The EuroCC team has collaborated with the Department of Psychology and Neuroscience at the University of Cyprus (UCY), to develop the first multimodal machine learning models for the prediction of Alexithymia in literature.
In more detail, Alexithymia is a trait that reflects a person’s difficulty in recognizing and expressing their emotions, which has been associated with various forms of mental illness. Identifying alexithymia can have therapeutic, preventive, and diagnostic benefits. However, there has been limited research on proposing predictive models for alexithymia, and literature on multimodal approaches is almost non-existent. Using machine learning tools can greatly enhance both
Leveraging the expertise of UCY on this topic, and the wealth of multimodal data captured at the UCY laboratory (physiological signals, such as heart rate, skin conductance, facial electromyograms, and audio signals), the team developed fast and efficient techniques based on a set of discriminative temporal features, that capture spectral information in a localized manner (e.g., via wavelet transformations). In this way, simple Machine Learning classifiers achieve up to 95.7% F1-score – even when using data from only one of the 12 phases of the data collection experiment.
The resulting models are lightweight, and can potentially be used in embedded devices for personalized monitoring. At the same time, insights stemming from the experiments can help speed up the data collection experiments carried out in the Department of Psychology, by focusing on phases of the experiment that appear to be highly discriminative for Alexithymia – reducing the effort required to collect the data.
Finally, features were extracted from two novel datasets collected at the UCY, and the team is making steps towards making these data publicly available to speed-up research in this domain.
- Asst Prof M. Nicolaou (CyI)
Technical Team CyI
- Dr V Filippou (lead)
- Mr N Theodosiou
- Prof G. Panayiotou (UCY)
UCY Technical Team
- Dr E. Constantinou
- Dr M. Theodorou
– Valeria Filippou, Nikolas Theodosiou, Mihalis Nicolaou, Elena Constantinou, Georgia Panayiotou, and Marios Theodorou. 2022. A Wavelet-based Approach for Multimodal Prediction of Alexithymia from Physiological Signals.
In Companion Publication of the 2022 International Conference on Multimodal Interaction (ICMI ’22). Association for Computing Machinery, New York, NY, USA, 177–184.