预测超调误差平均提高19.31%。
Tracking human motion with multiple body sensors has the potential to promote a large number of applications such as detecting patient motion, and monitoring for home-based applications. With multiple sensors, the tracking system architecture and data processing cannot perform the expected outcomes because of the limitations of data association. For the collaborative and intelligent applications of motion tracking (Polhemus Liberty AC magnetic tracker), we propose a human motion tracking system with multichannel interacting multiple model estimator (MC-IMME). To figure out interactive relationships among distributed sensors, we used a Gaussian mixture model (GMM) for clustering. With a collaborative grouping method based on GMM and expectation-maximization algorithm for distributed sensors, we can estimate the interactive relationship with multiple body sensors and achieve the efficient target estimation to employ a tracking relationship within a cluster. Using multiple models with filter divergence, the proposed MC-IMME can achieve the efficient estimation of the measurement and the velocity from measured datasets of human sensory data. We have newly developed MC-IMME to improve overall performance with a Markov switch probability and a proper grouping method. The experiment results shows that the prediction overshoot error can be improved on average by 19.31% by employing a tracking relationship.