Activity Analysis, Summarization, and Visualization for Indoor Human Activity Monitoring

DOI 10.1109/TCSVT.2008.2005612In this work, we study how continuous video monitoring and intelligent video processing can be used in eldercare to assist the independent living of elders and to improve the efficiency of eldercare practice. More specifically, we develop an automated activity analysis and summarization for eldercare video monitoring. At the object level, we construct an advanced silhouette extraction, human detection and tracking algorithm for indoor environments. At the feature level, we develop an adaptive learning method to estimate the physical location and moving speed of a person from a single camera view without calibration. At the action level, we explore hierarchical decision tree and dimension reduction methods for human action recognition. We extract important ADL (activities of daily living) statistics for automated functional assessment. To test and evaluate the proposed algorithms and methods, we deploy the camera system in a real living environment for about a month and have collected more than 200 hours (in excess of 600 G bytes) of activity monitoring videos. Our extensive tests over these massive video datasets demonstrate that the proposed automated activity analysis system is very efficient.This work was supported in part by National Institute of Health under Grant 5R21AG026412

Leveraging the Potential of Novel Data in Power Line Communication of Electricity Grids

Electricity grids have become an essential part of daily life, even if they are often not noticed in everyday life. We usually only become particularly aware of this dependence by the time the electricity grid is no longer available. However, significant changes, such as the transition to renewable energy (photovoltaic, wind turbines, etc.) and an increasing number of energy consumers with complex load profiles (electric vehicles, home battery systems, etc.), pose new challenges for the electricity grid. To address these challenges, we propose two first-of-its-kind datasets based on measurements in a broadband powerline communications (PLC) infrastructure. Both datasets FiN-1 and FiN-2, were collected during real practical use in a part of the German low-voltage grid that supplies around 4.4 million people and show more than 13 billion datapoints collected by more than 5100 sensors. In addition, we present different use cases in asset management, grid state visualization, forecasting, predictive maintenance, and novelty detection to highlight the benefits of these types of data. For these applications, we particularly highlight the use of novel machine learning architectures to extract rich information from real-world data that cannot be captured using traditional approaches. By publishing the first large-scale real-world dataset, we aim to shed light on the previously largely unrecognized potential of PLC data and emphasize machine-learning-based research in low-voltage distribution networks by presenting a variety of different use cases

Healthcare Event and Activity Logging.

The health of patients in the intensive care unit (ICU) can change frequently and inexplicably. Crucial events and activities responsible for these changes often go unnoticed. This paper introduces healthcare event and action logging (HEAL) which automatically and unobtrusively monitors and reports on events and activities that occur in a medical ICU room. HEAL uses a multimodal distributed camera network to monitor and identify ICU activities and estimate sanitation-event qualifiers. At the core is a novel approach to infer person roles based on semantic interactions, a critical requirement in many healthcare settings where individuals' identities must not be identified. The proposed approach for activity representation identifies contextual aspects basis and estimates aspect weights for proper action representation and reconstruction. The flexibility of the proposed algorithms enables the identification of people roles by associating them with inferred interactions and detected activities. A fully working prototype system is developed, tested in a mock ICU room and then deployed in two ICU rooms at a community hospital, thus offering unique capabilities for data gathering and analytics. The proposed method achieves a role identification accuracy of 84% and a backtracking role identification of 79% for obscured roles using interaction and appearance features on real ICU data. Detailed experimental results are provided in the context of four event-sanitation qualifiers: clean, transmission, contamination, and unclean

Seismic Risk of Inter-urban Transportation Networks

AbstractThe paper presents a holistic approach for assessing and managing the seismic risk and potential loss in inter-urban highway networks in earthquake-prone areas. The vulnerability of all elements of the intercity transportation system (i.e., roads, bridges, abutments, retaining walls, and tunnels) is assessed considering the interdependency among the structural, transportational and geotechnical components of the network under different seismic scenarios. Both the direct earthquake-induced damage, as well as the indirect socio-economic loss attributed to reduced network functionality are taken into account in an explicit and transparent formulation that is then displayed in space through an ad-hoc developed GIS-based software. The methodology and the decision-making tools developed are adequately modular, for them to be utilized after appropriate adaptation by local authorities in identifying, prior to a major earthquake event, those vulnerable components of their network whose failure may have a disproportional socio-economic impact. In this way, a rational and effective emergency plan can be deployed to minimize potential human, social and financial loss after a future earthquake. The outline of a foreseen application is also presented for the case of the road network of the Region of Western Macedonia in Greece. Through this pilot application, the methodology is to be optimized in real conditions before being cast in the form of a fully parameterised seismic risk tool, to be used in other earthquake prone areas as well

Boosted Beta regression.

Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1). Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures

Conceptual Architecture and Service-oriented Implementation of a Regional Geoportal for Rice Monitoring

Agricultural monitoring has greatly benefited from the increased availability of a wide variety of remote-sensed satellite imagery, ground-sensed data (e.g., weather station networks) and crop models, delivering a wealth of actionable information to stakeholders to better streamline and improve agricultural practices. Nevertheless, as the degree of sophistication of agriculture monitoring systems increases, significant challenges arise due to the handling and integration of multi-scale data sources to present information to decision-makers in a way which is useful, understandable and user friendly. To address these issues, in this article we present the conceptual architecture and service-oriented implementation of a regional geoportal, specifically focused on rice crop monitoring in order to perform unified monitoring with a supporting system at regional scale. It is capable of storing, processing, managing, serving and visualizing monitoring and generated data products with different granularity and originating from different data sources. Specifically, we focus on data sources and data flow, and their importance for and in relation to different stakeholders. In the context of an EU-funded research project, we present an implementation of the regional geoportal for rice monitoring, which is currently in use in Europe’s three largest rice-producing countries, Italy, Greece and Spain

Faculty of Engineering and Design. Research Review

STUDENTS AND ACADEMICS - This publication introduces you to the department or school and then each faculty member’s research areas, research applications, and their most recent activities. A comprehensive index can be found at the back of this publication to help guide you by specific areas of interest, as well as point out interdisciplinary topics and researchers. INDUSTRY LEADERS - This publication includes information regarding specific facilities, labs, and research areas of departments and schools as well as individual faculty members and researchers. A comprehensive index can be found at the back of this publication to help guide you by specific areas of interest, as well as point out interdisciplinary topics and researchers