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IEEE SMC Chapter Webinar – Lifelong Learning in Human Activity Recognition by Dr Juan Ye

This webinar is designed to discuss future research trends in human activity recognition, i.e. lifelong learning – how to automatically and continuously adapt activity recognition systems to unexpected, unavoidable and constant changes in human actions, sensing technologies, and environments.

Systems Man and Cybernetics Society

Human activity recognition systems will be increasingly deployed in real-world environments and for longer periods of time. This significantly challenges current approaches to human activity recognition, which have to account for changes in activity routines, evolution of situations, and of sensing technologies. Driven by these challenges, this webinar will argue the need to move beyond learning to lifelong machine learning – with the ability to incrementally and continuously adapt to changes in the environment being learned. We will introduce a conceptual framework for lifelong machine learning to structure various relevant proposals in the area, and identify some key research challenges that remain.

Who Should Attend:
This webinar is intended for everyone, from students already studying a branch of human activity recognition to sensor systems and IoTs, to practitioners who work in the field of ambient assisted living, to people who track their own health conditions.

Webinar Objectives:

    • Provide a general background in human activity recognition of relevance to ambient assisted living
    • Identify challenges in lifelong learning and the limitations of state-of-the-art activity recognition techniques
    • Outline future directions to address these challenges

Speaker:
Dr Juan Ye is a lecturer in the School of Computer Science at the University of St Andrews, UK. Her research interest centres on human behaviour recognition and analysis, specialising in ontologies, uncertainty and temporal reasoning, applied machine learning, and data mining techniques. From her PhD study to present, she has published ~70 papers in top-tier journals and conferences, including three canonical reviews on human activity recognition and identify the key research challenges and open questions. She has designed and developed techniques advancing the area of human activity recognition with emphasis on unsupervised learning, transfer learning, and emerging activity discovery. She is a leading researcher in human activity recognition and pervasive computing.

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