Development of a technology adoption and usage prediction tool for assistive technology for people with dementia

This article is available open access through the publisher’s website at the link below. Copyright @ The Authors 2013.In the current work, data gleaned from an assistive technology (reminding technology), which has been evaluated with people with Dementia over a period of several years was retrospectively studied to extract the factors that contributed to successful adoption. The aim was to develop a prediction model with the capability of prospectively assessing whether the assistive technology would be suitable for persons with Dementia (and their carer), based on user characteristics, needs and perceptions. Such a prediction tool has the ability to empower a formal carer to assess, through a very limited amount of questions, whether the technology will be adopted and used.EPSR

Engineering Knowledge for Assistive Living

This paper introduces a knowledge based approach to assistive living in smart homes. It proposes a system architecture that makes use of knowledge in the lifecycle of assistive living. The paper describes ontology based knowledge engineering practices and discusses mechanisms for exploiting knowledge for activity recognition and assistance. It presents system implementation and experiments, and discusses initial results

Ontology-based Activity Recognition Framework and Services

This paper introduces an ontology-based integrated framework for activity modeling, activity recognition and activity model evolution. Central to the framework is ontological activity modeling and semantic-based activity recognition, which is supported by an iterative process that incrementally improves the completeness and accuracy of activity models. In addition, the paper presents a service-oriented architecture for the realization of the proposed framework which can provide activity context-aware services in a scalable distributed manner. The paper further describes and discusses the implementation and testing experience of the framework and services in the context of smart home based assistive living

Adding Security to Networks-on-Chip using Neural Networks

Modern computing systems are using Networks-on-Chip (NoCs) for scalable on-chip communications. Traditional security attacks have focused on the computing cores however modern attacks can focus on the NoC interconnect as a means to injecting unwanted traffic. This poses a significant security risk to physical systems that rely on sensory feedback and control signals. Malicious modification of these signals generates abnormal traffic patterns which affect the operation of the system and its performance. In this paper, we aim to identify abnormal traffic patterns (attacks) in Networks-on-Chip data through the use of Spiking Neural Networks. We explore the vulnerabilities of Denial-of-Service (DoS) attacks and report on evaluations to identify the impact of the duration of individual attacks on the rate of detection. Keywords—Networks-on-Chip, Denial-of-Service, Security, Spiking Neural Networks, Pattern Recognitio