Plan recognition, activity recognition, and intent recognition together combine and unify techniques from user modeling, machine vision, intelligent user interfaces, human/computer interaction, autonomous and multi-agent systems, natural language understanding, and machine learning. Plan, Activity, and Intent Recognition explains the crucial role of these techniques in a wide variety of applications including: personal agent assistants, computer and network security, opponent modeling in games and simulation systems, coordination in robots and software agents, web e-commerce and collaborative filtering, dialog modeling, video surveillance, smart homes In this book, follow the history of this research area and witness exciting new developments in the field made possible by improved sensors, increased computational power, and new application areas. Combines basic theory on algorithms for plan/activity recognition along with results from recent workshops and seminars Explains how to interpret and recognize plans and activities from sensor data Provides valuable background knowledge and assembles key concepts into one guide for researchers or students studying these disciplines

Plan, Activity, and Intent Recognition: Theory and Practice

Devices and methods are provided for determining the proximity of client nodes within a wireless-enabled communication environment. A first client node comprises a database containing identification data associated with a plurality of wireless network access nodes and fingerprint data associated with the client node and a plurality of second client nodes. As the fingerprint information is updated, it is processed by the first client node to identify second client nodes that have matching, or similar, fingerprint information. Those that do are determined to be within a viable device-to-device (D2D) communication range.

Determining device in-range proximity

Public transport smartcard data can be used for detection of large crowds By comparing statistics on habitual behavior (eg, average by time of day), one can specifically identify nonhabitual crowds, which are often very problematic for transport systems While habitual overcrowding (eg, peak hour) is well understood both by traffic managers and travelers, nonhabitual overcrowding hotspots can become even more disruptive and unpleasant because they are generally unexpected By quickly understanding such cases, a transport manager can react and mitigate transport system disruptions We propose a probabilistic data analysis model that breaks each nonhabitual overcrowding hotspot into a set of explanatory components The potential explanatory components are initially retrieved from social networks and special events websites and then processed through text-analysis techniques Finally, for each such component, the probabilistic model estimates a specific share in the total overcrowding counts We first validate with synthetic data and then test our model with real data from the public transport system (EZLink) of Singapore, focused on three case study areas We demonstrate that it is able to generate explanations that are intuitively plausible and consistent both locally (correlation coefficient, ie, CC, from 85% to 99% for the three areas) and globally (CC from 412% to 839%) This model is directly applicable to any other domain sensitive to crowd formation due to large social events (eg, communications, water, energy, waste)

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Why so many people ? Explaining Nonhabitual Transport Overcrowding With Internet Data

The vast availability of mobile phones with built-in movement and location sensors enable the collection of detailed information about human movement even indoors. As mobility is a key element of many processes and activities, an interesting class of information to extract is movement patterns that quantify how humans move, interact and group. In this paper we propose methods for detecting two common pedestrian movement patterns, namely individual following relations and group leadership. The proposed methods for identifying following patterns employ machine learning on features derived using similarity analysis on time lagged sequences of WiFi measurements containing either raw signal strength values or derived locations. To detect leadership we combine the individual following relations into directed graphs and detect leadership within groups by graph link analysis. Methods for detecting these movement patterns open up new possibilities in - amongst others - computational social science, reality mining, marketing research and location-based gaming. We provide evaluation results that show error rates down to 7%, improving over state of the art methods with up to eleven percentage points for following patterns and up to twenty percentage points for leadership patterns. Our method is, contrary to state of the art, also applicable in challenging indoor environments, e.g., multi-story buildings. This implies that even quite small samples allow us to detect information such as how events and campaigns in multistory shopping malls may trigger following in small groups, or which group members typically take the lead when triggered by e.g. commercials, or how rescue or police forces act during training exercises.

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Time-lag method for detecting following and leadership behavior of pedestrians from mobile sensing data

A fundamental task in pervasive computing is reliable acquisition of contexts from sensor data. This is crucial to the operation of smart pervasive systems and services so that they might behave efficiently and appropriately upon a given context. Simple forms of context can often be extracted directly from raw data. Equally important, or more, is the hidden context and pattern buried inside the data, which is more challenging to discover. Most of existing approaches borrow methods and techniques from machine learning, dominantly employ parametric unsupervised learning and clustering techniques. Being parametric, a severe drawback of these methods is the requirement to specify the number of latent patterns in advance. In this paper, we explore the use of Bayesian nonparametric methods, a recent data modelling framework in machine learning, to infer latent patterns from sensor data acquired in a pervasive setting. Under this formalism, nonparametric prior distributions are used for data generative process, and thus, they allow the number of latent patterns to be learned automatically and grow with the data - as more data comes in, the model complexity can grow to explain new and unseen patterns. In particular, we make use of the hierarchical Dirichlet processes (HDP) to infer atomic activities and interaction patterns from honest signals collected from sociometric badges. We show how data from these sensors can be represented and learned with HDP. We illustrate insights into atomic patterns learned by the model and use them to achieve high-performance clustering. We also demonstrate the framework on the popular Reality Mining dataset, illustrating the ability of the model to automatically infer typical social groups in this dataset. Finally, our framework is generic and applicable to a much wider range of problems in pervasive computing where one needs to infer high-level, latent patterns and contexts from sensor data.

Extraction of latent patterns and contexts from social honest signals using hierarchical Dirichlet processes

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 a state-of-the-art probabilistic clustering technique, the Latent Dirichlet 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.

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High accuracy context recovery using clustering mechanisms

This paper presents a framework for indoor location prediction system using multiple wireless signals available freely in public or office spaces. We first propose an abstract architectural design for the system, outlining its key components and their functionalities. Different from existing works, such as robot indoor localization which requires as precise localization as possible, our work focuses on a higher grain: location prediction. Such a problem has a great implication in context-aware systems such as indoor navigation or smart self-managed mobile devices (e.g., battery management). Central to these systems is an effective method to perform location prediction under different constraints such as dealing with multiple wireless sources, effects of human body heats or mobility of the users. To this end, the second part of this paper presents a comparative and comprehensive study on different choices for modeling signals strengths and prediction methods under different condition settings. The results show that with simple, but effective modeling method, almost perfect prediction accuracy can be achieved in the static environment, and up to 85% in the presence of human movements. Finally, adopting the proposed framework we outline a fully developed system, named Marauder, that support user interface interaction and real-time voice-enabled location prediction.

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Kha Tran

Papers

High accuracy context recovery using clustering mechanisms

Indoor location prediction using multiple wireless received signal strengths

Improving Traffic Load Prediction with Multi-modality - A Case Study of Brisbane