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Machine Learning for Stress and Fatigue Detection in Ship Crews

  • Full or part time
  • Application Deadline
    Saturday, August 31, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

It is often reported that 80% of maritime accidents are due to human error, with a large proportion of these attributed to a failure to follow procedures. The usual response to this is to increase training. A recent research project (MARTHA) which looked into fatigue and sleepiness onboard ships, concluded that although all crew showed increased levels of fatigue by the end of a voyage, certain crew member (particularly 2nd Officers) were more susceptible to tiredness and that fatigue and stress were inter-related.

The aim of this project will be to measure and predict the levels of fatigue and stress in targeted Deck Officers from modern LNG ships. This will be carried out using a range of both intrusive and non-intrusive methods. Intrusive methods will include modern fitness trackers which can record activity, heart rate and track sleep in an unobtrusive manner. Non-intrusive methods will include environmental sensors built into the ship that allow the monitoring of physical activities and environmental parameters (temperature, humidity etc…). This will be combined with vessel tracking data from AIS and ship motions data and environmental data from wave buoys. Machine learning techniques (e.g., deep learning and Bayesian classifiers) can then be used to determine periods of acute stress and poor sleep leading to fatigue. Based on these outputs, optimisation algorithms will be developed to reduce tiredness and stress levels. Moreover, this information can then be used to develop more realistic training programmes which incorporate appropriate stressors, which could potentially reduce the negative effects of stress/ fatigue as the trainees become habituated to the stressors. The project will also investigate the levels of activity and stress when off watch and when not at sea, in particular the periods before and after a long sea voyage.

The ideal candidate will have a background in Machine learning, Ubiquitous Computing, Artificial Intelligence, and Human Factors or Human-Computer Interaction. Depending on the background of the successful PhD student, suitable training will be provided from specialist modules across Engineering and Health Sciences. In particular Ship Design and Economics and Research Methods for Evidence Based Practice. The student will also be given the opportunity to learn about ship operation and crew training from Shell Shipping. Training in relevant analysis software will also be provided.

The recent Global Marine Technology Trends 2030 document highlighted the increasing technology onboard ships and the need for highly skilled crew to operate them. Skilled crew requires good training programmes, which need to reinforce the correct human behaviours in stressful situations, especially as humans have finite resources in terms of memory and attention. The design of shipboard systems is usually the responsibility of engineers, but the evaluation of these systems needs to be carried out from the human perspective. This needs a multi-disciplinary approach building on the work already carried out between engineering and psychology in the laboratory and incorporating real in-situ measurement based on occupational health practice. The research has the potential to reduce major shipping accidents, saving lives and reducing environmental impact. Currently, a career at sea is not viewed in the same aspirational way as, say, becoming a commercial airline pilot. Yet the physical, mental and emotional requirements are very similar. The need to recruit highly skilled crew for ship operations will require significant development of training that more adequately prepares people for the ships of the future.

Funding Notes

This 3 year studentship covers UK/EU level tuition fees and provides an annual tax-free stipend at the standard EPSRC rate, which is £15,009 for 2019/20.

The funding available is competitive and will only be awarded to an outstanding applicant. As part of the selection process, the strength of the whole application is taken into account, including academic qualifications, personal statement, CV and references.

For further guidance on funding, please contact

References

If you wish to discuss any details of the project informally, please contact Professor Dominic Hudson, Fluid-structure interactions Research Group, Email: [email protected], Tel: +44 (0) 2380 592306.

How good is research at University of Southampton in General Engineering?

FTE Category A staff submitted: 192.23

Research output data provided by the Research Excellence Framework (REF)

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