Journal of Human-Computer Studies. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this

Publication informationGiven that the first generation of AAL systems will be deployed in the near future, it is incumbent on designers to factor this need for evolution and adaptivity in their designs and implementations. Thus this paper explores AAL from a number of prospective and considers an agent-based middleware approach to realising an architecture for evolutionary AAL.

The Internet of Robotic Things is an emerging vision that brings together pervasive sensors and objects with robotic and autonomous systems. This survey examines how the merger of robotic and Internet of Things technologies will advance the abilities of both the current Internet of Things and the current robotic systems, thus enabling the creation of new, potentially disruptive services. We discuss some of the new technological challenges created by this merger and conclude that a truly holistic view is needed but currently lacking.

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 agentbased 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 feedback 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.

In this paper, we present the results of deploying the first test prototype of the USMART low cost underwater sensor network in sea trials in Fort William, UK, on 29/06/2018 and 03/07/2018. We demonstrate the first ever hardware implementation of the TDA-MAC protocol for data gathering in underwater acoustic sensor networks (UASNs). The results show a successful application of TDA-MAC to remote environmental monitoring, integrating a range of different sensor nodes developed by the Universities of Heriot-Watt, York, Newcastle and Edinburgh. We focus on the practical challenges and their mitigation strategies related to TDA-MAC to increase its robustness in real-world deployments, compared with theoretical and simulation-based studies. The lessons learned from the sea trials reported in this paper prompted several crucial modifications to TDA-MAC which, in turn, form a solid foundation for further work on the development of TDA-MAC based UASNs.

Traditionally, social interaction research has concentrated on either fully virtually embodied agents (e.g. embodied conversational agents) or fully physically embodied agents (e.g. robots). For some time, however, both areas have started augmenting their agents' capabilities for social interaction using ubiquitous and intelligent environments.We are placing different agent systems for social interaction along Milgram's Reality-Virtuality Continuumaccording to the degree they are embodied in a physical, virtual or mixed reality environment-and show systems that follow the next logical step in this progression, namely social interaction in the middle of Milgram's continuum, that is, agents richly embodied in the physical and virtual world. This paper surveys the field of social interaction research with embodied agents with a particular view towards their embodiment forms and highlights some of the advantages and issues associated with the very recent field of social interaction with mixed reality agents.

Worldwide demographic projections point to a progressively older population. This fact has fostered research on Ambient Assisted Living, which includes developments on smart homes and social robots. To endow such environments with truly autonomous behaviours, algorithms must extract semantically meaningful information from whichever sensor data is available. Human activity recognition is one of the most active fields of research within this context. Proposed approaches vary according to the input modality and the environments considered. Different from others, this paper addresses the problem of recognising heterogeneous activities of daily living centred in home environments considering simultaneously data from videos, wearable IMUs and ambient sensors. For this, two contributions are presented. The first is the creation of the Heriot-Watt University/University of Sao Paulo (HWU-USP) activities dataset, which was recorded at the Robotic Assisted Living Testbed at Heriot-Watt University. This dataset differs from other multimodal datasets due to the fact that it consists of daily living activities with either periodical patterns or long-term dependencies, which are captured in a very rich and heterogeneous sensing environment. In particular, this dataset combines data from a humanoid robot’s RGBD (RGB + depth) camera, with inertial sensors from wearable devices, and ambient sensors from a smart home. The second contribution is the proposal of a Deep Learning (DL) framework, which provides multimodal activity recognition based on videos, inertial sensors and ambient sensors from the smart home, on their own or fused to each other. The classification DL framework has also validated on our dataset and on the University of Texas at Dallas Multimodal Human Activities Dataset (UTD-MHAD), a widely used benchmark for activity recognition based on videos and inertial sensors, providing a comparative analysis between the results on the two datasets considered. Results demonstrate that the introduction of data from ambient sensors expressively improved the accuracy results.

a b s t r a c tRobotic ecologies are systems made out of several robotic devices, including mobile robots, wireless sensors and effectors embedded in everyday environments, where they cooperate to achieve complex tasks. This paper demonstrates how endowing robotic ecologies with information processing algorithms such as perception, learning, planning, and novelty detection can make these systems able to deliver modular, flexible, manageable and dependable Ambient Assisted Living (AAL) solutions. Specifically, we show how the integrated and self-organising cognitive solutions implemented within the EU project RUBICON (Robotic UBIquitous Cognitive Network) can reduce the need of costly pre-programming and maintenance of robotic ecologies. We illustrate how these solutions can be harnessed to (i) deliver a range of assistive services by coordinating the sensing & acting capabilities of heterogeneous devices, (ii) adapt and tune the overall behaviour of the ecology to the preferences and behaviour of its inhabitants, and also (iii) deal with novel events, due to the occurrence of new user's activities and changing user's habits.