An Exploration of Experiences of Transdisciplinary Research in Aging and Technology

Transdisciplinary research (TDR) involves academics/scientists collaborating with stakeholders from diverse disciplinary and sectoral backgrounds. While TDR has been recognized as beneficial in generating innovative solutions to complex social problems, knowledge is limited about researchers' perceptions and experiences of TDR in the aging and technology field. We conducted a qualitative study to address this knowledge gap by exploring how members of a pan-Canadian research network on aging and technology perceived and experienced TDR. Thirty members participated in semi-structured interviews. Interview data were analyzed thematically. Participants identified benefits that can be gained from implementing TDR, including mutual learning, improved capacity to understand and solve problems, and community engagement and empowerment. Participants also identified challenges to implementing TDR: communication issues and conflicting priorities among team members; tensions between traditional and TDR approaches; and difficulties identifying partners and developing partnerships. In addition, contradictions between TDR principles and participants' understanding of them became apparent. Nevertheless, some participants described successful strategies for implementing transdisciplinary principles in their projects: stakeholder engagement; language and goal sharing; and open, respectful communication. We offer recommendations to support TDR in aging and technology that focus on education and reform of the culture and values that can constrain efforts to practice TDR.Im Rahmen transdisziplinärer Forschung (TDF) arbeiten Wissenschaftler*innen mit Stakeholdern unterschiedlicher disziplinärer und sektoraler Herkunft zusammen. Während es mittlerweile akzeptiert scheint, dass TDF hilfreich ist, um innovative Lösungen für komplexe soziale Probleme zu generieren, ist das Wissen um Wahrnehmungen und Erfahrungen transdisziplinärer Forscher*innen im Bereich Alter(n) und Technologie vergleichsweise gering. Mittels einer qualitativen Studie mit Mitgliedern eines Pan-Kanadischen Forschungsnetzwerks haben wir versucht, diese Wissenslücke zu schließen. Mit 33 Mitgliedern des Netzwerkes wurden teilstrukturierte Interviews geführt, die thematisch analysiert wurden. Zu den berichteten Benefits von TDF gehörten u.a. wechselseitiges Lernen, verbesserte Möglichkeiten zum Verstehen und Lösen von Problemen  sowie Zugehörigkeit zu und Einbettung in die jeweilige Community. Erlebte Herausforderungen betrafen insbesondere kommunikative Schwierigkeiten und Prioritätskonflikte im Team, Spannungen zwischen Vertreter*innen von traditionellen vs. TDF-Ansätzen sowie Hindernisse beim Identifizieren von potenziellen Partner*innen. Zusätzliche waren Widersprüche zwischen TDF-Prinzipien und deren Verständnis durch die Interviewten offensichtlich. Einige der Gesprächspartner*innen haben gleichwohl Strategien beschrieben, die auf eine erfolgreiche Implementierung transdisziplinärer Prinzipien verweisen, nämlich das Engagement von Stakeholdern, das Teilen von Zielen und Sprachen sowie eine offene, respektvolle Kommunikation. Hiervon ausgehend bieten wir Empfehlungen für TDF zu Alter(n) und Technologie mit einem Fokus auf Bildung und auf eine Reform von Kulturen und Werten, die in der Praxis Bemühungen um TDF entgegenstehen

ICT skills acquisition by older people: motivations for learning and barriers to progression

This paper reports findings from one strand of an extensive research project investigating digital engagement of older people and the risks to sustained usage of information and communication technologies (ICTs). The factors that motivate older people to learn about ICTs, the barriers they face in the learning process and with on-going ICT use are examined. Research methods included focus groups (28 ICT learners aged 50+); questionnaires and interviews with seven 50+ learners; three interviews with ICT tutors; and observation sessions in three different ICT learning and support environments in England and Scotland. Findings show that while learning to use ICTs to ease the mechanics of daily life (e.g. on-line shopping) was a motivating factor for some, the more powerful drivers tended to be those applications seen as enriching quality of life e.g. using ICTs to keeping in contact with family and friends and using ICTs in pursuit of passions and interests. The key barriers relate to fear of using a computer; learning suppo rt ; quality and provision of ICT training; cost of training and technology; memory problems, and technology barriers. Implications of these findings for service providers, ICT designers and policy makers are identified and discussed

Use of an agile bridge in the development of assistive technology

Engaging with end users in the development of assistive technologies remains one of the major challenges for researchers and developers in the field of accessibility and HCI. Developing usable software systems for people with complex disabilities is problematic, software developers are wary of using user-centred design, one of the main methods by which usability can be improved, due to concerns about how best to work with adults with complex disabilities, in particular Severe Speech and Physical Impairments (SSPI) and how to involve them in research. This paper reports on how the adoption of an adapted agile approach involving the incorporation of a user advocate on the research team helped in meeting this challenge in one software project and offers suggestions for how this could be used by other development teams

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

What is a robot companion - friend, assistant or butler?

The study presented in this paper explored people's perceptions and attitudes towards the idea of a future robot companion for the home. A human-centred approach was adopted using questionnaires and human-robot interaction trials to derive data from 28 adults. Results indicated that a large proportion of participants were in favour of a robot companion and saw the potential role as being an assistant, machine or servant. Few wanted a robot companion to be a friend. Household tasks were preferred to child/animal care tasks. Humanlike communication was desirable for a robot companion, whereas humanlike behaviour and appearance were less essential. Results are discussed in relation to future research directions for the development of robot companions

Learning robot policies using a high-level abstraction persona-behaviour simulator

2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksCollecting data in Human-Robot Interaction for training learning agents might be a hard task to accomplish. This is especially true when the target users are older adults with dementia since this usually requires hours of interactions and puts quite a lot of workload on the user. This paper addresses the problem of importing the Personas technique from HRI to create fictional patients’ profiles. We propose a Persona-Behaviour Simulator tool that provides, with high-level abstraction, user’s actions during an HRI task, and we apply it to cognitive training exercises for older adults with dementia. It consists of a Persona Definition that characterizes a patient along four dimensions and a Task Engine that provides information regarding the task complexity. We build a simulated environment where the high-level user’s actions are provided by the simulator and the robot initial policy is learned using a Q-learning algorithm. The results show that the current simulator provides a reasonable initial policy for a defined Persona profile. Moreover, the learned robot assistance has proved to be robust to potential changes in the user’s behaviour. In this way, we can speed up the fine-tuning of the rough policy during the real interactions to tailor the assistance to the given user. We believe the presented approach can be easily extended to account for other types of HRI tasks; for example, when input data is required to train a learning algorithm, but data collection is very expensive or unfeasible. We advocate that simulation is a convenient tool in these cases.Peer ReviewedPostprint (author's final draft