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CIGAR: Concurrent and Interleaving Goal and Activity Recognition
"... In artificial intelligence and pervasive computing research, inferring users ’ high-level goals from activity sequences is an important task. A major challenge in goal recognition is that users often pursue several high-level goals in a concurrent and interleaving manner, where the pursuit of goals ..."
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Cited by 30 (5 self)
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In artificial intelligence and pervasive computing research, inferring users ’ high-level goals from activity sequences is an important task. A major challenge in goal recognition is that users often pursue several high-level goals in a concurrent and interleaving manner, where the pursuit of goals may spread over different parts of an activity sequence and may be pursued in parallel. Existing approaches to recognizing multiple goals often formulate this problem either as a single-goal recognition problem or in a deterministic way, ignoring uncertainty. In this paper, we propose CIGAR (Concurrent and Interleaving Goal and Activity Recognition)- a novel and simple two-level probabilistic framework for multiple-goal recognition where we can recognize both concurrent and interleaving goals. We use skip-chain conditional random fields (SCCRF) for modeling interleaving goals and we model concurrent goals by adjusting inferred probabilities through a correlation graph, which is a major advantage in that we are able to reason about goal interactions explicitly through the correlation graph. The two-level framework also avoids the high training complexity when modeling concurrency and interleaving together in a unified CRF model. Experimental results show that our method can effectively improve recognition accuracies on several real-world datasets collected from various wireless and sensor networks.
Cross-Domain Activity Recognition
"... In activity recognition, one major challenge is huge manual effort in labeling when a new domain of activities is to be tested. In this paper, we ask an interesting question: can we transfer the available labeled data from a set of existing activities in one domain to help recognize the activities i ..."
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Cited by 21 (4 self)
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In activity recognition, one major challenge is huge manual effort in labeling when a new domain of activities is to be tested. In this paper, we ask an interesting question: can we transfer the available labeled data from a set of existing activities in one domain to help recognize the activities in another different but related domain? Our answer is “yes”, provided that the sensor data from the two domains are related in some way. We develop a bridge between the activities in two domains by learning a similarity function via Web search, under the condition that the sensor data are from the same feature space. Based on the learned similarity measures, our algorithm interprets the data from the source domain as the data in the domain with different confidence levels, thus accomplishing the cross-domain knowledge transfer task. Our algorithm is evaluated on several real-world datasets to demonstrate its effectiveness.
Real World Activity Recognition with Multiple Goals
"... Recognizing and understanding the activities of people from sensor readings is an important task in ubiquitous computing. Activity recognition is also a particularly difficult task because of the inherent uncertainty and complexity of the data collected by the sensors. Many researchers have tackled ..."
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Cited by 19 (3 self)
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Recognizing and understanding the activities of people from sensor readings is an important task in ubiquitous computing. Activity recognition is also a particularly difficult task because of the inherent uncertainty and complexity of the data collected by the sensors. Many researchers have tackled this problem in an overly simplistic setting by assuming that users often carry out single activities one at a time or multiple activities consecutively, one after another. However, so far there has been no formal exploration on the degree in which humans perform concurrent or interleaving activities, and no thorough study on how to detect multiple goals in a real world scenario. In this article, we ask the fundamental questions of whether users often carry out multiple concurrent and interleaving activities or single activities in their daily life, and if so, whether such complex behavior can be detected accurately using sensors. We define several classes of complexity levels under a goal taxonomy that describe different granularities of activities, and relate the recognition accuracy with different complexity levels or granularities. We present a theoretical framework for recognizing multiple concurrent and interleaving activities, and evaluate the framework in several real-world ubiquitous computing environments.
Learning Tree Conditional Random Fields
"... We examine maximum spanning tree-based methods for learning the structure of tree Conditional Random Fields (CRFs) P (Y|X). We use edge weights which take advantage of local inputs X and thus scale to large problems. For a general class of edge weights, we give a negative learnability result. Howeve ..."
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Cited by 19 (1 self)
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We examine maximum spanning tree-based methods for learning the structure of tree Conditional Random Fields (CRFs) P (Y|X). We use edge weights which take advantage of local inputs X and thus scale to large problems. For a general class of edge weights, we give a negative learnability result. However, we demonstrate that two members of the class–local Conditional Mutual Information and Decomposable Conditional Influence– have reasonable theoretical bases and perform very well in practice. On synthetic data and a large-scale fMRI application, our methods outperform existing techniques. 1.
