Ulster University, UK
Maurice Mulvenna is Professor of Computer Science at Ulster University. His research areas include artificial intelligence, digital interventions for health and wellbeing, and assistive technologies. He is currently involved in several international research projects, including H2020 MIDAS (Meaningful Integration of Data Analytics and Services), H2020 SenseCare (Sensor Enabled Affective Computing for Enhancing Medical Care, and UK HSC Facilitated reminiscence for people living with dementia. Arising from his research, he has published around 300 papers and served on many program committees. He is co-chair of the 32nd British Human-Computer Interaction conference in 2018, and the 31st European Cognitive Ergonomics conference in 2019. He served for three years on UK Ofcom’s Advisory Committee on Older and Disabled People and currently serves on the editorial boards for several academic journals including the IET Journal of Engineering, Journal of Enabling Technologies and JMIR Rehabilitation and Assistive Technologies. In 2014, he was elected as a Board Member of the International Society for Gerontechnology (ISG). Maurice is also a past winner of the European €100K IST Grand Prize and has won with colleagues the Best Innovation in Practice Award at the Dementia Care Awards. He is a senior member of both the Institute of Electrical and Electronics Engineers and the Association for Computing Machinery, and a chartered fellow of the British Computer Society.
Title: Digital Health Interaction Data - From Smart Homes to Smart Everywhere
Abstract: The talk begins by reviewing the evolution of the use of technology to support peoples’ health and wellbeing, from telecare and telehealth through to personalised healthcare, the growth of the idea of ‘quantified self’ and ultimately, self-managed care. This talk then focuses on the growing use of commercially available digital devices and software for selfcare, and the explosion in the data arising from their use in society. The opportunities for the application of machine learning to the data, including for example, event logging data and ecological momentary assessment data are discussed. The implications are explored, across such areas as big data for research study design, ethics, the ‘servitization’ of machine learning, bias, surveillance, and health and wellbeing services.