About the Project
Novel AI techniques for fusing information from multiple sensor modalities, to timely detect/predict falls will be developed in this project, for minimizing the adverse effects of falling -on vulnerable people. Related techniques which will be developed in this project will contribute to falls prevention and the reduction of associated healthcare costs of falls, such as fractured neck of femur and prolonged hospitalisation. Moreover, it is anticipated that the techniques developed in this project will contribute to the development of novel healthcare support systems for detecting/predicting/preventing falls, as well as optimise independent living for older adults and other related stakeholders by reducing their risks of falling in their independent living.
Falls and fractures are a common and a serious health issue faced by older people in England and globally. Around a third of people aged 65 and over, and around half of people aged 80 and over, fall at least once a year. Falling is a cause of distress, pain, injury, loss of confidence, loss of independence and mortality. Detecting/predicting the occurrence of falls and reducing falls and associated fractures are important for maintaining the health, wellbeing and independence of older people and other related stakeholders in the health and social care sectors.
Considering the importance of detecting/predicting falls and for minimizing the adverse effects of falling on vulnerable people, this project aims to detect and predict the risk/likelihood of falls based on multimodal sensor modalities (vision sensor and wearable sensor). The candidate is expected to develop novel signal processing, machine learning and deep learning algorithms, to optimally extract and fuse information from vision and wearable sensors’ recordings of a person’s movements, for detecting possible falls as well as predicting the fall risks associated with some normal daily activities (such as walking or sitting). Novel automatic fall detection/prediction techniques based on multimodal sensors are expected to be developed at the end of the project.
The successful candidate will be given the opportunity to work across disciplines and engage with both experts across different Schools in the University of Lincoln and experienced physiotherapists in the NHS and private sector. Moreover, the successful candidate also has internship opportunities in prestigious AI healthcare start-ups specialised in wearable and vision sensors for healthcare.
Skills the candidate will learn:
Skills and experience you can gain are expected to include:
- opportunities to develop expertise in machine learning and deep learning and their applications in healthcare
- quantitative research skills and applied statistics
- critical thinking and problem-solving abilities
- applied data science/machine learning/deep learning
- develop strong verbal and written communication skills
- internship opportunities in Vitrue Health (https://www.vitruehealth.com/) and Activinsights (www.activinsights.com)
- opportunities to present research outputs at national and international venues.
Interested applicants should hold, at a minimum, a 2.1 degree in a relevant or related discipline and are encouraged to demonstrate any skills and/or experience relevant to the project subject area(s) of interest. They must evidence an ability to engage in scientific research and to work collaboratively as part of a team. Excellent communication skills in written and spoken English are also required.
Who is eligible for funding?
Please make sure to check the eligibility criteria before you apply. Normally, a student must have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship. UK students will be eligible for a full studentship, covering the costs of Home fees, and a stipend to support living costs for 3.5 years.
Although most DTP students must be UK residents, we also have an opportunity for an international (EU and non-EU) student. The international studentship award will be subject to eligibility, and also the availability of complementary funding (to provide the differential to the international fee rate). You should get in touch with the lead supervisor before applying this award.
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