Job Description: Developing machine-learning algorithms based on accelerometer data for assessment of energy expenditure, coordination skill and motoneuronal control in home healthcare applications. Physical activity is affected by health, and thus physical activity is an indicator of health status. Increasing age is accompanied by a decline in coordination and aerobic capacity. In this project, age associated changes as mentioned are studied by measuring patterns of objectively assessed physical activity and smoothness of typical activities like walking. Altered neural control disrupts the timing and muscle patterns necessary for smooth and regular stepping.
The age related decline in cardio-respiratory response to exercise accelerates after the age of fourth decade of life. The project will be a combination of cross sectional studies and longitudinal studies, where subjects participate in regular activity programs. Measures for coordination and aerobic capacity are derived from newly developed accelerometers for movement registration in combination with the assessment of energy expenditure.
The project aims at discovering appropriate data mining techniques to unravel complex accelerometry data as markers of health, derived from the physical activity pattern in elderly persons. These markers of health will be used to develop algorithms for assessing coordination skill, motoneural control during gait, and aerobic capacity. The models will be implemented in monitoring tools for home healthcare applications, supporting appropriate lifestyle interventions aimed at delaying functional and physiological deterioration related to ageing.
A secondary aim of the project is to apply data mining techniques to detect posture and classify activity types from accelerometer data. Activity recognition will be used to optimally characterize the physical activity pattern for improving energy expenditure assessment. This project is conducted in close collaboration with the Medical Signal Processing Group, Philips Research Laboratories, Eindhoven, The Netherlands.
MSc in technical discipline with interest in Biology, MSc (Bio) Engineering, or MSc in (medical) Biology with feeling for technical sciences/applications.
The terms of employment of Maastricht University are set out in the Collective Labour Agreement of Dutch Universities (CAO). Furthermore, local UM provisions also apply. For more information look at the website http://www.maastrichtuniversity.nl/ , A-Z Terms of Employment.
NUTRIM School for Nutrition, Toxicology and Metabolism initiates and catalyzes translational research into nutritional health benefits and risks focusing on metabolic and chronic inflammatory diseases. Through its research master and PhD program NUTRIM aims to educate scientists of high academic excellence and ambassadors to support and develop the filed of nutrition, metabolism and toxicology within and outside the Netherlands.
15 Biomedical, clinical, and behavioural-science departments are incorporated within NUTRIM. The school participates in the Graduate School VLAG (Food Technology, Agrobiotechnology, Nutrition and Health Sciences), accredited by the Royal Academy of Arts and Sciences (KNAW) and is a partner in the national Top Institutes TI Food, TI-Pharma and the Centre for Translational Molecular Medicine (CTMM).
These unique consortia of government, industry and research aim to stimulate the transfer of knowledge generated in fundamental research to Dutch industry and thus to strengthen its innovative power and competitive strength.
Prof. K. Westerterp, Department of Human Biology, T: +31-43-3881628, firstname.lastname@example.org
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