Starts at: April 28, 2014 2:00 PM
Ends at: 5:00 PM
Location: SV Campus, Bldg. 23, Rm. 211
Recent advances in sensor technologies and the growing interest in context- aware applications, such as targeted advertising and location-based services have led to a demand for understanding human behavior patterns from sensor data. People engage in routine behaviors. Automatic routine discovery goes beyond low-level activity recognition such as sitting or standing and analyzes human behaviors at a higher level (e.g., commuting to work). The goal of this research is to automatically discover high-level semantic human routines from low-level sensor streams. One new line of research is to mine human routines from sensor data using parametric topic models. The main shortcoming of parametric models is that it assumes a fixed, pre-specified parameter regardless of the data. Choosing an appropriate parameter usually requires an inefficient trial-and-error model selection process. Furthermore, it is even more difficult to find optimal parameter values in advance for personalized applications.
This research presents a novel nonparametric framework for human routine discovery that can infer high-level routines without knowing the number of latent topics beforehand. More specifically, the framework automatically finds the size of the low-level activity vocabulary from sensor feature vectors at the vocabulary extraction phase. At the routine discovery phase, the framework further automatically selects the appropriate number of latent topics and discovers latent routines. Moreover, we propose a new generative graphical model to incorporate multimodal sensor streams for the human activity discovery task. Evaluations are performed on public datasets in two routine domains: two daily-activity datasets and a transportation mode dataset. Experimental results show that our nonparametric framework can automatically learn the appropriate model parameters from multimodal sensor data without any form of manual model selection procedure and can outperform traditional parametric approaches for human routine discovery tasks.