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    Multimodal affect analysis of psychodynamic play therapy
    (Routledge Journals, Taylor & Francis Ltd, 2021) Halfon, Sibel; Doyran, Metehan; Turkmen, Batikan; Oktay, Eda Aydin; Salah, Ali Albert
    Objective: We explore state of the art machine learning based tools for automatic facial and linguistic affect analysis to allow easier, faster, and more precise quantification and annotation of children's verbal and non-verbal affective expressions in psychodynamic child psychotherapy. Method: The sample included 53 Turkish children: 41 with internalizing, externalizing and comorbid problems; 12 in the non-clinical range. We collected audio and video recordings of 148 sessions, which were manually transcribed. Independent raters coded children's expressions of pleasure, anger, sadness and anxiety using the Children's Play Therapy Instrument (CPTI). Automatic facial and linguistic affect analysis modalities were adapted, developed, and combined in a system that predicts affect. Statistical regression methods (linear and polynomial regression) and machine learning techniques (deep learning, support vector regression and extreme learning machine) were used for predicting CPTI affect dimensions. Results: Experimental results show significant associations between automated affect predictions and CPTI affect dimensions with small to medium effect sizes. Fusion of facial and linguistic features work best for pleasure predictions; however, for other affect predictions linguistic analyses outperform facial analyses. External validity analyses partially support anger and pleasure predictions. Discussion: The system enables retrieving affective expressions of children, but needs improvement for precision.
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    Video and Text-Based Affect Analysis of Children in Play Therapy
    (Assoc Computing Machinery, 2019) Doyran, Metehan; Turkmen, Batikan; Oktay, Eda Aydin; Halfon, Sibel; Salah, Albert Ali
    Play therapy is an approach to psychotherapy where a child is engaging in play activities. Because of the strong affective component of play, it provides a natural setting to analyze feelings and coping strategies of the child. In this paper, we investigate an approach to track the affective state of a child during a play therapy session. We assume a simple, camera-based sensor setup, and describe the challenges of this application scenario. We use fine-tuned off-the-shelf deep convolutional neural networks for the processing of the child's face during sessions to automatically extract valence and arousal dimensions of affect, as well as basic emotional expressions. We further investigate text-based and body-movement based affect analysis. We evaluate these modalities separately and in conjunction with play therapy videos in natural sessions, discussing the results of such analysis and how it aligns with the professional clinicians' assessments.