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Developing the DANU Analytics Platform for Sports Performance (Ref: RDFC20/HLS/SER/BARRY)

  • Full or part time
  • Application Deadline
    Friday, June 19, 2020
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

Gait is a common behaviour undertaken by humans within a variety of contexts and environments. Gait is also a biomarker for health and can predict injuries or illness progression. Within research settings gait is typically examined in laboratories with expensive large-scale devices, such as 3D-motion capture. Recently, progress to modern low-cost wearable devices, such as inertial measurement units or pressure sensors, have allowed gait examination within real-world environments and activities. Data derived from wearable devices, such as the DANU Analytics wearable system (https://www.danusport.com/), can provide useful information regarding performance and injuries, and inform more tailored rehabilitation or performance progression strategies. However, automated analysis of motion signal data and validation of devices or algorithms compared to laboratory references remains challenging.
This project aims to develop and evaluate novel gait analysis methods using a wearable device (DANU Analytics), which will be applied to various populations (healthy, sports, clinical).

The PhD candidate will benefit from multidisciplinary supervision by Dr Samuel Stuart (physiotherapist), Dr Alan Godfrey (engineer) and Dr Gillian Barry (biomechanist). This is a collaborative project between an industry partner, specifically Kinetika Limited (trading as DANU Analytics), and Northumbria University, therefore the candidate will benefit from industry experience as well as academic research.

The PhD candidate will;
● Coordinate with all partners for development of the DANU Analytics system
● Design and organise data collection in collaboration with sports staff and healthcare professionals
● Develop and evaluate novel gait (walking and running) data analysis algorithms, involving techniques such as AI, feature extraction or machine learning
● Validate algorithms using laboratory references
● Investigate the ability for DANU Analytics system to predict injuries and inform performance enhancement

This project is relevant for an engineering, computer science or biomechanics graduate with experience of coding/algorithm development and an interest in sports or health technology development.

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.

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. RDFC20/…) will not be considered.
Deadline for applications: Friday 19th June 2020
Start Date: 1st October 2020

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 holds an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality.

Informal enquiries are invited - please contact Dr Samuel Stuart () or Dr Gill Barry ()

Funding Notes

The studentship is available to Home and EU students where a full stipend, paid for three years at RCUK rates (for 2020/21, this is £15,287 pa) and full Home/ EU Fees.

References

Recent publications by supervisors relevant to this project
1. Stuart, S., Parrington, L., Morris, R., Martini, D., Chesnutt, J., Fino, P. and King, L., ‘Gait Measurement in Chronic Mild Traumatic Brain Injury: A Model Approach’, Human Movement Science.
2. Morris, R., Stuart S., McBarron, G., Fino, P., Mancini, M. and Curtze, C. (2019) ‘ Validation of MobilityLab (version 2) for gait assessment in young adults, older adults and Parkinson’s disease’, Physiological Measurement.
3. Stuart, S., Johnston, W., Caulfield, B. and Godfrey, A. (2019) ‘Focus on Modern Approaches to Sports Medicine and Performance’, Physiological Measurement.
4. Stuart, S., Parrington, L., Martini, D., Kreter, N., Chesnutt, J., Fino, P. and King, L., (2019) ‘Analysis of free-living mobility in mild traumatic brain injury and healthy controls: Quality over Quantity’, Journal of Neurotrauma.
5. Godfrey, A., Hetherington, V., Shum, H., Bonato, P., Lovell, N. and Stuart, S. (2018) ‘From A to Z: Wearable technology explained’, Maturitas, 113: 40-47.
6. Del Din, S., Hickey, A., Ladha, C., Stuart, S., Bourke, A.K., Esser, P., Rochester, L. and Godfrey, A. (2016) ‘Instrumented gait assessment with a single wearable: an introductory tutorial’, F1000Research, 5:2323.
7. Stuart S, Morris R, Hickey A, O’Donovan K, Godfrey A. Concussion in contact sport: a challenging area to tackle. Journal of Sport and Health Science. 2017. 6:3. 299-301.
8. 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.
9. Barry G, Galna B, Lord S, Rochester L and Godfrey A. (2015) Defining ambulatory bouts in free-living activity: Impact of brief stationary periods on bout metrics. Gait and Posture 42(4):594-7.

10. Stoneham, R., Barry, G., Saxby, L., Wilkinson, M. (2019). Measurement error of 3-D kinematic and kinetic measures during overground endurance running in recreational runners between two test sessions separated by 48 hours. In: Physiological Measurement

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