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Activity Recognition

The Experts below are selected from a list of 23211 Experts worldwide ranked by ideXlab platform

Liming Chen – 1st expert 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, Jesse Hoey, Chris D. Nugent, Diane J. Cook, 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.

Zhiwen Yu – 2nd expert 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, Jesse Hoey, Chris D. Nugent, Diane J. Cook, 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 – 3rd expert 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.