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

Implementing Learning Design by lams to improve teaching and learning

Learning Design has the potential to revolutionize e-learning by capturing the process of education, rather than simply content. By describing sequences of collaborative learning activities, Learning Design offers a new approach to re-use in e-learning. E-learning has a well developed approach to the creation and sequencing of content-based, single learner, self-paced learning objects. While definitions of Learning Design vary, the main elements tend to include greater focus on context dimensions of e-learning, a more activity based view of e-learning, and greater recognition of the role of multi-learner environments. While Learning Design does not exclude single learner, self-paced modes of e-learning, it draws attention to a wider range of collaborative e-learning approaches in addition to single learner approaches. This paper shows an example, which is applied to speciality of economic and rural development agricultural engineer at University of Debrecen and its implementation in the Learning Activity Management System. We created a learning design was implemented at this speciality with LAMS, which is a learning design editing and play back tool that puts the learning process, rather than collections of content, at the heart of e-learning

MOSDEN: A Scalable Mobile Collaborative Platform for Opportunistic Sensing Applications

Mobile smartphones along with embedded sensors have become an efficient enabler for various mobile applications including opportunistic sensing. The hi-tech advances in smartphones are opening up a world of possibilities. This paper proposes a mobile collaborative platform called MOSDEN that enables and supports opportunistic sensing at run time. MOSDEN captures and shares sensor data across multiple apps, smartphones and users. MOSDEN supports the emerging trend of separating sensors from application-specific processing, storing and sharing. MOSDEN promotes reuse and re-purposing of sensor data hence reducing the efforts in developing novel opportunistic sensing applications. MOSDEN has been implemented on Android-based smartphones and tablets. Experimental evaluations validate the scalability and energy efficiency of MOSDEN and its suitability towards real world applications. The results of evaluation and lessons learned are presented and discussed in this paper.Comment: Accepted to be published in Transactions on Collaborative Computing, 2014. arXiv admin note: substantial text overlap with arXiv:1310.405

Implementing learning design by LAMS to improve teaching and learning

Learning Design has the potential to revolutionize e-learning by capturing the process of education, rather than simply content. By describing sequences of collaborative learning activities, Learning Design offers a new approach to re-use in e-learning. E-learning has a well developed approach to the creation and sequencing of content-based, single learner, self-paced learning objects. While definitions of Learning Design vary, the main elements tend to include greater focus on context dimensions of e-learning, a more activity based view of e-learning, and greater recognition of the role of multi-learner environments. While Learning Design does not exclude single learner, self-paced modes of elearning, it draws attention to a wider range of collaborative e-learning approaches in addition to single learner approaches. This paper shows an example, which is applied to speciality of economic and rural development agricultural engineer at University of Debrecen and its implementation in the Learning Activity Management System. We created a learning design was implemented at this speciality with LAMS, which is a learning design editing and play back tool that puts the learning process, rather than collections of content, at the heart of e-learnin

Mixed reality participants in smart meeting rooms and smart home enviroments

Human–computer interaction requires modeling of the user. A user profile typically contains preferences, interests, characteristics, and interaction behavior. However, in its multimodal interaction with a smart environment the user displays characteristics that show how the user, not necessarily consciously, verbally and nonverbally provides the smart environment with useful input and feedback. Especially in ambient intelligence environments we encounter situations where the environment supports interaction between the environment, smart objects (e.g., mobile robots, smart furniture) and human participants in the environment. Therefore it is useful for the profile to contain a physical representation of the user obtained by multi-modal capturing techniques. We discuss the modeling and simulation of interacting participants in a virtual meeting room, we discuss how remote meeting participants can take part in meeting activities and they have some observations on translating research results to smart home environments

Building Collaborative Capacities in Learners: The M/cyclopedia Project Revisited

In this paper we trace the evolution of a project using a wiki-based learning environment in a tertiary education setting. The project has the pedagogical goal of building learners’ capacities to work effectively in the networked, collaborative, creative environments of the knowledge economy. The paper explores the four key characteristics of a ‘produsage’ environment and identifies four strategic capacities that need to be developed in learners to be effective ‘produsers’ (user-producers). A case study is presented of our experiences with the subject New Media Technologies, run at Queensland University of Technology, Brisbane, Australia. This progress report updates our observations made at the 2005 WikiSym conference

Structured evaluation of virtual environments for special-needs education

This paper describes the development of a structured approach to evaluate experiential and communication virtual learning environments (VLEs) designed specifically for use in the education of children with severe learning difficulties at the Shepherd special needs school in Nottingham, UK. Constructivist learning theory was used as a basis for the production of an evaluation framework, used to evaluate the design of three VLEs and how they were used by students with respect to this learning theory. From an observational field study of student-teacher pairs using the VLEs, 18 behaviour categories were identified as relevant to five of the seven constructivist principles defined by Jonassen (1994). Analysis of student-teacher behaviour was used to provide support for, or against, the constructivist principles. The results show that the three VLEs meet the constructivist principles in very different ways and recommendations for design modifications are put forward

Efficient Opportunistic Sensing using Mobile Collaborative Platform MOSDEN

Mobile devices are rapidly becoming the primary computing device in people's lives. Application delivery platforms like Google Play, Apple App Store have transformed mobile phones into intelligent computing devices by the means of applications that can be downloaded and installed instantly. Many of these applications take advantage of the plethora of sensors installed on the mobile device to deliver enhanced user experience. The sensors on the smartphone provide the opportunity to develop innovative mobile opportunistic sensing applications in many sectors including healthcare, environmental monitoring and transportation. In this paper, we present a collaborative mobile sensing framework namely Mobile Sensor Data EngiNe (MOSDEN) that can operate on smartphones capturing and sharing sensed data between multiple distributed applications and users. MOSDEN follows a component-based design philosophy promoting reuse for easy and quick opportunistic sensing application deployments. MOSDEN separates the application-specific processing from the sensing, storing and sharing. MOSDEN is scalable and requires minimal development effort from the application developer. We have implemented our framework on Android-based mobile platforms and evaluate its performance to validate the feasibility and efficiency of MOSDEN to operate collaboratively in mobile opportunistic sensing applications. Experimental outcomes and lessons learnt conclude the paper