Adaptation of cluster analysis methods to optimize a biomechanical motion model of humans in a nursing bed

J. Demmer, A. Kitzig, G. Stockmanns, E. Naroska, R. Viga, A. Grabmaier
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引用次数: 2

Abstract

The paper considers the optimization of a Hidden-Markov Model (HMM) based method for the generation of averaged motion sequences. To create averaged motion sequences, motion sequences of different test persons were originally recorded with a motion capture system (MoCap system) and then averaged using an HMM approach. The resulting averaged data sets, however, partly showed serious motion artifacts and uncoordinated intermediate movements, especially in the extremities. The aim of this work was to combine only movements with similar courses in the extremities by a suitable cluster analysis. For each test person, model body descriptions of 21 body elements are available, each of which is represented in three-dimensional time series. For optimization, the MoCap data are first compared using time warp edit distance (TWED) and clustered using an agglomerative hierarchical procedure. Finally, the data of the resulting clusters are used to generate new averaged motion sequences using the HMM approach. The resulting averaged data can be used, for example, in a simulation in a multilevel biomechanical model.
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应用聚类分析方法优化护理床上人体生物力学运动模型
研究了基于隐马尔可夫模型(HMM)的平均运动序列生成方法的优化问题。为了创建平均运动序列,首先使用动作捕捉系统(MoCap系统)记录不同测试人员的运动序列,然后使用HMM方法进行平均。然而,结果的平均数据集部分显示严重的运动伪影和不协调的中间运动,特别是在四肢。这项工作的目的是通过适当的聚类分析,仅将运动与四肢的相似课程结合起来。对于每个测试人,可以获得21个身体元素的模型身体描述,每个身体元素都以三维时间序列的形式表示。为了优化,首先使用时间扭曲编辑距离(TWED)比较动作捕捉数据,并使用聚集分层过程进行聚类。最后,使用隐马尔可夫方法将得到的聚类数据用于生成新的平均运动序列。所得到的平均数据可用于,例如,在多层生物力学模型的模拟中。
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