Figure 1: General procedure of the Kids-SIT research tool.A child participant follows a fully automated conversation scenario with pre-recorded videos of an actress.The actress initiates a dialogue on meal preferences, talking about her and asking the participant on their favorite and disliked foods one after the other.The actress empathically waits for responses and automatically continues the conversation in the following.For the whole time, the participant behavior is video recorded through the front camera.

Autism Spectrum Condition (ASC) is characterized by social interaction difficulties that can be challenging to assess objectively in the diagnostic process. In this paper, we evaluate the capability of using videos of a standardized social interaction to differentiate non-verbal behaviors of individuals with and without ASC. We collected a large video dataset consisting of 164 participants with ASC (n = 83) and neurotypical individuals (n = 81) who completed the computer-based Simulated Interaction Task (SIT) in different studies including lab and home settings. To classify individuals with and without ASC, we trained uni-and multimodal machine learning models based on different modalities such as facial expressions, gaze behavior, head pose and voice features. Our results indicate that a multimodal late fusion approach achieved the highest accuracy (74%). In the unimodal setting, classification based on facial expressions (accuracy 73%) and voice features (accuracy 70%) were most effective. An explainability analysis of the most relevant features for the facial expression model indicated that features from all emotional parts as well as from both the speaking and listening part of the interaction were informative. Based on our results, we developed a scalable online version of the SIT to collect diverse data on a large scale for the development of machine learning models that can differentiate between different clinical conditions. Our study highlights the potential of machine learning on videos of standardized social interactions in supporting clinical diagnosis and the objective and effective measurement of differences in social interaction behavior.

The usage of social robots in psychotherapy has gained interest in various applications. In the context of therapy for children with socio-emotional impairments, for example autism spectrum conditions, the first approaches have already been successfully evaluated in research. In this context, the robot can be seen as a tool for therapists to foster interaction with the children. To ensure a successful integration of social robots into therapy sessions, an intuitive and comprehensive interface for the therapist is needed to guarantee save and appropriate human-robot interaction. This publication addresses the development of a graphical user interface for robot-assisted therapy to train socio-emotional skills in children on the autism spectrum. The software follows a generic and modular approach. Furthermore, a robotic middleware is used to control the robot and the user interface is based on a local web application. During therapy sessions, the therapist interface is used to control the robot’s reactions and provides additional information from emotion and arousal recognition software. The approach is implemented with the humanoid robot Pepper (Softbank Robotics). A pilot study is carried out with four experts from a child and youth psychiatry to evaluate the feasibility and user experience of the therapist interface. In sum, the user experience and usefulness can be rated positively.

The aim of the mobile app "Zirkus Empathico" is to strengthen socio-emotional competences in pre-and primary school children. It's holistic and natural training concept is based on current results of empathy research. Pilot testing of the app revealed it's good usability and comprehensibility. The effectiveness of "Zirkus Empathico" is currently investigated in a longitudinal clinical study with children aged 5 to 10.