To achieve seamless interactions, robots have to be capable of reliably detecting affective states in real time. One of the possible states that humans go through while interacting with robots is frustration. Detecting frustration from RGB images can be challenging in some real-world situations; thus, we investigate in this work whether thermal imaging can be used to create a model that is capable of detecting frustration induced by cognitive load and failure. To train our model, we collected a data set from 18 participants experiencing both types of frustration induced by a robot. The model was tested using features from several modalities: thermal, RGB, Electrodermal Activity (EDA), and all three combined. When data from both frustration cases were combined and used as training input, the model reached an accuracy of 89% with just RGB features, 87% using only thermal features, 84% using EDA, and 86% when using all modalities. Furthermore, the highest accuracy for the thermal data was reached using three facial regions of interest: nose, forehead and lower lip.

Affect recognition, or the ability to detect and interpret emotional states, has the potential to be a valuable tool in the field of healthcare. In particular, it can be useful in gamified therapy, which involves using gaming techniques to motivate and keep the engagement of patients in therapeutic activities. This study aims to examine the accuracy of machine learning models using thermal imaging and action unit data for affect classification in a gamified robot therapy scenario. A self-report survey and three machine learning models were used to assess emotions including frustration, boredom, and enjoyment in participants during different phases of the game. The results showed that the multimodal approach with the combination of thermal imaging and action units with LSTM model had the highest accuracy of 77% for emotion classification over a 7-s sliding window, while thermal imaging had the lowest standard deviation among participants. The results suggest that thermal imaging and action units can be effective in detecting affective states and might have the potential to be used in healthcare applications, such as gamified therapy, as a promising non-intrusive method for recognizing internal states.

As AI systems increasingly engage in social interactions, comprehending human social dynamics is crucial. Affect recognition enables systems to respond appropriately to emotional nuances in social situations. However, existing multimodal approaches lack accounting for the social appropriateness of detected emotions within their contexts. This paper presents a novel methodology leveraging sentence embeddings to distinguish socially appropriate and inappropriate interactions for more context-aware AI systems. Our approach measures the semantic distance between facial expression descriptions and predefined reference points. We evaluate our method using a benchmark dataset and a real-world robot deployment in a library, combining GPT-4(V) for expression descriptions and ada-2 for sentence embeddings to detect socially inappropriate interactions. Our results underscore the importance of considering contextual factors for effective social interaction understanding through context-aware affect recognition, contributing to the development of socially intelligent AI capable of interpreting and responding to human affect appropriately.