1 research outputs found
Accelerometer based Activity Classification with Variational Inference on Sticky HDP-SLDS
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