4,650 research outputs found
Discovering activity patterns in office environment using a network of low-resolution visual sensors
Understanding activity patterns in office environments is important in order to increase workersâ comfort and productivity. This paper proposes an automated system for discovering activity patterns of multiple persons in a work environment using a network of cheap low-resolution visual sensors (900 pixels). Firstly, the usersâ locations are obtained from a robust people tracker based on recursive maximum likelihood principles. Secondly, based on the usersâ mobility tracks, the high density positions are found using a bivariate kernel density estimation. Then, the hotspots are detected using a confidence region estimation. Thirdly, we analyze the individualâs tracks to find the starting and ending hotspots. The starting and ending hotspots form an observation sequence, where the userâs presence and absence are detected using three powerful Probabilistic Graphical Models (PGMs). We describe two approaches to identify the userâs status: a single model approach and a two-model mining approach. We evaluate both approaches on video sequences captured in a real work environment, where the personsâ daily routines are recorded over 5 months. We show how the second approach achieves a better performance than the first approach. Routines dominating the entire groupâs activities are identified with a methodology based on the Latent Dirichlet Allocation topic model. We also detect routines which are characteristic of persons. More specifically, we perform various analysis to determine regions with high variations, which may correspond to specific events
High accuracy context recovery using clustering mechanisms
This paper examines the recovery of user context in indoor environmnents with existing wireless infrastructures to enable assistive systems. We present a novel approach to the extraction of user context, casting the problem of context recovery as an unsupervised, clustering problem. A well known density-based clustering technique, DBSCAN, is adapted to recover user context that includes user motion state, and significant places the user visits from WiFi observations consisting of access point id and signal strength. Furthermore, user rhythms or sequences of places the user visits periodically are derived from the above low level contexts by employing state-of-the-art probabilistic clustering technique, the Latent Dirichiet Allocation (LDA), to enable a variety of application services. Experimental results with real data are presented to validate the proposed unsupervised learning approach and demonstrate its applicability.<br /
Routine pattern discovery and anomaly detection in individual travel behavior
Discovering patterns and detecting anomalies in individual travel behavior is
a crucial problem in both research and practice. In this paper, we address this
problem by building a probabilistic framework to model individual
spatiotemporal travel behavior data (e.g., trip records and trajectory data).
We develop a two-dimensional latent Dirichlet allocation (LDA) model to
characterize the generative mechanism of spatiotemporal trip records of each
traveler. This model introduces two separate factor matrices for the spatial
dimension and the temporal dimension, respectively, and use a two-dimensional
core structure at the individual level to effectively model the joint
interactions and complex dependencies. This model can efficiently summarize
travel behavior patterns on both spatial and temporal dimensions from very
sparse trip sequences in an unsupervised way. In this way, complex travel
behavior can be modeled as a mixture of representative and interpretable
spatiotemporal patterns. By applying the trained model on future/unseen
spatiotemporal records of a traveler, we can detect her behavior anomalies by
scoring those observations using perplexity. We demonstrate the effectiveness
of the proposed modeling framework on a real-world license plate recognition
(LPR) data set. The results confirm the advantage of statistical learning
methods in modeling sparse individual travel behavior data. This type of
pattern discovery and anomaly detection applications can provide useful
insights for traffic monitoring, law enforcement, and individual travel
behavior profiling
Recognition of activities of daily living from topic model
Research in ubiquitous and pervasive technologies have made it possible to recognise activities of daily living through non-intrusive sensors. The data captured from these sensors are required to be classified using various machine learning or knowledge driven techniques to infer and recognise activities. The process of discovering the activities and activity-object patterns from the sensors tagged to objects as they are used is critical to recognising the activities. In this paper, we propose a topic model process of discovering activities and activity-object patterns from the interactions of low level state-change sensors. We also develop a recognition and segmentation algorithm to recognise activities and recognise activity boundaries. Experimental results we present validates our framework and shows it is comparable to existing approaches
What Did You Do Today? Discovering Daily Routines from Large-Scale Mobile Data
We present a framework built from two Hierarchical Bayesian topic models to discover human location-driven routines from mobile phones. The framework uses location-driven bag representations of people's daily activities obtained from celltower connections. Using 68 000+ hours of real-life human data from the Reality Mining dataset, we successfully discover various types of routines. The first studied model, Latent Dirichlet Allocation (LDA), automatically discovers characteristic routines for all individuals in the study, including ``going to work at 10am", ``leaving work at night", or ``staying home for the entire evening". In contrast, the second methodology with the Author Topic model (ATM) finds routines characteristic of a selected groups of users, such as ``being at home in the mornings and evenings while being out in the afternoon", and ranks users by their probability of conforming to certain daily routines
Pervasive sensing to model political opinions in face-to-face networks
Exposure and adoption of opinions in social networks are
important questions in education, business, and government. We de-
scribe a novel application of pervasive computing based on using mobile
phone sensors to measure and model the face-to-face interactions and
subsequent opinion changes amongst undergraduates, during the 2008
US presidential election campaign. We nd that self-reported political
discussants have characteristic interaction patterns and can be predicted
from sensor data. Mobile features can be used to estimate unique individ-
ual exposure to di erent opinions, and help discover surprising patterns
of dynamic homophily related to external political events, such as elec-
tion debates and election day. To our knowledge, this is the rst time
such dynamic homophily e ects have been measured. Automatically esti-
mated exposure explains individual opinions on election day. Finally, we
report statistically signi cant di erences in the daily activities of individ-
uals that change political opinions versus those that do not, by modeling
and discovering dominant activities using topic models. We nd people
who decrease their interest in politics are routinely exposed (face-to-face)
to friends with little or no interest in politics.U.S. Army Research Laboratory (Cooperative Agreement No. W911NF-09-2-0053)United States. Air Force Office of Scientific Research (Award No. FA9550-10-1-0122)Swiss National Science Foundatio
Patterns, Entropy, and Predictability of Human Mobility and Life
Cellular phones are now offering an ubiquitous means for scientists to observe life: how people act, move and respond to external influences. They can be utilized as measurement devices of individual persons and for groups of people of the social context and the related interactions. The picture of human life that emerges shows complexity, which is manifested in such data in properties of the spatiotemporal tracks of individuals. We extract from smartphone-based data for a set of persons important locations such as âhomeâ, âworkâ and so forth over fixed length time-slots covering the days in the data-set (see also [1], [2]). This set of typical places is heavy-tailed, a power-law distribution with an exponent close to â1.7. To analyze the regularities and stochastic features present, the days are classified for each person into regular, personal patterns. To this are superimposed fluctuations for each day. This randomness is measured by âlifeâ entropy, computed both before and after finding the clustering so as to subtract the contribution of a number of patterns. The main issue that we then address is how predictable individuals are in their mobility. The patterns and entropy are reflected in the predictability of the mobility of the life both individually and on average. We explore the simple approaches to guess the location from the typical behavior, and of exploiting the transition probabilities with time from location or activity A to B. The patterns allow an enhanced predictability, at least up to a few hours into the future from the current location. Such fixed habits are most clearly visible in the working-day length.Peer reviewe
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