Advances in Human-Computer Interaction

Recent years have seen a surge in interest in the multifaceted topic of human-computer interaction (HCI). Since the advent of the Fourth Industrial Revolution, the significance of human-computer interaction in the field of safety risk management has only grown. There has not been a lot of focus on developing human-computer interaction for identifying potential hazards in buildings. After conducting a comprehensive literature review, we developed a study framework for the use of human-computer interaction in the identification of construction-related hazards (CHR-HCI). Future studies will focus on the intersection of computer vision, VR, and ergonomics. In this research, we have built a theoretical foundation for past studies’ findings and connections and offered concrete recommendations for the improvement of HCI in danger identification in the future. Moreover, we analyzed two cases studies related to the domain of CHR-HCI in terms of wearable vibration-based systems and context aware navigation.

Many unstructured documents contain segments with specific topics. Extracting these segments and identifying their topics helps to access the required information directly. This can improve the quality of many NLP applications such as information extraction, information retrieval, summarization, and question answering. Resumes (CVs) are unstructured documents that have diverse formats. They contain various segments such as personal information, experience, and education. Manually processing resumes to find the most suitable candidates for a particular job is a difficult task. Due to the increased amount of data, it has become very necessary to manipulate resumes by computer to save time and effort. This research presents a new algorithm named TSHD for topic segmentation based on headings detection. We apply the algorithm to extract resume segments and identify their topics. The proposed TSHD algorithm is accurate and addresses many weaknesses in previous studies. Evaluation results show a very high F1 score (about 96%) and a very low segmentation error (about 2%). The algorithm can be easily adapted to deal with other textual domains that contain headings in their segments.

Chronic pain affects the quality of life of those affected. The need to investigate alternative and complementary methods to the pharmacological one to alleviate chronic pain is evident, so virtual reality and binaural tones have become a topic of interest in this field in recent years. This study aims to analyze the contributions of the combination of these two techniques in pediatric patients with chronic pain. For this, data on psychophysiological responses (heart rate and galvanic skin response) and pain perception are collected during and after interaction with this technology using a mixed pre- and posttest experimental methodology. The physiological data and answers in the Pediatric Pain Questionnaire (PPQ) have been collected in a sample of n = 13 healthy participants and n = 9 pediatric patients with chronic pain. The results show a significant difference between baseline and after applying virtual reality and binaural beats, m_d = 1.205 (t = 3.32; ). There are great effects on the perception of chronic pain if virtual reality and binaural beats are combined, even greater than with virtual reality alone, making this combination of technologies a very useful tool to be exploited for the management of chronic pain in pediatric patients with rheumatic diseases.

Speech emotion recognition (SER) has grown to be one of the most trending research topics in computational linguistics in the last two decades. Speech being the primary communication medium, understanding the emotional state of humans from speech and responding accordingly have made the speech emotion recognition system an essential part of the human-computer interaction (HCI) field. Although there are a few review works carried out for SER, none of them discusses the development of SER system for the Indo-Aryan or Dravidian language families. This paper focuses on some studies carried out for the development of an automatic SER system for Indo-Aryan and Dravidian languages. Besides, it presents a brief study of the prominent databases available for SER experiments. Some remarkable research works on the identification of emotion from the speech signal in the last two decades have also been discussed in this paper.

Nowadays, both text-based and 3D online applications rely on conversational agents and autonomous characters to interact with users. Previous experiments demonstrated that perceived agency, that is to say, one’s belief in interacting with a computer- or another human-controlled entity could impact social interaction. At present, theories and results still diverge and no consensus has been reached. Therefore, we developed an experiment to investigate the effect of perceived agency and emotional communication on social presence. Participants were told that they would play an online image recognition game against a computer- or a human-controlled opponent. In both cases, participants faced a computer-controlled opponent designed to provide a challenging yet balanced competitive experience. Depending on the experimental conditions, participants were able to communicate with their opponents using emoticons between the game rounds. Results demonstrate a significant main effect of emotional communication on the three dimensions of social presence we considered in this experiment. An interaction effect between perceived agency and emotional communication was observed in copresence, another core dimension of social presence. The impact of emotional communication on participants’ sense of copresence depends on the perceived agency of the opponent. A significant increase was observed for participants facing a computer-controlled opponent when emotional communication was allowed. The sense of copresence was even higher when they were facing a computer-controlled opponent rather than a presumed human-controlled one. These results are discussed with regard to theories of social interaction in computer-mediated communication.

The number of children with mental health problems is ever-growing; as a result, nearly 850,000 children in the UK are believed to have clinically significant problems, and only a quarter show evidence of mental illness. Family members often have a hard time dealing with children with mental health problems. As a result, digital mental health interventions are becoming popular for people seeking professional mental health services. Previous studies in this area have also shown that parents who are divorced or working away from home struggle to maintain contact with their children. This lack of communication between the parents and their children can worsen the children’s mental health conditions and prevent early diagnosis. Human-centred design thinking is applied step by step in this paper to provide an intuitive understanding of the design process. Five stages of the design thinking process were examined to follow a correct path. The results were promising, and the feedback received assured that the product helps parents to better monitor their children’s mental health and provides support when needed. The design thinking process was followed in concordance with the user needs identified from previous studies in this area, which led to a working solution that benefits both parents and children in tackling these problems.