Wearable technologies facilitate robust quantification of human activities with digital integration to IT infrastructures making them a viable and pragmatic tool for modern patient assessment. Wearables are discreet to be worn directly on the skin for continuous monitoring in most environments. However, large-scale and longitudinal deployment of wearables remains impractical due to limitations in hardware capabilities and the dearth of real-time event detection algorithms. Currently, data must be post-processed by complex techniques leading to delays in patient assessment/diagnosis, poor energy efficiency, inept data analytics and an accumulation of superfluous data, which: (a) limits computational resources and (b) overwhelms healthcare professionals.
Therefore, project aims are to: (i) implement real-time detection of human activities with wearables, (ii) deploy algorithms and associated wearables during field-testing and (iii) integrate newly developed methodologies into ongoing clinical studies.
The student will undertake a detailed examination of the topic, aiming to publish a literature review within a leading journal. The student will develop an extensive knowledge of algorithm development for use in real-time through open source tools, incorporating deep learning methodologies. The student will work across disciplines, using collaborators within Health Sciences at Northumbria and further afield. This presents an exciting opportunity to work across diverse testing environments, conducting clinically orientated studies including the submission of ethical documentation to experience multidisciplinary research.
The student will be encouraged to develop this project and their professional development by publishing at alternating computer science, engineering and clinical conferences in addition to high impact IEEE and clinical journals. This project will aim to display the departments computing science abilities and translational expertise to the medical sciences.
The student will be supervised by Dr Alan Godfrey (Senior Research Fellow). The breath of expertise developed will mirror the multidisciplinary work by the lead supervisor, https://scholar.google.co.uk/citations?user=AUptNgUAAAAJ&hl=en
For more information and informal enquiries please contact Dr Alan Godfrey [email protected]
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]) in computer science or electronic engineering; or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
* Appropriate IELTS score, if required
This project is well suited to motivated and hard-working candidates with a keen interest in algorithm development, signal processing and wearable technology for healthcare applications. The applicant should have excellent communication skills including proven ability to write in English.
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. SF18/CIS/GODFREY) will not be considered.
Start Date: 1 March 2019 or 1 June 2019 or 1 October 2019
Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University hold an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality and is a member of the Euraxess network, which delivers information and support to professional researchers.
• Godfrey A, Hetherington V, Shum H, Bonato P, Lovell N, Stuart S. From A to Z: Wearable technology explained. Maturitas. 2017. 113, 40-47.
• Hickey A, Stuart S, O'Donovan K, Godfrey A. Walk on the wild side: the complexity of free-living mobility assessment. Journal of Epidemiology and Community Health 2017, Epub ahead of print.
• Godfrey A. Wearables for independent living in older adults: Gait and falls. Maturitas 2017, 100, 16-26.
• Hickey A, Del Din S, Rochester L, Godfrey A. Detecting free-living steps and walking bouts: validating an algorithm for macro gait analysis. Physiological Measurement 2017, 38(1), N1-N15.
• Godfrey A, Morris R, Hickey A, Del Din S. Beyond the front end: investigating a thigh worn accelerometer device for step count and bout detection in Parkinson's disease. Medical Engineering & Physics 2016, 38(12), 1524–1529.
• Del Din S, Godfrey A, Mazzà C, Lord S, Rochester L. Free‐living monitoring of Parkinson's disease: Lessons from the field. Movement Disorders, 2016. 31(9), 1293-1313.
• Del Din S, Hickey A, Hurwitz N, Mathers JC, Rochester L, Godfrey A. Measuring gait with an accelerometer-based wearable: influence of device location, testing protocol and age. Physiological Measurement 2016, 37(10), 1785-1797.
• Ladha A, Del Din S, Nazarpour K, Hickey A, Morris R, Catt M, Rochester L, Godfrey A. Toward a low-cost gait analysis system for clinical and free-living assessment. IEEE Engineering and Medicine Biology Society, Orlando. 2016
• Godfrey A, Bourke A, Del Din S, Morris, R, Hickey A, Helbostad J, Rochester L. Towards holistic free-living assessment in Parkinson's disease: unification of gait and fall algorithms with a single accelerometer. IEEE Engineering and Medicine Biology Society, Orlando. 2016
• Barry G, Galna B, Lord S, Rochester L, Godfrey A. Defining ambulatory bouts in free-living activity: Impact of brief stationary periods on bout metrics. Gait & Posture 2015, 42(4), 594-597.
• Godfrey A, Del Din S, Barry G, Mathers JC, Rochester L. Instrumenting gait with an accelerometer: a system and algorithm examination. Medical Engineering & Physics 2015, 37(4), 400-407