This paper connects two large research areas, namely sentiment analysis and human–robot interaction. Emotion analysis, as a subfield of sentiment analysis, explores text data and, based on the characteristics of the text and generally known emotional models, evaluates what emotion is presented in it. The analysis of emotions in the human–robot interaction aims to evaluate the emotional state of the human being and on this basis to decide how the robot should adapt its behavior to the human being. There are several approaches and algorithms to detect emotions in the text data. We decided to apply a combined method of dictionary approach with machine learning algorithms. As a result of the ambiguity and subjectivity of labeling emotions, it was possible to assign more than one emotion to a sentence; thus, we were dealing with a multi-label problem. Based on the overview of the problem, we performed experiments with the Naive Bayes, Support Vector Machine and Neural Network classifiers. Results obtained from classification were subsequently used in human–robot experiments. Despise the lower accuracy of emotion classification, we proved the importance of expressing emotion gestures based on the words we speak.

Lung ultrasound is used to detect various artifacts in the lungs that support the diagnosis of different conditions. There is ongoing research to support the automatic detection of such artifacts using machine learning. We propose a solution that uses analytical computer vision methods to detect two types of lung artifacts, namely A- and B-lines. We evaluate the proposed approach on the POCUS dataset and data acquired from a hospital. We show that by using the Fourier transform, we can analyze lung ultrasound images in real-time and classify videos with an accuracy above 70%. We also evaluate the method’s applicability for segmentation, showcasing its high success rate for B-lines (89% accuracy) and its shortcomings for A-line detection. We then propose a hybrid solution that uses a combination of neural networks and analytical methods to increase accuracy in horizontal line detection, emphasizing the pleura.

In real-life medical applications, there is often no curated data available for training machine learning solutions. In this article, we explore the efficacy of various ways of addressing this issue, such as extending the training set using historical data, data transformation, and data augmentation. We conducted experiments on lung ultrasound data for A-line and B-line artifact detection. The dataset used was acquired from real-life examinations done in a hospital where physicians labeled approximately every tenth frame, resulting in sparsely labeled data. We expanded this dataset using historical ultrasound scans from various probe types and evaluated two transformation strategies—standard resizing and edge padding—alongside several augmentation methods. The biggest improvement in F1-score was from 0.69 to 0.86 for A-line detection, and from 0.53 to 0.8 for B-line detection. These results prove the utility of carefully chosen data transformation and augmentation when training on sparsely labeled data.