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AI assistive tools to predict mental wellbeing within care homes

   UKRI Centre for Doctoral Training in Socially Intelligent Artificial Agents

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  Mr Jared de Bruin  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

For instructions on how to apply, please see: PhD Studentships: UKRI Centre for Doctoral Training in Socially Intelligent Artificial Agents.


  • Marwa Mahmoud: School of Computing Science
  • Emily Cross: School of Psychology

Cultivating and maintaining mental health is a significant challenge for many residents in care homes. Depression and loneliness, self isolation and low levels of life satisfaction are common among the elderly, who are also more likely to suffer from other physical health problems compared to community-dwelling elderly citizens. Many care homes do not (and can not) provide one-on-one, person-centred care due to lack of resources. This project aims to build data-driven AI models using multimodal machine learning to create a comprehensive picture of care home residents’ mental health. The aim will be to use these models to predict the emergence of potential mental health problems as early as possible, based on analysing multimodal data (audio, video, wearables) collected from care homes.

Main objectives and novelty.

There has been an increased interest in automatic detection of mental health problems over the past several years, mainly focussing on (1) signals collected from mobile phones and wearables; and (2) younger, tech-savvy populations. However, audio-visual signals provide a vast array of extra cues that can improve inference models (Lin et al. 2021, Zhang et al. 2020), but which are currently underused.

The main aims of this project are to:

  1. Build a dataset of structured interviews to be collected at care homes using multimodal sensors (audio/video/wearable sensors).
  2. Devise novel machine learning models that extend state-of-the-art methods to analyse and use the multimodal signal collected to predict mental health conditions.
  3. Validate and evaluate the accuracy of these models via quantitative and qualitative measures of loneliness, depression and anxiety among aged care residents, as well as build a clear picture of aged care residents’ feelings and personal experience with this technology through qualitative interview methods.


This project will use experimental methods on collecting, validating and evaluating multimodal data related to mental health (Lin et. al. 2021, Laban et. al. 2021,2022). Using the collected data, it will also build on and extend state-of-the-art approaches on multimodal data representation and feature selection to devise inference models to predict and correlate with mental health conditions and risks identified within care homes.

Likely outcome and impact.

Nearly a half million UK residents currently live in care homes, representing 4% of the population older than 65, and 15% of those aged 85 and over. The onset of the global coronavirus pandemic, and resulting restrictions on face-to-face meetings with friends, family and loved ones has highlighted how fragile human mental health is when faced with even short-term restrictions to socialising, and these effects have been experienced even more acutely by older individuals living in live-in care settings (Cross and Henschel 2020) . If we are able to achieve our goals with this project, we will be able to develop tools to assist residents as well as care home staff to identify when individuals are at risk of deteriorating mental health and/or in need of extra 1:1 care or companionship from staff.


[1] Lin, W., Orton, I.,Li, Q., Pavarini, G. and Mahmoud, M. (2021). Looking At The Body: Automatic Analysis of Body Gestures and Self-Adaptors in Psychological Distress. IEEE Transaction on Affective Computing.
[2] Zhang, Z., Lin, W., Liu, M. and Mahmoud, M. (2020). Multimodal Deep Learning Framework for Mental Disorder Recognition. IEEE International Conference on Automatic Face and Gesture Recognition.
[3] Laban, G., Ben-Zion, Z. & Cross, E. S. (2022). Social robots for supporting post-traumatic stress disorder diagnosis and treatment. Frontiers in Psychiatry (in press).
[4] Laban, G., George, J.-N., Morrison, V. & Cross, E. S. (2021). Tell me more! Assessing interactions with social robots from speech. Paladyn: Journal of Behavioral Robotics, 12(1), 136-159.
[5] Cross, E. and Henschel, A. (2020) The neuroscience of loneliness – and how technology is helping us.
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