Accessibility of Health Data Representations for Older Adults: Challenges and Opportunities for Design

Health data of consumer off-the-shelf wearable devices is often conveyed to users through visual data representations and analyses. However, this is not always accessible to people with disabilities or older people due to low vision, cognitive impairments or literacy issues. Due to trade-offs between aesthetics predominance or information overload, real-time user feedback may not be conveyed easily from sensor devices through visual cues like graphs and texts. These difficulties may hinder critical data understanding. Additional auditory and tactile feedback can also provide immediate and accessible cues from these wearable devices, but it is necessary to understand existing data representation limitations initially. To avoid higher cognitive and visual overload, auditory and haptic cues can be designed to complement, replace or reinforce visual cues. In this paper, we outline the challenges in existing data representation and the necessary evidence to enhance the accessibility of health information from personal sensing devices used to monitor health parameters such as blood pressure, sleep, activity, heart rate and more. By creating innovative and inclusive user feedback, users will likely want to engage and interact with new devices and their own data

Design for Child-Robot Play The implications of Design Research within the field of Human-Robot Interaction studies for Children

This thesis investigates the intersections of three disciplines, that are Design Research, Human-Robot Interaction studies, and Child Studies. In particular, this doctoral research is focused on two research questions, namely, what is (or might be) the role of design research in HRI? And, how to design acceptable and desirable child-robot play applications? The first chapter introduces an overview of the mutual interest between robotics and design that is at the basis of the research. On the one hand, the interest of design toward robotics is documented through some exemplary projects from artists and designers that speculate on the human-robot coexistence condition. Vice versa, the robotics interest toward design is documented by referring to some tracks of robotic conferences, scienti c workshops and robotics journals which focused on the design-robotics relationship. Finally, a brief description of the background conditions that characterized this doctoral research are introduced, such as the fact of being a research founded by a company. The second chapter provides an overview of the state of the art of the intersections between three multidisciplinary disciplines. First, a de nition of Design Research is provided, together with its main trends and open issues. Then, the review focuses on the contribution of Design Research to the HRI eld, which can be summed up in actions focused on three aspects: artefacts, stakeholders, and contexts. This is followed by a focus on the role of Design Research within the context of children studies, in which it is possible to identify two main design-child relationships: design as a method for developing children’s learning experiences; and children as part of the design process for developing novel interactive systems. The third chapter introduces the Research through Design (RtD) approach and its relevance in conducting design research in HRI. The proposed methodology, based on this approach, is particularly characterized by the presence of design explorations as study methods. These, in turn, are developed through a common project’s methodology, also reported in this chapter. The fourth chapter is dedicated to the analysis of the scenario in which the child-robot interaction takes place. This was aimed at understanding what is edutainment robotics for children, its common features, how it relates to existing children play types, and where the interaction takes place. The chapter provides also a focus on the relationship between children and technology on a more general level, through which two themes and relative design opportunities were identi ed: physically active play and objects-to-think-with. These were respectively addressed in the two design explorations presented in this thesis: Phygital Play and Shybo. The Phygital Play project consists of an exploration of natural interaction modalities with robots, through mixed-reality, for fostering children’s active behaviours. To this end, a game platform was developed for allowing children to play with or against a robot, through body movement. Shybo, instead, is a low-anthropomorphic robot for playful learning activities with children that can be carried out in educational contexts. The robot, which reacts to properties of the physical environment, is designed to support different kinds of experiences. Then, the chapter eight is dedicated to the research outcomes, that were de ned through a process of reflection. The contribution of the research was analysed and documented by focusing on three main levels, namely: artefact, knowledge and theory. The artefact level corresponds to the situated implementations developed through the projects. The knowledge level consists of a set of actionable principles, emerged from the results and lessons learned from the projects. At the theory level, a theoretical framework was proposed with the aim of informing the future design of child- robot play applications. Thelastchapterprovidesa naloverviewofthe doctoral research, a series of limitations regarding the research, its process and its outcomes, and some indications for future research

Adaptive Shared Autonomy between Human and Robot to Assist Mobile Robot Teleoperation

