Unlocking Emotions: Multimodal Sensing for Context-Aware Affective Computing

Summary

The rapid development of sensor-rich, pervasive environments alongside personal wearables provides exciting opportunities to support individuals who have difficulty regulating their emotions.

This research will leverage machine learning to monitor and interpret emotion, focusing on stress and anxiety, employing a combination of behavioural, physiological, environmental, and contextual data. The research is set within a wider agenda to develop technologies that seek to empower neurodiverse individuals to recognise and self-regulate emotions, thus promoting better health outcomes [1].

Utilising machine learning techniques such as fusion, ensemble and transfer learning, the project will research robust machine learning models for interpreting emotions within contextually complex environments. To support early-stage model training and validation, open-source datasets containing real multimodal sensor data have been identified, namely: WESAD (Wearable Stress and Affect Detection), SWELL-KW (Stress and Workload in an Office Environment), DEAP (Database for Emotion Analysis using Physiological Signals).

The objectives will investigate three core challenges set out in a recent review on Multimodal Emotion Recognition [2]:

(1) feature engineering of high dimensional data to identify key stress indicators which would allow for in real-time intervention.
(2) evaluating ensemble approach for multimodal emotion recognition using sensor data fusion.
(3) investigating context integration, to consider personalisation and granularity of available sensing modalities.

An additional objective may seek to demonstrate the research achievements by moving beyond the lab setting to deploy the findings within an off-the-shelf wearable device.

The School of Computing at Ulster University  holds Athena Swan Bronze Award since 2016 and is committed to promote and advance gender equality in Higher Education. We particularly welcome female applicants, as they are under-represented within the School.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Sound understanding of subject area as evidenced by a comprehensive research proposal
• A demonstrable interest in the research area associated with the studentship

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 70%
• For VCRS Awards, Masters at 75%
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed
• Experience of presentation of research findings

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

This project is funded by:

• Department for the Economy (DfE)
• Vice Chancellor's Research Scholarship (VCRS)

Our fully funded PhD scholarships will cover tuition fees and provide a maintenance allowance of £19,237 (tbc) per annum for three years (subject to satisfactory academic performance).  A Research Training Support Grant (RTSG) of £900 per annum is also available.

These scholarships, funded via the Department for the Economy (DfE) and the Vice Chancellor’s Research Scholarships (VCRS), are open to applicants worldwide, regardless of residency or domicile.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies.