Surveying human habit modeling and mining techniques in smart spaces

A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field

Modeling Interaction in Multi-Resident Activities

In this paper we investigate the problem of modeling multi-resident activities. Specifically, we explore different approaches based on Hidden Markov Models (HMMs) to deal with parallel activities and cooperative activities. We propose an HMM-based method, called CL-HMM, where activity labels as well as observation labels of different residents are combined to generate the corresponding sequence of activities as well as the corresponding sequence of observations on which a conventional HMM is applied. We also propose a Linked HMM (LHMM) in which activities of all residents are linked at each time step. We compare these two models to baseline models which are Coupled HMM (CHMM) and Parallel HMM (PHMM). The experimental results show that the proposed models outperform CHMM and PHMM when tested on parallel and cooperative activities

Multioccupant Activity Recognition in Pervasive Smart Home Environments

been the center of lot of research for many years now. The aim is to recognize the sequence of actions by a specific person using sensor readings. Most of the research has been devoted to activity recognition of single occupants in the environment. However, living environments are usually inhabited by more than one person and possibly with pets. Hence, human activity recognition in the context of multi-occupancy is more general, but also more challenging. The difficulty comes from mainly two aspects: resident identification, known as data association, and diversity of human activities. The present survey paper provides an overview of existing approaches and current practices for activity recognition in multi-occupant smart homes. It presents the latest developments and highlights the open issues in this field

Opportunity-Rich Schools and Sustainable Communities: Seven Steps to Align High-Quality Education With Innovations in City and Metropolitan Planning and Development

Details challenges and steps for linking quality education and community and economic vitality, including establishing a shared vision and metrics, aligning investments for prosperity, and expanding access via transportation. Lists promising practices

A Survey on Multi-Resident Activity Recognition in Smart Environments

Human activity recognition (HAR) is a rapidly growing field that utilizes smart devices, sensors, and algorithms to automatically classify and identify the actions of individuals within a given environment. These systems have a wide range of applications, including assisting with caring tasks, increasing security, and improving energy efficiency. However, there are several challenges that must be addressed in order to effectively utilize HAR systems in multi-resident environments. One of the key challenges is accurately associating sensor observations with the identities of the individuals involved, which can be particularly difficult when residents are engaging in complex and collaborative activities. This paper provides a brief overview of the design and implementation of HAR systems, including a summary of the various data collection devices and approaches used for human activity identification. It also reviews previous research on the use of these systems in multi-resident environments and offers conclusions on the current state of the art in the field.Comment: 16 pages, to appear in Evolution of Information, Communication and Computing Systems (EICCS) Book Serie

Community Development in Dynamic Neighborhoods: Synchronizing Services and Strategies with Immigrant Communities

Community development organizations must be increasingly cognizant of and responsive to their changing neighborhoods. Major demographic factors related to the growth and influx of recent immigrants to the United States are having a notable impact on many communities. Through a review of current research and interviews with leading experts and practitioners of community development organizations, private lenders and governmental agencies, this analysis explores (1) the importance of immigrants in community development, (2) the response of community development organizations to recent demographic shifts, and (3) the challenges and opportunities practitioners face when connecting immigrants to their communities.Despite growing research about the implications of immigrant markets for the private sector, there is little research about the role and contributions of community development organizations in the integration of new immigrants. Immigration trends and characteristics are different today than those of the late nineteenth and early twentieth centuries. This research concludes that these new demographics drive much of the dynamic change in cities across the United States. CDOs can best address the changes at the local level, but need more data and market analysis of neighborhood trends. These organizations are in a key position to connect newcomers not only to long-term housing, but also to business development, jobs and leadership opportunities through strategic partnerships and planning

A Modified KNN Algorithm for Activity Recognition in Smart Home

Nowadays, more and more elderly people cannot take care of themselves, and feel uncomfortable in daily activities. Smart home systems can help to improve daily life of elderly people. A smart home can bring residents a more comfortable living environment by recognizing the daily activities automatically. In this paper, in order to improve the accuracy of activity recognition in smart homes, we conduct some improvements in data preprocess and recognition phase, and more importantly, a novel sensor segmentation method and a modified KNN algorithm are proposed. The segmentation algorithm employs segment sensor data into fragments based on predefined activity knowledge, and then the proposed modified KNN algorithm uses center distances as a measure for classification. We also conduct comprehensive experiments, and the results demonstrate that the proposed method outperforms the other classifiers