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Monitoring of Physical Activity and Sedentary Behaviour SEBE0035

School of Engineering and the Built Environment (SEBE)

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Prof H Yu No more applications being accepted Funded PhD Project (European/UK Students Only)

About the Project

Ambulatory assessments (AA) comprises the use of field methods to assess the ongoing behaviour, physiology, experience and environmental aspects of people in the natural setting. Ecologically-valid tools are used to understand biopsychosocial processes in a spatiotemporal context. This project will examine the dynamical interplay between physical activity, sedentary behaviour, the natural environment and Subjective Well-Being (SWB) in the elderly. Movement and exertion, and in particular, the difference in the amount of physical activity and sedentary behaviour have consistently been found to be related to SWB and a wide range of health outcomes. We will implement new AA methodologies, incorporating t-SNE analysis, with actigraphy based upon state-of-art wearable devices, to offer novel insights into elderly activity and the relationship to SWB with a particular focus of the impact of environmental factors. The study will also incorporate the development and refinement of a prototype AA method to look at intervention with real-time analysis and real-time feedback of physical inactivity and sedentary behaviour.

Methodology: The use of experience sampling and ecological momentary assessment; repeated-entry diary techniques; monitoring of physiological function in combination with or without physical behaviour; acquisition of ambient environmental parameters; applied psychology; statistics and data modelling, psychophysiology, science communication.

Expected progress beyond the state-of-the-art: The project will implement new ambulatory assessments methodologies, incorporating t-SNE analysis, with actigraphy based upon state-of-art wearable devices. This would include the use of accelerometer, magnetometer and gyroscope intelligent multi-sensor based personalized risk assessment systems to monitor gait and stability as well as a wrist device for activity recognition and environmental monitoring.

Academic qualifications
A first degree (at least a 2.1) ideally Electronic and Electrical Engineering with a good fundamental knowledge of wearable sensors and data analysis

English language requirement
IELTS score must be at least 6.5 (with no less than 6.0 in each of the four components. Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Essential attributes
• Experience of fundamental sensor technology and data analysis
• Competent in statistics and data modelling
• Knowledge of applied statistics
• Good written and oral communication skills
• Strong motivation, with evidence of independent research skills relevant to the project
• Good time management

Desirable attributes
• Awareness of issues linked to digital healthcare for the ageing society

For informal enquiries please contact Professor Hongnian Yu ([Email Address Removed])

Funding Notes

Full Scholarship for UK/EU applicants


Yan Wang, Shuang Cang, Hongnian Yu, Mutual Information Inspired Feature Selection Using Kernel Canonical Correlation Analysis, Expert Systems with Applications, Vol. 4, November 2019

Yan Wang, Shuang Cang, Hongnian Yu, A survey on wearable sensor modality centred human activity recognition in health care, Expert Systems with Applications, Volume 137, Pages 167-190, 2019

Arif Reza Anwary, Hongnian Yu and Michael Vassallo, Gait Evaluation using Procrustes and Euclidean Distance Matrix Analysis, IEEE Journal of Biomedical and Health Informatics, 2019

Yan Wang, Shuang Cang, Hongnian Yu, Improving Daily Activity Recognition Accuracy for Older People: Data fusion based on a case study in a Hybrid Sensory Environment, IEEE Sensors Journal, 18(16), pp. 6874 – 6888, 2018

Arif Reza Anwary, Hongnian Yu and Michael Vassallo, Optimal foot location for placing wearable IMU sensors and automatic feature extraction for gait analysis, IEEE Sensors Journal, pp. 2555 – 2567, 18(6), 2018

Saisakul Chernbumroong, Shuang Cang and Hongnian Yu, A practical multi-sensor activity recognition framework for home-based care, Decision Support Systems, 66, pp. 61-70, 2014,

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