Detecting Affective States based on Human Motion Analysis

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via the CDT in Socially Intelligent Artificial Agents


Collaborators: Dr. Marios Philiastides


 

© Deligianni et al., IEEE journal of biomedical and health informatics, 2019.

Human motion analysis is a powerful tool to extract biomarkers for disease progression in neurological conditions, such as Parkinson disease and Alzheimer’s. Gait analysis has also revealed several indices that relate to emotional well-being. For example, increased gait speed, step length and arm swing has been related with positive emotions, whereas a low gait initiation reaction time and flexion of posture has related with negative feelings (Deligianni et al. 2019). Strong neuroscientific evidence show that the reason behind these relationships are due to an interaction between brain networks involved in gait and emotion. Therefore, it does not come to surprise that gait has been also related to mood disorders, such as depression and anxiety.

In this project, we aim to investigate the relationship between effective mental states and psychomotor abilities with relation to gait, balance and posture while emotions are modulated via augmented reality displays. The goal is to develop a comprehensive continuous map of interrelationships in both normal subjects and subjects affected by a mood disorder. In this way, we are going to derive objective measures that would allow to detect early signs of abnormalities and intervene via intelligent social agents. This is a multi-disciplinary project with several challenges to address:

  • Build robust experimental setup of intuitive naturalistic paradigms.

  • Develop AI algorithms to relate neurophysiological data with gait characteristics based on state-of-the-art motion capture systems (taking into account motion artefacts during gait)

  • Develop AI algorithms to improve detection of gait characteristics via rgbd cameras (Gu et al. 2020) and possibly new assistive living technologies based on pulsed laser beam.

The proposed AI technology for social agents has several advantages. It can enable the development of intelligent social agents that would track mental well-being based on objective measures and provide personalised feedback and suggestions. In several cases, assessment is done based on self-reports via mobile apps. These measures of disease progression are subjective and it has been found that in major disorders they do not correlate well with objective evaluations. Furthermore, measurements of gait characteristics are continuous and they can reveal episodes of mood disorders that are not present when the subject visits a health practitioner. This approach might shed a light on subject variability with relation to behavioural therapy and provide more opportunities for earlier intervention (Queirazza et al. 2019). Finally, compared to other state-of-the-art effect recognition approaches, human motion analysis might pose less privacy issues and enhance users’ trust and comfort with the technology. In several situations, where facial expressions are not easy to track, human motion analysis is far more accurate in classifying subjects with mental disorders.

Related Publications

  • F Deligianni, Y Guo, GZ Yang, ‘From Emotions to Mood Disorders: A Survey on Gait Analysis Methodology’, IEEE journal of biomedical and health informatics, 2019.

  • Y Guo, F Deligianni, X Gu, GZ Yang, ‘3-D Canonical pose estimation and abnormal gait recognition with a single RGB-D camera’, IEEE Robotics and Automation Letters, 2019.

  • X Gu, Y Guo, F Deligianni, GZ Yang, ‘Coupled Real-Synthetic Domain Adaptation for Real-World Deep Depth Enhancement.’, IEEE Transactions on Image Processing, 2020.

  • F Queirazza, E Fouragnan, JD Steele, J Cavanagh and MG Philiastides, Neural correlates of weighted reward prediction error during reinforcement learning classify response to Cognitive Behavioural Therapy in depression, Science Advances, 5 (7), 2019.