As part of daily monitoring of human activities, wearable sensors and devices are becoming increasingly popular sources of data. With the advent of smartphones equipped with acceloremeter, gyroscope and camera; it is now possible to develop activity classification platforms everyone can use conveniently. In this paper, we propose a fast inference method for an unsupervised non-parametric time series model namely variational inference for sticky HDP-SLDS(Hierarchical Dirichlet Process Switching Linear Dynamical System). We show that the proposed algorithm can differentiate various indoor activities such as sitting, walking, turning, going up/down the stairs and taking the elevator using only the acceloremeter of an Android smartphone Samsung Galaxy S4. We used the front camera of the smartphone to annotate activity types precisely. We compared the proposed method with Hidden Markov Models with Gaussian emission probabilities on a dataset of 10 subjects. We showed that the efficacy of the stickiness property. We further compared the variational inference to the Gibbs sampler on the same model and show that variational inference is faster in one order of magnitude.

翻译：作为日常监测人类活动的一部分,可磨损的传感器和装置正在日益成为受欢迎的数据来源。随着配备了立方厘米、陀螺仪和相机的智能手机的出现,现在有可能开发活动分类平台,每个人都可以方便地使用。在本文件中,我们建议对未经监督的非参数时间序列模型,即粘性 HDP-SLDS(高科技二重处理程序切换线性动态系统)的变位推推法,快速推导方法。我们表明,拟议的算法可以区分各种室内活动,如坐坐、走路、转动、上下楼梯和电梯,仅使用机械智能手机Samsung Galaxy S4的弧度计。我们使用智能手机的前摄像头照相机来精确说明活动类型。我们比较了隐性马尔科夫模型的拟议方法与10个主题数据集的高斯排放概率。我们显示,粘性特性的效力。我们进一步比较了与同一模型的吉布斯采样器的变位顺序,显示变速速度。