Activity Recognition

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Liming Chen - One of the best experts on this subject based on the ideXlab platform.

  • Composite Activity Recognition
    Human Activity Recognition and Behaviour Analysis, 2019
    Co-Authors: Liming Chen, Chris Nugent
    Abstract:

    Activity Recognition is essential in providing Activity assistance for users in smart homes. While significant progress has been made for single-user single-Activity Recognition, it still remains a challenge to carry out real-time progressive composite Activity Recognition. This Chapter introduces a hybrid approach to composite Activity modelling and Recognition by extending existing ontology-based knowledge-driven approach with temporal modelling and reasoning methods. It combines and describes in details ontological Activity modelling which establishes relationships between activities and their involved entities, and temporal Activity modelling which defines relationships between constituent activities of a composite Activity, thus providing powerful representation capabilities for composite Activity modelling. The Chapter describes an integrated architecture for composite Activity Recognition, and elaborates a unified Activity Recognition algorithm for the Recognition of simple and composite activities. As an essential part of the model, the Chapter also presents methods for developing temporal entailment rules to support the interpretation and inference of composite activities. An example case study has been undertaken using a number of experiments to evaluate and demonstrate the proposed approach in a feature-rich multi-agent prototype system.

  • An Ontology-Based Approach to Activity Recognition
    Human Activity Recognition and Behaviour Analysis, 2019
    Co-Authors: Liming Chen, Chris Nugent
    Abstract:

    This chapter introduces an ontology-based knowledge-driven approach to real-time, continuous Activity Recognition based on multi-sensor data streams in the context of assisted living within smart homes. It first presents a generic system architecture for the proposed knowledge-driven approach and its underlying ontology-based Activity Recognition process. It then analyses the characteristics of smart homes and Activities of Daily Living (ADL) upon which both context and ADL ontologies are developed. Following this, the chapter describes algorithms for Activity Recognition based on semantic subsumption reasoning. Finally, an example case study is conducted using an implemented function-rich software system, which evaluates and demonstrates the proposed approach through extensive experiments involving a number of various ADL use scenarios.

  • Sensor-based Activity Recognition
    IEEE Transactions on Systems Man and Cybernetics Part C: Applications and Reviews, 2012
    Co-Authors: Liming Chen, Chris D. Nugent, Diane J. Cook, Jesse Hoey, Zhiwen Yu
    Abstract:

    Research on sensor-based Activity Recognition has recently made significant progress and is attracting growing attention in a number of disciplines and application domains. However, there is a lack of high-level overview on this topic that can inform related communities of the research state of the art. In this paper we present a comprehensive survey to examine the development and current status of various aspects of sensor-based Activity Recognition. We first discuss the general rationale and distinctions of vision-based and sensor-based Activity Recognition. Then we review the major approaches and methods associated with sensor-based Activity monitoring, modeling and Recognition from which strengths and weaknesses of those approaches are highlighted. We make a primary distinction in this paper between data-driven and knowledge-driven approaches, and use this distinction to structure our survey. We also discuss some promising directions for future research.

  • Activity Recognition in Pervasive Intelligent Environments - Activity Recognition in Pervasive Intelligent Environments
    Atlantis Ambient and Pervasive Intelligence, 2011
    Co-Authors: Liming Chen, Jit Biswas, Chris Nugent, Jesse Hoey
    Abstract:

    This book consists of a number of chapters addressing different aspects of Activity Recognition, roughly in three main categories of topics. The first topic will be focused on Activity modeling, representation and reasoning using mathematical models, knowledge representation formalisms and AI techniques. The second topic will concentrate on Activity Recognition methods and algorithms. Apart from traditional methods based on data mining and machine learning, we are particularly interested in novel approaches, such as the ontology-based approach, that facilitate data integration, sharing and automatic/automated processing. In the third topic we intend to cover novel architectures and frameworks for Activity Recognition, which are scalable and applicable to large scale distributed dynamic environments. In addition, this topic will also include the underpinning technological infrastructure, i.e. tools and APIs, that supports function/capability sharing and reuse, and rapid development and deployment of technological solutions. The fourth category of topic will be dedicated to representative applications of Activity Recognition in intelligent environments, which address the life cycle of Activity Recognition and their use for novel functions of the end-user systems with comprehensive implementation, prototyping and evaluation. This will include a wide range of application scenarios, such as smart homes, intelligent conference venues and cars.

  • Activity Recognition: Approaches, Practices and Trends
    Activity Recognition in Pervasive Intelligent Environments, 2011
    Co-Authors: Liming Chen, Ismail Khalil
    Abstract:

    Activity Recognition has attracted increasing attention as a number of related research areas such as pervasive computing, intelligent environments and robotics converge on this critical issue. It is also driven by growing real-world application needs in such areas as ambient assisted living and security surveillance. This chapter aims to provide an overview on existing approaches, current practices and future trends on Activity Recognition. It is intended to provide the necessary material to inform relevant research communities of the latest developments in this field in addition to providing a reference for researchers and system developers who are working towards the design and development of Activity-based context aware applications. The chapter first reviews the existing approaches and algorithms that have been used for Activity Recognition in a number of related areas. It then describes the practice and lifecycle of the ontology-based approach to Activity Recognition that has recently been under vigorous investigation. Finally the chapter presents emerging research on Activity Recognition by outlining various issues and directions the field will take.

