Jian Lu

A Pattern Mining Approach to Sensor-Based Human Activity Recognition

Recognizing human activities from sensor readings has recently attracted much research interest in pervasive computing due to its potential in many applications, such as assistive living and healthcare. This task is particularly challenging because human activities are often performed in not only a simple (i.e., sequential), but also a complex (i.e., interleaved or concurrent) manner in real life. Little work has been done in addressing complex issues in such a situation. The existing models of interleaved and concurrent activities are typically learning-based. Such models lack of flexibility in real life because activities can be interleaved and performed concurrently in many different ways. In this paper, we propose a novel pattern mining approach to recognize sequential, interleaved, and concurrent activities in a unified framework. We exploit Emerging Pattern-a discriminative pattern that describes significant changes between classes of data-to identify sensor features for classifying activities. Different from existing learning-based approaches which require different training data sets for building activity models, our activity models are built upon the sequential activity trace only and can be applied to recognize both simple and complex activities. We conduct our empirical studies by collecting real-world traces, evaluating the performance of our algorithm, and comparing our algorithm with static and temporal models. Our results demonstrate that, with a time slice of 15 seconds, we achieve an accuracy of 90.96 percent for sequential activity, 88.1 percent for interleaved activity, and 82.53 percent for concurrent activity.

Managing Quality of Context in Pervasive Computing

Context-awareness plays a key role in a paradigm shift from traditional desktop styled computing to emerging pervasive computing. Many context-aware systems have been built to achieve the vision of pervasive computing and alleviate the human attention bottleneck; however, these systems are far from real world applications. Quality of context is critical in reducing the gap between existing systems and real-life applications. Aiming to provide the support of quality of context, in this paper, we propose a novel quality model for context information and a context management mechanism for inconsistency resolution. We also build a prototype system to validate our proposed model and mechanism, and to assist the development of context-aware applications. Through our evaluations and case study, context-aware applications can be built with the support of quality of context

Recognizing multi-user activities using wearable sensors in a smart home

The advances of wearable sensors and wireless networks offer many opportunities to recognize human activities from sensor readings in pervasive computing. Existing work so far focuses mainly on recognizing activities of a single user in a home environment. However, there are typically multiple inhabitants in a real home and they often perform activities together. In this paper, we investigate the problem of recognizing multi-user activities using wearable sensors in a home setting. We develop a multi-modal, wearable sensor platform to collect sensor data for multiple users, and study two temporal probabilistic models-Coupled Hidden Markov Model (CHMM) and Factorial Conditional Random Field (FCRF)-to model interacting processes in a sensor-based, multi-user scenario. We conduct a real-world trace collection done by two subjects over two weeks, and evaluate these two models through our experimental studies. Our experimental results show that we achieve an accuracy of 96.41% with CHMM and an accuracy of 87.93% with FCRF, respectively, for recognizing multi-user activities.

A hierarchical approach to real-time activity recognition in body sensor networks

Real-time activity recognition in body sensor networks is an important and challenging task. In this paper, we propose a real-time, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network. In this model, we first use a fast and lightweight algorithm to detect gestures at the sensor node level, and then propose a pattern based real-time algorithm to recognize complex, high-level activities at the portable device level. We evaluate our algorithms over a real-world dataset. The results show that the proposed system not only achieves good performance (an average utility of 0.81, an average accuracy of 82.87%, and an average real-time delay of 5.7 seconds), but also significantly reduces the networks communication cost by 60.2%.

Sensor-Based Human Activity Recognition in a Multi-user Scenario

Existing work on sensor-based activity recognition focuses mainly on single-user activities. However, in real life, activities are often performed by multiple users involving interactions between them. In this paper, we propose Coupled Hidden Markov Models (CHMMs) to recognize multi-user activities from sensor readings in a smart home environment. We develop a multimodal sensing platform and present a theoretical framework to recognize both single-user and multi-user activities. We conduct our trace collection done in a smart home, and evaluate our framework through experimental studies. Our experimental result shows that we achieve an average accuracy of 85.46% with CHMMs.

