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Monitoring of Physical Activity and Sedentary Behaviour Project Description


School of Engineering and the Built Environment (SEBE)

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Prof H Yu No more applications being accepted Funded PhD Project (Students Worldwide)
Edinburgh United Kingdom Applied Mathematics Electrical Engineering Software Engineering

About the Project

This project aims to examine the dynamical interplay between physical activity, sedentary behaviour, the natural environment and wellbeing in the elderly[1-7]. 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 elderly wellbeing and a wide range of health outcomes. We will implement new

methodologies, incorporating data analysis, with state-of-art wearable devices, to offer novel insights into elderly activity and the relationship to their wellbeing with a particular focus of the impact of environmental factors. The study will also incorporate the development and refinement of a prototype 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 using wearable sensors and ambient sensors; acquisition of ambient environmental parameters, statistics and data modelling.

Expected progress beyond the state-of-the-art: The project will implement new assessments methodologies based on the previous research [1-7], incorporating data analysis, with state-of-art wearable devices [2]. 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 in Computing, or Computing Engineering, or Electronics and electrical engineering, or Robotics, or Mathematics with a good fundamental knowledge of software engineering, programming, wearable sensors and data analysis.

English language requirement

IELTS score must be at least 6.5 (with not 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


Funding Notes

MSCA-ITN offers an attractive salary and working conditions. A unique feature of MSCA-ITN is that during the PhD research, PhD students will be given the opportunity to perform at least two secondments of about 3 months each at the facilities of other consortium members. ESR PhD students will benefit from a dedicated training program in the various fields of expertise of the consortium partners. Work contract with a salary of approx. 3,270 euros per month gross; plus 600 euros mobility allowance and (if applicable) 500 euros family allowance. The research costs of the host organisation(s) are also supported.

References

1]. Yan Wang, Shuang Cang, Hongnian Yu, Mutual Information Inspired Feature Selection Using Kernel

Canonical Correlation Analysis, Expert Systems with Applications, Vol. 4, November 2019 [2]. 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 [3]. 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 [4]. 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 [5]. 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 [6]. 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, [7]. Saisakul Chernbumroong, Shuang Cang, Anthony Atkins, Hongnian Yu, Elderly activities recognition and classification for applications in assisted living, Expert Systems with Applications, 2013
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