Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract a person’s activities and significant places from traces of GPS data. Our system uses hierarchically structured conditional random fields to generate a consistent model of a person’s activities and places. In contrast to existing techniques, our approach takes high-level context into account in order to detect the significant places of a person. Our experiments show significant improvements over existing techniques. Furthermore, they indicate that our system is able to robustly estimate a person’s activities using a model that is trained from data collected by other persons. 1

We introduce a new dynamic model with the capability of recognizing both activities that an individual is performing as well as where that individual is located. Our approach is novel in that it utilizes a dynamic graphical model to jointly estimate both activity and spatial context over time based on the simultaneous use of asynchronous observations consisting of GPS measurements, and a small mountable sensor board. Joint inference is quite desirable as it has the ability to improve accuracy of the model and consistency of the location and activity estimates. The parameters of our model are trained on partially labeled data. We apply virtual evidence to improve data annotation, giving the user high flexibility when labeling training data. We present results indicating the performance gains achieved by virtual evidence for data annotation and the joint inference performed by our system. 1

Abstract — This paper considers the use of non-parametric system models for sequential state estimation. In particular, motion and observation models are learned from training examples using Gaussian Process (GP) regression. The state estimator is an Unscented Kalman Filter (UKF). The resulting GP-UKF algorithm has a number of advantages over standard (parametric) UKFs. These include the ability to estimate the state of arbitrary nonlinear systems, improved tracking quality compared to a parametric UKF, and graceful degradation with increased model uncertainty. These advantages stem from the fact that GPs consider both the noise in the system and the uncertainty in the model. If an approximate parametric model is available, it can be incorporated into the GP; resulting in further performance improvements. In experiments, we show how the GP-UKF algorithm can be applied to the problem of tracking an autonomous micro-blimp. I.

This paper presents a context-aware mobile recommender system, codenamed Magitti. Magitti is unique in that it infers user activity from context and patterns of user behavior and, without its user having to issue a query, automatically generates recommendations for content matching. Extensive field studies of leisure time practices in an urban setting (Tokyo) motivated the idea, shaped the details of its design and provided data describing typical behavior patterns. The paper describes the fieldwork, user interface, system components and functionality, and an evaluation of the Magitti prototype. Author Keywords Field studies, user experience design, interaction, contextaware computing, mobile recommendation systems, leisure. ACM Classification Keywords H5.m. Information interfaces and presentation (e.g., HCI):

Abstract — In this paper we propose a hierarchical dynamic Bayesian network to jointly recognize the activity and environment of a person. The hierarchical nature of the model allows us to implicitly learn data driven decompositions of complex activities into simpler sub-activities. We show by means of our experiments that the hierarchical nature of the model is able to better explain the observed data thus leading to better performance. We also show that joint estimation of both activity and environment of a person outperforms systems in which they are estimated alone. The proposed model yields about 10% absolute improvement in accuracy over existing systems. I.

We describe the activity recognition component

Obesity is now considered a global epidemic and is predicted to become the number one preventive health threat in the industrialized world. Presently, over 60 % of the U.S. adult population is overweight and 30 % is obese. This is of concern because obesity is linked to leading causes of death, such as heart and pulmonary diseases, stroke, and type 2 diabetes. The dramatic rise in obesity rates is attributed to an environment that provides easy access to high caloric food and drink and promotes low levels of physical activity. Unfortunately, many people have a poor understanding of their own daily energy (im)balance: the number of calories they consume from food compared with what they expend through physical activity. Accelerometers offer promise as an objective measure of physical activity. In prior work they have been used to estimate energy expenditure and activity type. This work further demonstrates how wireless accelerometers can be used for real-time automatic recognition of physical activity type, intensity, and duration and estimation of energy expenditure. The parameters of the algorithms such as type of classifier/regressor, feature set, window length, signal preprocessing, sensor set utilized