FollowMe: on research of pluggable infrastructure for context-awareness

Pervasive computing is to enhance the environment by embedding many computers that are gracefully integrated with human users. To achieve this, the key research thrust is to create a smart context-awareness environment which should enclose various users and satisfy different needs of the users. Building such smart environments is still difficult and complex due to lacking a uniform infrastructure that can adapt to diverse smart domains. To address this problem, we propose a context-aware computing infrastructure, called FollowMe. Our infrastructure integrates an ontology based context model and a workflow based application model with the OSGi framework. By plugging different domain contexts and applications, FollowMe can be customized to various domains.

Mining Emerging Patterns for recognizing activities of multiple users in pervasive computing

Understanding and recognizing human activities from sensor readings is an important task in pervasive computing. Existing work on activity recognition mainly focuses on recognizing activities for a single user in a smart home environment. However, in real life, there are often multiple inhabitants live in such an environment. Recognizing activities of not only a single user, but also multiple users is essential to the development of practical context-aware applications in pervasive computing. In this paper, we investigate the fundamental problem of recognizing activities for multiple users from sensor readings in a home environment, and propose a novel pattern mining approach to recognize both single-user and multi-user activities in a unified solution. We exploit Emerging Pattern —a type of knowledge pattern that describes significant changes between classes of data — for constructing our activity models, and propose an Emerging Pattern based Multi-user Activity Recognizer (epMAR) to recognize both single-user and multiuser activities. We conduct our empirical studies by collecting real-world activity traces done by two volunteers over a period of two weeks in a smart home environment, and analyze the performance in detail with respect to various activity cases in a multi-user scenario. Our experimental results demonstrate that our epMAR recognizer achieves an average accuracy of 89.72% for all the activity cases.

Mobile agent enabled application mobility for pervasive computing

Applications that can follow mobile users when they change to a different environment are in high demand by pervasive computing. In this paper, we describe a mobile agent based paradigm for enabling an application to migrate with the user in pervasive computing environments. Compared with existing efforts on application mobility, our approach has the following distinctive features: (1) Applications are supported by a middleware with a reflective architecture that helps separate business functions from context-awareness logic; (2) Mobile agent is used to manage the mobility of an application and help the application adapt to its new context; (3) The advantages of mobile agent, such as reactivity, autonomy and intelligence, are naturally incorporated into the pervasive computing environment. Our experience shows that mobile agent is a promising technology for pervasive and mobile computing where mobile agents can act as a bridge connecting the cyber world with the physical world.

Context consistency management using ontology based model

Inconsistent contexts are death-wounds which usually result in context-aware applications incongruous behaviors and users perplexed feelings, therefore the benefits of context-aware computing will become less believed. This problem occurs in most sensor based applications due to the intrinsic drawbacks of fallible physical sensors which can only detect some evidence of real worlds situations rather than global views of them. In this paper, we extend ontology based context modeling approach with some descriptive information added to contexts, modify reasoners to support time information, bring in a context lifecycle management strategy, establish a context exploitation mechanism, and propose an inconsistency resolution algorithm, fostering timely, exact and conflict-free contexts. Besides, evaluations and a case study are carried out to attest our design principles.

A Middleware Support for Agent-Based Application Mobility in Pervasive Environments

Application mobility is an efficient way to mask uneven conditioning and reduce users distractions in pervasive environments. However, since mobility brings more dynamism and uncertainty, it also raises new research issues in developing pervasive applications, including underlying application models, adaptive resource rebinding mechanisms, synchronization and fault tolerance techniques, etc. In this paper, we approach these problems from the middleware perspective. Inspired by software agents inherent capability of autonomy and mobility, we investigate its potential use in application mobility and propose an agent-based architecture called MDAgent. Three salient features are emphasized: 1) Reduced mobility overhead. Flexible bindings of application components avoid migrating whole application. 2) Simplified mobility management. Mobile agent takes over the responsibility of mobility and synchronization, so user intervention is reduced. 3) Enhanced customizability and adaptability. Context information can be updated dynamically, and ontology-based reasoning ability embedded in autonomous agents can direct the application to adapt to the changes accordingly. On top of MDAgent, we have developed several applications, and evaluated the performance.