About the Project
Rapid population ageing is becoming a serious social challenge all over the world. It will not only have an impact on the economics, but also have a great impact to the quality of life of elderly people. This research aims to address this challenge through investigating and identifying the factors which have the effect on elderly quality of life. Aging people suffer several chronic illnesses which are placing an increasing burden on health systems and impacting the elderly quality of life. Identifying the effect indicators on elderly physical and mental health and improving elderly health status by reducing health inequalities are important to our modern society.
This project aims to identify the impact indicators on health of elderly people and investigate health inequalities from countries level cross the world to counties level cross the UK. The secondary data from World Health Organization (WHO) will be used to analyse elderly health inequality from physical perspective and primary method such as the mixed method of questionnaire design and interview will be used to analyse elderly health inequality from mental health perspective. The techniques of cluster analysis from the conventional K-means to the advanced expectation–maximization (EM) algorithm which can deal with a small sample size and unbalanced data will be used to identify the elderly health inequalities and the statistical inferences and Partial least square structural equation model (PLS-SEM) will be used for conducting the impact study and difference analysis.
Two REFable papers will be created during this project life time and it helps to build impact case study. The student will also have an opportunity to work and collaborate with the partners of the current EU funded project (CHARMED).
Eligibility and How to Apply:
Please note eligibility requirement:
• Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
• Appropriate IELTS score, if required.
• Applicants cannot apply for this funding if currently engaged in Doctoral study at Northumbria or elsewhere.
For further details of how to apply, entry requirements and the application form, see
https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/
Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. RDF19/BL/MOS/CANG) will not be considered.
Deadline for applications: Friday 25 January 2019
Start Date: 1 October 2019
Northumbria University is an equal opportunities provider and in welcoming applications for studentships from all sectors of the community we strongly encourage applications from women and under-represented groups.
Funding Notes
The studentship is available to Students Worldwide, and covers full fees and a full stipend, paid for three years at RCUK rates (for 2018/19, this is £14,777 pa).
References
Wang Y., Cang S. & Yu H. 2018. A Data Fusion based Hybrid Sensory System for Older People’s Daily Activity and Daily Routine Recognition. IEEE Sensors Journal (Impact Factor: 2.617)
Asghar I., Cang S. & Yu H. 2018. Impact evaluation of assistive technology support for the people with dementia. Assistive technology: the official journal of RESNA (ASSIST TECHNOL) (Impact Factor: 1.089)
Asghar I., Cang S. & Yu H. 2017. The Usability Evaluation of Assistive Technologies through Qualitative Research Focusing on the People with Mild Dementia. Computers in Human Behaviour (ABS 3*, Impact Factor: 3.536)
Asghar I., Cang S. & Yu H. 2017. Biblometric Study on Recent Research Activities of Assistive Technology for People with Dementia among Leading World Countries, Health Information and Libraries Journal, 34(1):5-19
Akbar, M.S., Cang S. & Yu H. 2017. IEEE 802.15.4 frame aggregation enhancement to provide high performance in life-critical patient monitoring systems. Sensors, 17 (2), 241
Akbar, M.S., Cang S. & Yu H. 2016. TMP: Tele-Medicine Protocol for Slotted 802.15.4 with Duty-Cycle Optimization in Wireless Body Area Sensor Networks IEEE Sensors Journal
Tan Z., Yu H. & Cang S. 2015. Impact of load variation on joint angle estimation from surface EMG signals. IEEE Transactions on Neural Systems & Rehabilitation Engineering (Impact Factor: 3.410)
Akbar S., Yu H. & Cang S. 2015. Delay, Reliability and Throughput based QoS Profile: A MAC Layer Performance Optimization Mechanism for Biomedical Applications in Wireless Body Area Sensor Networks. Journal of Sensors
Chernbumroong S, Cang S. and Yu H., 2015. Maximum relevancy maximum complementary feature selection for multi-sensor activity recognition. Expert Systems with Applications, 42(1), 573-583. (ABS 3*, Impact Factor: 3.928)
Chernbumroong S, Cang S. and Yu H., 2015. Genetic Algorithm based classifiers fusion for multi-sensor activity recognition of elderly people. IEEE Journal of Biomedical and Health Information, 19(1), 282-289. (ABS 3*, Impact Factor: 3.85)
Chernbumroong S, Cang S. and Yu H., 2014. A practical multi-sensor activity recognition system for home-based care, Decision Support Systems, 66, 61-70. (ABS 3*, Impact Factor: 3.56)
Cang S. and Yu. H., 2014. A combination selection algorithm on forecasting. European Journal of Operational Research, 234 (1), 127-139. (ABS 4*, Impact Factor: 3.428)
Cang S., 2009. Expectation maximization algorithm cluster analysis for UK national trust visitors. Tourism Analysis, 14(5), 637-650 (ABS 2*)
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