Adrián Bresó

Modelling Two Emotion Regulation Strategies as Key Features of Therapeutic Empathy

Computational models of affective processes have allowed the construction of synthetic characters able to produce empathic behaviours. The use of empathy, as a strategy to enhance engagement and cooperation with human pairs has proved good results in different application domains. Mental care is a particular area where the use of empathic virtual characters would offer several advantages facilitating the self-treatment management. Empathic responses in counselling and psychotherapy differ from “natural” empathy produced in everyday situations. Therapeutic empathy requires an emotional involvement of the therapist with the patient and an emotional detachment for a more objective appraisal of the situation. This paper introduces a model of emotion regulation as the first steps to get therapeutic empathy responses in a virtual agent constructed to support the treatment of major depression. The modelling of two specific strategies of emotion regulation based on Gross theory (cognitive change and response modulation) is described.

Look on the Bright Side: A Model of Cognitive Change in Virtual Agents

The modelling of empathic reactions in virtual agents has been recognised as a key aspect to improve agent-user relationship. Empathy is particularly important in a virtual agent used in computer-based psychotherapy applications. From a clinical perspective, it is more useful to produce therapeutic-empathy responses in the agent and not only natural empathic reactions as response to patient’s inputs. Based on Gross’s theory of emotion regulation, this paper presents a model of cognitive change used to reappraise with a more positive perspective those negative situations reported by the user and provide an adequate emotional reaction during the interaction. An initial evaluation of the model is also presented and the further work is described.

Actigraphy pattern analysis for outpatient monitoring.

The actigraphy is a cost-effective method for assessing specific sleep disorders such as diagnosing insomnia, circadian rhythm disorders, or excessive sleepiness. Due to recent advances in wireless connectivity and motion activity sensors, the new actigraphy devices allow the non-intrusive and non-stigmatizing monitoring of outpatients for weeks or even months facilitating treatment outcome measure in daily life activities. This possibility has propitiated new studies suggesting the utility of actigraphy to monitor outpatients with mood disorders such as major depression, or patients with dementia. However, the full exploitation of data acquired during the monitoring period requires the use of automatic systems and techniques that allow the reduction of inherent complexity of the data, the extraction of most informative features, and the interpretability and decision-making. In this study we purpose a set of techniques for actigraphy patterns analysis for outpatient monitoring. These techniques include actigraphy signal pre-processing, quantification, nonlinear registration, feature extraction, detection of anomalies, and pattern visualization. In addition, techniques for daily actigraphy signals modelling and simulation are included to facilitate the development and test of new analysis techniques in controlled scenarios.