Smart Kitchens for People with Cognitive Impairments: A Qualitative Study of Design Requirements

Individuals with cognitive impairments currently leverage extensive human resources during their transitions from assisted living to independent living. In Western Europe, many government-supported volunteer organizations provide sheltered living facilities; supervised environments in which people with cognitive impairments collaboratively learn daily living skills. In this paper, we describe communal cooking practices in sheltered living facilities and identify opportunities for supporting these with interactive technology to reduce volunteer workload. We conducted two contextual observations of twelve people with cognitive impairments cooking in sheltered living facilities and supplemented this data through interviews with four employees and volunteers who supervise them. Through thematic analysis, we identified four themes to inform design requirements for communal cooking activities: Work organization, community, supervision, and practicalities. Based on these, we present five design implications for assistive systems in kitchens for people with cognitive deficiencies

Smart Fire Prevention Device

House fire is mainly caused by stovetop of the kitchen. According to the research done by the NFPA, equipment unattended has become the leading factor in cooking fires and cooking fire casualties. The main idea of this project is to develop a prototype that can detect stovetop fire at the first place. To achieve the goal, the device uses temperature sensor to detect temperature on the stovetop and uses OpenMV camera to search human’s faces to determine whether there are people attendant and operating the Stovetop or not, meanwhile a timer will start to count down once the OpenMV determine that there is no one attendants front of stove

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work

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

Position paper on realizing smart products: challenges for Semantic Web technologies

In the rapidly developing space of novel technologies that combine sensing and semantic technologies, research on smart products has the potential of establishing a research field in itself. In this paper, we synthesize existing work in this area in order to define and characterize smart products. We then reflect on a set of challenges that semantic technologies are likely to face in this domain. Finally, in order to initiate discussion in the workshop, we sketch an initial comparison of smart products and semantic sensor networks from the perspective of knowledge technologies