Healthcare systems around the world is under unprecedented pressure due to our ageing population. According to the Office for National Statistics, around 18.2% of the UK population were aged 65 or older at mid-2017 and this number is projected to grow to 20.7% by 2027. Recent years have seen a growing demand in developing robotics and autonomous systems for supporting independent living which have been making increasing impact in many sectors. For example, it has been highlighted that the most effective way to deliver the need of the rehabilitation people who require hundreds of repetitive movements each day is to automate routine therapy with assistant robots . However, how to make assistant robots to better understand the needs of the independent living people and interact in an effective and intuitive manner is challenging.
Thus, the main objectives of this project are to provide an effective indoor navigation system for AI assistant robots, and to improve human-machine interaction by realizing the autonomous cognition of user emotion.
The main challenges include
1.How to implement robot indoor navigation? Despite recent advances, autonomous indoor robot navigation is far from ideal due to the complexity of the real environment.
2.How to effectively implement human-machine interaction by improving automatic emotion recognition of elderly people? The main challenge would be to implement multimodal machine learning  which can combine information perceived through multiple modalities such as speech, facial expressions, and physiological signals.
It is envisaged that the ongoing inter-disciplinary and inter-institutional collaborations will support the development of this project.
 UK Robotics and Autonomous Systems Network, “Robotics in social care: a connected care ecosystem for independent living”, 2017. [Online] Available: https://www.housinglin.org.uk/_assets/Resources/Housing/OtherOrganisation/UK_RAS_robitics-in-care-report.pdf. [Accessed: 10-December-2018]
 T. Baltrušaitis, C. Ahuja, and L.-P. Morency, “Multimodal machine learning: A survey and taxonomy”, 2017. [Online]. Available: https://arxiv.org/abs/1705.09406. [Accessed: 10-December-2018]
Vice Chancellors Research Scholarships (VCRS)
The scholarships will cover tuition fees and a maintenance award of £14,777 per annum for three years (subject to satisfactory academic performance). Applications are invited from UK, European Union and overseas students.
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 14,777 per annum for three years. EU applicants will only be eligible for the fees component of the studentship (no maintenance award is provided). For Non EU nationals the candidate must be "settled" in the UK.
As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day
Adrian Johnston - PhD in InformaticsWatch Video
Monday 18 February 2019
25 to 29 March 2019
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Monday 25 November 2019
Computer Science and Informatics