Zhiwen Yu - One of the best experts on this subject based on the ideXlab platform.

  • Activity Recognition Using Ubiquitous Sensors
    Wearable Technologies, 2020
    Co-Authors: Yunji Liang, Xingshe Zhou, Zhiwen Yu
    Abstract:

    With the unprecedented sensing capabilities and the emergence of Internet of things, studies on Activity Recognition have been hot issues for different application areas, such as pervasive healthcare, industry and commerce, and recommendation systems. Much effort has been devoted to Activity Recognition using different sensors. Based on the differences of ubiquitous sensors, the authors classify the existing work into approximating sensing, wearable sensing, and video/audio sensing. Generally, methodologies for Activity Recognition are divided into logical reasoning and probabilistic reasoning. They illustrate the generalized framework and outline the advantages and disadvantages for each algorithm. Despite the research on Activity Recognition, Activity Recognition still faces many challenges in many aspects including nonintrusive data collection, scalable algorithms, energy consumption, and semantic extraction from social interaction. Towards those challenging research issues, the authors present their contributions to the field of Activity Recognition.

  • Sensor-based Activity Recognition
    IEEE Transactions on Systems Man and Cybernetics Part C: Applications and Reviews, 2012
    Co-Authors: Liming Chen, Chris D. Nugent, Diane J. Cook, Jesse Hoey, Zhiwen Yu
    Abstract:

    Research on sensor-based Activity Recognition has recently made significant progress and is attracting growing attention in a number of disciplines and application domains. However, there is a lack of high-level overview on this topic that can inform related communities of the research state of the art. In this paper we present a comprehensive survey to examine the development and current status of various aspects of sensor-based Activity Recognition. We first discuss the general rationale and distinctions of vision-based and sensor-based Activity Recognition. Then we review the major approaches and methods associated with sensor-based Activity monitoring, modeling and Recognition from which strengths and weaknesses of those approaches are highlighted. We make a primary distinction in this paper between data-driven and knowledge-driven approaches, and use this distinction to structure our survey. We also discuss some promising directions for future research.

Qiang Yang - One of the best experts on this subject based on the ideXlab platform.

  • Probabilistic Activity Recognition from low-level sensors
    2020
    Co-Authors: Qiang Yang
    Abstract:

    Activity Recognition is gaining increasing interest in the artificial intelligence (AI) and ubiquitous computing communities due to the dramatically advancing sensor technology. In this dissertation, we address the problem of probabilistic Activity Recognition from low-level sensor data. The novelty of our work can be seen from two fronts. First, in the pervasive computing literature, an important focus has been to determine a user's context from streams of sensor data. Despite the large amount of previous work done on computing the locations of users, there has been a lack of study on the problem of high-level Activity Recognition. Second, in the AI area, recognizing complex high-level behavior has traditionally been the focus of plan Recognition. However, most of the work has been restricted to high-level inferences in a logical framework, and the challenge of dealing with low-level sensor modeling has so far not been adequately addressed. In this dissertation, we propose several novel probabilistic algorithms for Activity Recognition from low-level sensors. Firstly, we present a novel clustering algorithm to automatically group user traces into a set of clusters, each corresponding to a typical class of user Activity patterns. Secondly, we propose a hierarchical Activity Recognition model, in which high-level inference about activities is enabled via a location-based sensor model at the low level. Thirdly, to reduce the calibration effort for Activity Recognition and to increase robustness in Recognition quality, we design a segmentation-based Activity Recognition model, in which activities can be directly recognized from sequences of discovered motion patterns. Finally, we also propose a novel approach to detecting users' abnormal activities from sensor data. We demonstrate the effectiveness of our proposed algorithms using the data collected in real wireless environments.

  • user dependent aspect model for collaborative Activity Recognition
    International Joint Conference on Artificial Intelligence, 2011
    Co-Authors: Vincent W Zheng, Qiang Yang
    Abstract:

    Activity Recognition aims to discover one or more users' actions and goals based on sensor readings. In the real world, a single user's data are often insufficient for training an Activity Recognition model due to the data sparsity problem. This is especially true when we are interested in obtaining a personalized model. In this paper, we study how to collaboratively use different users' sensor data to train a model that can provide personalized Activity Recognition for each user. We propose a user-dependent aspect model for this collaborative Activity Recognition task. Our model introduces user aspect variables to capture the user grouping information, so that a target user can also benefit from her similar users in the same group to train the Recognition model. In this way, we can greatly reduce the need for much valuable and expensive labeled data required in training the Recognition model for each user. Our model is also capable of incorporating time information and handling new user in Activity Recognition. We evaluate our model on a real-world WiFi data set obtained from an indoor environment, and show that the proposed model can outperform several state-of-art baseline algorithms.