Die Teleoperation vom mobilen Roboter wird in großem Umfang eingesetzt, wenn es für Mensch unpraktisch oder undurchführbar ist, anwesend zu sein, aber die Entscheidung von Mensch wird dennoch verlangt. Es ist für Mensch stressig und fehleranfällig wegen Zeitverzögerung und Abwesenheit des Situationsbewusstseins, ohne Unterstützung den Roboter zu steuern einerseits, andererseits kann der völlig autonome Roboter, trotz jüngsten Errungenschaften, noch keine Aufgabe basiert auf die aktuellen Modelle der Wahrnehmung und Steuerung unabhängig ausführen. Deswegen müssen beide der Mensch und der Roboter in der Regelschleife bleiben, um gleichzeitig Intelligenz zur Durchführung von Aufgaben beizutragen. Das bedeut, dass der Mensch die Autonomie mit dem Roboter während des Betriebes zusammenhaben sollte. Allerdings besteht die Herausforderung darin, die beiden Quellen der Intelligenz vom Mensch und dem Roboter am besten zu koordinieren, um eine sichere und effiziente Aufgabenausführung in der Fernbedienung zu gewährleisten. Daher wird in dieser Arbeit eine neuartige Strategie vorgeschlagen. Sie modelliert die Benutzerabsicht als eine kontextuelle Aufgabe, um eine Aktionsprimitive zu vervollständigen, und stellt dem Bediener eine angemessene Bewegungshilfe bei der Erkennung der Aufgabe zur Verfügung. Auf diese Weise bewältigt der Roboter intelligent mit den laufenden Aufgaben auf der Grundlage der kontextuellen Informationen, entlastet die Arbeitsbelastung des Bedieners und verbessert die Aufgabenleistung. Um diese Strategie umzusetzen und die Unsicherheiten bei der Erfassung und Verarbeitung von Umgebungsinformationen und Benutzereingaben (i.e. der Kontextinformationen) zu berücksichtigen, wird ein probabilistischer Rahmen von Shared Autonomy eingeführt, um die kontextuelle Aufgabe mit Unsicherheitsmessungen zu erkennen, die der Bediener mit dem Roboter durchführt, und dem Bediener die angemesse Unterstützung der Aufgabenausführung nach diesen Messungen anzubieten. Da die Weise, wie der Bediener eine Aufgabe ausführt, implizit ist, ist es nicht trivial, das Bewegungsmuster der Aufgabenausführung manuell zu modellieren, so dass eine Reihe von der datengesteuerten Ansätzen verwendet wird, um das Muster der verschiedenen Aufgabenausführungen von menschlichen Demonstrationen abzuleiten, sich an die Bedürfnisse des Bedieners in einer intuitiven Weise über lange Zeit anzupassen. Die Praxistauglichkeit und Skalierbarkeit der vorgeschlagenen Ansätze wird durch umfangreiche Experimente sowohl in der Simulation als auch auf dem realen Roboter demonstriert. Mit den vorgeschlagenen Ansätzen kann der Bediener aktiv und angemessen unterstützt werden, indem die Kognitionsfähigkeit und Autonomieflexibilität des Roboters zu erhöhen

Human Machine Interfaces for Teleoperators and Virtual Environments

In Mar. 1990, a meeting organized around the general theme of teleoperation research into virtual environment display technology was conducted. This is a collection of conference-related fragments that will give a glimpse of the potential of the following fields and how they interplay: sensorimotor performance; human-machine interfaces; teleoperation; virtual environments; performance measurement and evaluation methods; and design principles and predictive models

Generative Models for Learning Robot Manipulation Skills from Humans

A long standing goal in artificial intelligence is to make robots seamlessly interact with humans in performing everyday manipulation skills. Learning from demonstrations or imitation learning provides a promising route to bridge this gap. In contrast to direct trajectory learning from demonstrations, many problems arise in interactive robotic applications that require higher contextual level understanding of the environment. This requires learning invariant mappings in the demonstrations that can generalize across different environmental situations such as size, position, orientation of objects, viewpoint of the observer, etc. In this thesis, we address this challenge by encapsulating invariant patterns in the demonstrations using probabilistic learning models for acquiring dexterous manipulation skills. We learn the joint probability density function of the demonstrations with a hidden semi-Markov model, and smoothly follow the generated sequence of states with a linear quadratic tracking controller. The model exploits the invariant segments (also termed as sub-goals, options or actions) in the demonstrations and adapts the movement in accordance with the external environmental situations such as size, position and orientation of the objects in the environment using a task-parameterized formulation. We incorporate high-dimensional sensory data for skill acquisition by parsimoniously representing the demonstrations using statistical subspace clustering methods and exploit the coordination patterns in latent space. To adapt the models on the fly and/or teach new manipulation skills online with the streaming data, we formulate a non-parametric scalable online sequence clustering algorithm with Bayesian non-parametric mixture models to avoid the model selection problem while ensuring tractability under small variance asymptotics. We exploit the developed generative models to perform manipulation skills with remotely operated vehicles over satellite communication in the presence of communication delays and limited bandwidth. A set of task-parameterized generative models are learned from the demonstrations of different manipulation skills provided by the teleoperator. The model captures the intention of teleoperator on one hand and provides assistance in performing remote manipulation tasks on the other hand under varying environmental situations. The assistance is formulated under time-independent shared control, where the model continuously corrects the remote arm movement based on the current state of the teleoperator; and/or time-dependent autonomous control, where the model synthesizes the movement of the remote arm for autonomous skill execution. Using the proposed methodology with the two-armed Baxter robot as a mock-up for semi-autonomous teleoperation, we are able to learn manipulation skills such as opening a valve, pick-and-place an object by obstacle avoidance, hot-stabbing (a specialized underwater task akin to peg-in-a-hole task), screw-driver target snapping, and tracking a carabiner in as few as 4 - 8 demonstrations. Our study shows that the proposed manipulation assistance formulations improve the performance of the teleoperator by reducing the task errors and the execution time, while catering for the environmental differences in performing remote manipulation tasks with limited bandwidth and communication delays