Mining Recent Temporal Patterns for Event Detection in Multivariate Time Data
- IN PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (SIGKDD
, 2012
"... Improving the performance of classifiers using pattern mining techniques has been an active topic of data mining research. In this work we introduce the recent temporal pattern mining framework for finding predictive patterns for monitoring and event detection problems in complex multivariate time s ..."
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Cited by 15 (4 self)
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Improving the performance of classifiers using pattern mining techniques has been an active topic of data mining research. In this work we introduce the recent temporal pattern mining framework for finding predictive patterns for monitoring and event detection problems in complex multivariate time series data. This framework first converts time series into time-interval sequences of temporal abstractions. It then constructs more complex temporal patterns backwards in time using temporal operators. We apply our frame-work to health care data of 13,558 diabetic patients and show its benefits by efficiently finding useful patterns for detecting and diagnosing adverse medical conditions that are associated with diabetes.
Exploring techniques for vision based human activity recognition: Methods, systems, and evaluation
- Sensors 2013
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Location-based reasoning about complex multiagent behavior
- In Journal of Artificial Intelligence Research. AI Access Foundation
, 2011
"... Recent research has shown that surprisingly rich models of human activity can be learned from GPS (positional) data. However, most effort to date has concentrated on modeling single individuals or statistical properties of groups of people. Moreover, prior work focused solely on modeling actual succ ..."
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Cited by 11 (3 self)
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Recent research has shown that surprisingly rich models of human activity can be learned from GPS (positional) data. However, most effort to date has concentrated on modeling single individuals or statistical properties of groups of people. Moreover, prior work focused solely on modeling actual successful executions (and not failed or attempted executions) of the activities of interest. We, in contrast, take on the task of understanding human interactions, attempted interactions, and intentions from noisy sensor data in a fully relational multi-agent setting. We use a real-world game of capture the flag to illustrate our approach in a well-defined domain that involves many distinct cooperative and competitive joint activities. We model the domain using Markov logic, a statistical-relational language, and learn a theory that jointly denoises the data and infers occurrences of high-level activities, such as a player capturing an enemy. Our unified model combines constraints imposed by the geometry of the game area, the motion model of the players, and by the rules and dynamics of the game in a probabilistically and logically sound fashion. We show that while it may be impossible to directly detect a multi-agent activity due to sensor noise or malfunction, the occurrence of the activity can still be inferred by considering both its impact on the
Event Processing Under Uncertainty
"... Big data is recognized as one of the three technology trends at the leading edge a CEO cannot afford to overlook in 2012. Big data is characterized by volume, velocity, variety and veracity (“data in doubt”). As big data applications, many of the emerging event processing applications must process e ..."
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Cited by 7 (1 self)
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Big data is recognized as one of the three technology trends at the leading edge a CEO cannot afford to overlook in 2012. Big data is characterized by volume, velocity, variety and veracity (“data in doubt”). As big data applications, many of the emerging event processing applications must process events that arrive from sources such as sensors and social media, which have inherent uncertainties associated with them. Consider, for example, the possibility of incomplete data streams and streams including inaccurate data. In this tutorial we classify the different types of uncertainty found in event processing applications and discuss the implications on event representation and reasoning. An area of research in which uncertainty has been studied is Artificial Intelligence. We discuss, therefore, the main Artificial Intelligence-based event processing systems that support probabilistic reasoning. The presented approaches are illustrated using an example concerning crime detection.
Conditional Random Fields for Activity Recognition in Smart Environments
- Proceedings of IHI
, 2010
"... One of the most common functions of smart environments is to monitor and assist older adults with their activities of daily living. Activity recognition is a key component in this application. It is essentially a temporal classification problem which has been modeled in the past by naïve Bayes class ..."
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Cited by 7 (1 self)
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One of the most common functions of smart environments is to monitor and assist older adults with their activities of daily living. Activity recognition is a key component in this application. It is essentially a temporal classification problem which has been modeled in the past by naïve Bayes classifiers and hidden Markov models (HMMs). In this paper, we describe the use of another probabilistic model: Conditional Random Fields (CRFs), which is currently gaining popularity for its remarkable performance for activity recognition. Our focus is on using CRFs to recognize activities performed by an inhabitant in a smart home environment and our goal is to validate the claim of its higher or similar performance by comparing CRFs with HMMs using data collected in a real smart home.