  • IJCAI - Transfer learning for Activity Recognition via sensor mapping
    2011
    Co-Authors: Derek Hao Hu, Qiang Yang
    Abstract:

    Activity Recognition aims to identify and predict human activities based on a series of sensor readings. In recent years, machine learning methods have become popular in solving Activity Recognition problems. A special difficulty for adopting machine learning methods is the workload to annotate a large number of sensor readings as training data. Labeling sensor readings for their corresponding activities is a time-consuming task. In practice, we often have a set of labeled training instances ready for an Activity Recognition task. If we can transfer such knowledge to a new Activity Recognition scenario that is different from, but related to, the source domain, it will ease our effort to perform manual labeling of training data for the new scenario. In this paper, we propose a transfer learning framework based on automatically learning a correspondence between different sets of sensors to solve this transfer-learning in Activity Recognition problem. We validate our framework on two different datasets and compare it against previous approaches of Activity Recognition, and demonstrate its effectiveness.

  • Transfer Learning for Activity Recognition via Sensor Mapping
    IJCAI, 2011
    Co-Authors: Derek Hao Hu, Qiang Yang
    Abstract:

    Activity Recognition aims to identify and predict human activities based on a series of sensor readings. In recent years, machine learning methods have become popular in solving Activity Recognition problems. A special difficulty for adopting machine learning methods is the workload to annotate a large number of sensor readings as training data. Labeling sensor readings for their corresponding activities is a time-consuming task. In practice, we often have a set of labeled training instances ready for an Activity Recognition task. If we can transfer such knowledge to a new Activity Recognition scenario that is different from, but related to, the source domain, it will ease our effort to perform manual labeling of training data for the new scenario. In this paper, we propose a transfer learning framework based on automatically learning a correspondence between different sets of sensors to solve this transfer-learning in Activity Recognition problem. We validate our framework on two different datasets and compare it against previous approaches of Activity Recognition, and demonstrate its effectiveness.

Jesse Hoey - One of the best experts on this subject based on the ideXlab platform.

  • Sensor-based Activity Recognition
    IEEE Transactions on Systems Man and Cybernetics Part C: Applications and Reviews, 2012
    Co-Authors: Liming Chen, Chris D. Nugent, Diane J. Cook, Jesse Hoey, Zhiwen Yu
    Abstract:

    Research on sensor-based Activity Recognition has recently made significant progress and is attracting growing attention in a number of disciplines and application domains. However, there is a lack of high-level overview on this topic that can inform related communities of the research state of the art. In this paper we present a comprehensive survey to examine the development and current status of various aspects of sensor-based Activity Recognition. We first discuss the general rationale and distinctions of vision-based and sensor-based Activity Recognition. Then we review the major approaches and methods associated with sensor-based Activity monitoring, modeling and Recognition from which strengths and weaknesses of those approaches are highlighted. We make a primary distinction in this paper between data-driven and knowledge-driven approaches, and use this distinction to structure our survey. We also discuss some promising directions for future research.

  • Activity Recognition in Pervasive Intelligent Environments - Activity Recognition in Pervasive Intelligent Environments
    Atlantis Ambient and Pervasive Intelligence, 2011
    Co-Authors: Liming Chen, Jit Biswas, Chris Nugent, Jesse Hoey
    Abstract:

    This book consists of a number of chapters addressing different aspects of Activity Recognition, roughly in three main categories of topics. The first topic will be focused on Activity modeling, representation and reasoning using mathematical models, knowledge representation formalisms and AI techniques. The second topic will concentrate on Activity Recognition methods and algorithms. Apart from traditional methods based on data mining and machine learning, we are particularly interested in novel approaches, such as the ontology-based approach, that facilitate data integration, sharing and automatic/automated processing. In the third topic we intend to cover novel architectures and frameworks for Activity Recognition, which are scalable and applicable to large scale distributed dynamic environments. In addition, this topic will also include the underpinning technological infrastructure, i.e. tools and APIs, that supports function/capability sharing and reuse, and rapid development and deployment of technological solutions. The fourth category of topic will be dedicated to representative applications of Activity Recognition in intelligent environments, which address the life cycle of Activity Recognition and their use for novel functions of the end-user systems with comprehensive implementation, prototyping and evaluation. This will include a wide range of application scenarios, such as smart homes, intelligent conference venues and cars.

Paul J M Havinga - One of the best experts on this subject based on the ideXlab platform.

  • a survey of online Activity Recognition using mobile phones
    Sensors, 2015
    Co-Authors: Muhammad Shoaib, Stephan Bosch, Ozlem Durmaz Incel, Hans Scholten, Paul J M Havinga
    Abstract:

    Physical Activity Recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors. In most of the current studies, sensor data collected for Activity Recognition are analyzed offline using machine learning tools. However, there is now a trend towards implementing Activity Recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery. The research on offline Activity Recognition has been reviewed in several earlier studies in detail. However, work done on online Activity Recognition is still in its infancy and is yet to be reviewed. In this paper, we review the studies done so far that implement Activity Recognition systems on mobile phones and use only their on-board sensors. We discuss various aspects of these studies. Moreover, we discuss their limitations and present various recommendations for future research.