Maintaining Structured Experiences for Robots via Human Demonstrations: An Architecture To Convey Long-Term Robot\u2019s Beliefs

This PhD thesis presents an architecture for structuring experiences, learned through demonstrations, in a robot memory. To test our architecture, we consider a specific application where a robot learns how objects are spatially arranged in a tabletop scenario. We use this application as a mean to present a few software development guidelines for building architecture for similar scenarios, where a robot is able to interact with a user through a qualitative shared knowledge stored in its memory. In particular, the thesis proposes a novel technique for deploying ontologies in a robotic architecture based on semantic interfaces. To better support those interfaces, it also presents general-purpose tools especially designed for an iterative development process, which is suitable for Human-Robot Interaction scenarios. We considered ourselves at the beginning of the first iteration of the design process, and our objective was to build a flexible architecture through which evaluate different heuristic during further development iterations. Our architecture is based on a novel algorithm performing a oneshot structured learning based on logic formalism. We used a fuzzy ontology for dealing with uncertain environments, and we integrated the algorithm in the architecture based on a specific semantic interface. The algorithm is used for building experience graphs encoded in the robot\u2019s memory that can be used for recognising and associating situations after a knowledge bootstrapping phase. During this phase, a user is supposed to teach and supervise the beliefs of the robot through multimodal, not physical, interactions. We used the algorithm to implement a cognitive like memory involving the encoding, storing, retrieving, consolidating, and forgetting behaviours, and we showed that our flexible design pattern could be used for building architectures where contextualised memories are managed with different purposes, i.e. they contains representation of the same experience encoded with different semantics. The proposed architecture has the main purposes of generating and maintaining knowledge in memory, but it can be directly interfaced with perceiving and acting components if they provide, or require, symbolical knowledge. With the purposes of showing the type of data considered as inputs and outputs in our tests, this thesis also presents components to evaluate point clouds, engage dialogues, perform late data fusion and simulate the search of a target position. Nevertheless, our design pattern is not meant to be coupled only with those components, which indeed have a large room of improvement

TECHNOLOGY STRATEGY FOR DEVELOPING THE ASSISTIVE ROBOTICS MARKET

Robotics has increased productivity in industries such as manufacturing, defence and construction but less so in healthcare where, despite the pressures from demographic changes, barriers to the adoption of assistive robotics (AR) persist. Due to the cutting-edge nature of the technology, the field requires studies that explore how it is developed and applied, effectively resulting in the development of an AR market in healthcare. Therefore, the research question for this thesis is ‘What strategy can be adopted to develop the AR market?’ This thesis adopted a Collaborative Action Research methodology to explore the development of an AR market in one UK region (Cornwall), and through this experience develop a Technology Strategy for building and orchestrating the creation of AR markets in other regions. This thesis is based on interdisciplinary research that draws from fields such as business management, entrepreneurship policy, robotics development and evaluation, and health technology adoption research. The intervention in Cornwall focussed on two key market constituents: the healthcare sector and producers (suppliers, i.e. firms and developers). The main work with the healthcare sector focused on supporting the AR adoption process. To this end, 35 events in Cornwall were used to raise awareness of AR, exploring healthcare challenges and the sector’s role in co-creation activities. The main work with the producers was to identify market barriers while actively supporting them in the product development process. Here, 28 AR companies in total from the UK, Ireland, the US, France and China working at different business stages were supported as part of this activity. Eight case studies were generated, including two completed trials of AR and two external strategic partnerships. An entrepreneurship programme that supported 58 entrepreneurs was designed, creating four robotic start-ups for the region. Finally, a lab for the evaluation of AR technologies to support companies was also established. Through the work with the healthcare sector, this thesis identified a lack of awareness of the AR market and the critical role that the sector plays in its development process. On the supply side, this thesis explored the main market barriers, including a lack of specialized agencies at a planning level, a fragmented healthcare sector that inhibits entrepreneurship, and outdated governmental policies for technology-based innovations. Overall, the findings confirmed a complete lack of preparedness and a need for changing traditional methods that are blocking innovation. Building upon these findings, this thesis presents a Technology Strategy for the creation of AR markets. The strategy offers practical recommendations on how regions can build and benefit from AR development. Through co-creation and open innovation principles, the strategy establishes key market actors and the multilateral nature of relationships between them. It also details a complete entrepreneurship programme to create companies for the region and business platforms to start the AR market. For the healthcare sector, it describes a complete AR knowledge awareness programme to guide the engagement with the sector. For the producers, it presents best practices and a new model for the development of AR technologies. This is the first study of its kind to offer a sector-specific Technology Strategy for the emerging AR market, aiming to improve the consolidation of this sector. The strategy could be used in regions that share characteristics with Cornwall, but its applicability to other regions is also worth exploring