Shaxun Chen

An unsupervised approach to activity recognition and segmentation based on object-use fingerprints

Human activity recognition is an important task which has many potential applications. In recent years, researchers from pervasive computing are interested in deploying on-body sensors to collect observations and applying machine learning techniques to model and recognize activities. Supervised machine learning techniques typically require an appropriate training process in which training data need to be labeled manually. In this paper, we propose an unsupervised approach based on object-use fingerprints to recognize activities without human labeling. We show how to build our activity models based on object-use fingerprints, which are sets of contrast patterns describing significant differences of object use between any two activity classes. We then propose a fingerprint-based algorithm to recognize activities. We also propose two heuristic algorithms based on object relevance to segment a trace and detect the boundary of any two adjacent activities. We develop a wearable RFID system and conduct a real-world trace collection done by seven volunteers in a smart home over a period of 2 weeks. We conduct comprehensive experimental evaluations and comparison study. The results show that our recognition algorithm achieves a precision of 91.4% and a recall 92.8%, and the segmentation algorithm achieves an accuracy of 93.1% on the dataset we collected.

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

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.

Toward context-awareness: a workflow embedded middleware

Context-aware computing is widely researched in recent years, but we lack for a powerful context-aware middleware which supports a uniform programming model. So developing context-aware applications is still complex and time-consuming. We introduce a workflow embedded middleware called FollowMe. It supports pluggable context-aware applications. FollowMe includes a workflow engine and sustains applications described with pvPDL, which is a workflow definition language proposed specially for context-awareness. The employment of workflow makes the development of applications simplified and the maintenance much easier. We testify the improvement by realizing an example and the related evaluation.

An enhanced ontology based context model and fusion mechanism

With diverse sensors, context-aware applications which aim at decreasing people’s attentions to computational devices are becoming more and more popular. But it is inadequate just with sensors because what applications really need is high-level context knowledge rather than low-level raw sensor data. So a software layer which delivers high quality contexts to applications with an easy application programming model is needed. In this paper, we establish a formal context model using semantic web languages and design a context fusion mechanism which not only generates high-level contexts by reasoning, but also brings in context lifecycle management and periodically time based conflict resolution to improve the quality of contexts. Using the context fusion mechanism, a programmable and service oriented middleware is built upon OSGi framework to support context-aware applications. Also, we propose an application programming model using RDQL as context query language and demonstrate an application called Seminar Assistant. According to the experiment results, we believe our prototype system is useful for non-real-time applications in various domains.

A transaction model for context-aware applications

Pervasive computing is widely researched and a large number of context-aware applications have been built in the recent years. However, correctness of contexts and fault handling of these applications have always been ignored. This paper proposes a transaction model for context-aware applications. In this model, context-aware applications are organized as a number of logic units and each unit may have a compensation module, which will be executed when errors or exceptions occur in context-aware applications in order to minimize the bad infection. This model supports nested scopes and the number of levels of subtransactions is unlimited. We also present an implementation of this transaction model, which is specialized for context-aware use.

Toward an agent‐based pluggable infrastructure for context‐awareness

Purpose – Pervasive computing enhances the environment by embedding many computers that are gracefully integrated with human users. The purpose of this paper is to describe the creation of a smart context‐aware environment in which computation follows people and serves them everywhere. Building such smart environments is still difficult and complex due to lacking a uniform infrastructure that can adapt to diverse smart domains.Design/methodology/approach – To address this problem, the paper proposes an agent‐based pluggable infrastructure which integrates a mobile agent system named pvMogent, establishes an ontology‐based context model and introduces a workflow‐based application model with the open services gateway initiative (OSGi) framework. By plugging corresponding domain context in ontology model and different applications, the infrastructure can be customized to various domains.Findings – Through the implementation of several context‐aware applications, it was found that the infrastructure can large...

A model for context‐aware applications

Purpose – Context correctness and fault handling are very important to the quality of service in context‐aware applications. However, they are often ignored by researchers and application developers. This paper aims to present a model for context‐aware applications.Design/methodology/approach – This paper proposes a transaction model for context‐aware applications, in order to provide a uniform infrastructure for service quality management.Findings – The paper finds that, in this model, context‐aware applications are organized as a number of logic units and each unit may have a compensation module, which will be executed when errors or exceptions occur during the execution of those applications in order to minimize the bad infection. This model supports nested scopes and the number of levels of subtransactions is unlimited.Practical implications – The paper also presents an implementation of this transaction model, which is specialized for context‐aware use.Originality/value – This paper utilized a novel ...