Nakamura Tomoaki, Nagai Takayuki, Mochihashi Daichi, Kobayashi Ichiro, Asoh Hideki, Kaneko Masahide
Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, Chofu-shi, Japan.
Department of Mathematical Analysis and Statistical Inference, Institute of Statistical Mathematics, Tachikawa, Japan.
Front Neurorobot. 2017 Dec 21;11:67. doi: 10.3389/fnbot.2017.00067. eCollection 2017.
Humans divide perceived continuous information into segments to facilitate recognition. For example, humans can segment speech waves into recognizable morphemes. Analogously, continuous motions are segmented into recognizable unit actions. People can divide continuous information into segments without using explicit segment points. This capacity for unsupervised segmentation is also useful for robots, because it enables them to flexibly learn languages, gestures, and actions. In this paper, we propose a Gaussian process-hidden semi-Markov model (GP-HSMM) that can divide continuous time series data into segments in an unsupervised manner. Our proposed method consists of a generative model based on the hidden semi-Markov model (HSMM), the emission distributions of which are Gaussian processes (GPs). Continuous time series data is generated by connecting segments generated by the GP. Segmentation can be achieved by using forward filtering-backward sampling to estimate the model's parameters, including the lengths and classes of the segments. In an experiment using the CMU motion capture dataset, we tested GP-HSMM with motion capture data containing simple exercise motions; the results of this experiment showed that the proposed GP-HSMM was comparable with other methods. We also conducted an experiment using karate motion capture data, which is more complex than exercise motion capture data; in this experiment, the segmentation accuracy of GP-HSMM was 0.92, which outperformed other methods.
人类将感知到的连续信息分割成片段以方便识别。例如，人类可以将语音波分割成可识别的语素。类似地，连续动作被分割成可识别的单元动作。人们可以在不使用明确分割点的情况下将连续信息分割成片段。这种无监督分割能力对机器人也很有用，因为它使机器人能够灵活地学习语言、手势和动作。在本文中，我们提出了一种高斯过程-隐半马尔可夫模型（GP-HSMM），它可以以无监督的方式将连续时间序列数据分割成片段。我们提出的方法由一个基于隐半马尔可夫模型（HSMM）的生成模型组成，其发射分布是高斯过程（GPs）。连续时间序列数据是通过连接由高斯过程生成的片段而生成的。分割可以通过使用前向滤波-后向采样来估计模型参数来实现，这些参数包括片段的长度和类别。在使用卡内基梅隆大学运动捕捉数据集的实验中，我们用包含简单锻炼动作的运动捕捉数据测试了GP-HSMM；该实验结果表明，所提出的GP-HSMM与其他方法相当。我们还使用空手道运动捕捉数据进行了实验，该数据比锻炼运动捕捉数据更复杂；在这个实验中，GP-HSMM的分割准确率为0.92，优于其他方法。
