As society moves towards a preventative approach to healthcare, there is growing interest in scientific research involving technology that can monitor and prevent adverse health outcomes. The primary objective of this paper is to develop an Internet of Things (IoT) wearable system based on Fried’s phenotype that is capable of detecting frailty. To determine user requirements, the system’s architecture was designed based on the findings of a questionnaire administered to individuals confirmed to be frail. A functional prototype was successfully developed and tested under real-world conditions. This paper introduces the methodology that was used to analyze the data collected from the prototype. It proposes an interdisciplinary approach to interpret wearable sensor data, providing a comprehensive overview through both visual representations and computational analyses facilitated by machine learning models. The findings of these analyses offer insights into the ways in which different types of activities can be classified and quantified as part of an overall physical activity level, which is recognized as an important indicator of frailty. The results provide the foundations for a new generation of affordable and non-intrusive systems able to detect and assess early signs of frailty.

In this paper, we describe an actuator-based EMDR (eye movement desensitization and reprocessing) virtual assistant system that can be used for the treatment of participants with traumatic memories. EMDR is a psychological therapy designed to treat emotional distress caused by a traumatic event from the past, most frequently in post-traumatic stress disorder treatment. We implemented a system based on video, tactile, and audio actuators which includes an artificial intelligence chatbot, making the system capable of acting autonomously. We tested the system on a sample of 31 participants. Our results showed the efficiency of the EMDR virtual assistant system in reducing anxiety, distress, and negative cognitions and emotions associated with the traumatic memory. There are no such systems reported in the existing literature. Through the present research, we fill this gap by describing a system that can be used by patients with traumatic memories.

This study compares immersive VR-based control systems with conventional keyboard-based control to examine the efficacy of VR interfaces for controlling robotic arms in Internet of Things (IoT) education. A 5-DOF robotic arm with MG996R servomotors and controlled by an Arduino microcontroller and Raspberry Pi wireless communication was operated by 31 third-year engineering students in hands-on experiments using both control modalities. To determine student preferences across in-person, online, and hybrid learning contexts, the study applied a mixed-methods approach that combined qualitative evaluation using open-ended questionnaires and quantitative analysis through Likert-scale surveys. First, it should be mentioned that most of the reported papers either use a robotic arm or a VR system in education. However, we are among the first to report a combination of the two. Secondly, in most cases, there are either technical papers or educational quantitative/qualitative research papers on existing technologies reported in the literature. We combine an innovative education context (robotic arm and VR), completed with a quantitative and qualitative study, making it a complete experiment. Lastly, combining qualitative with quantitative research that complement each other is an innovative aspect in itself in this field.

Introduction: Depression is one of the most common mental disorders and one that has a great potential to affect people mentally, physically, and socially. Unfortunately, a majority of people either do not have access to treatment or avoid seeking help. In this context, many platforms have emerged to provide a space for discussion and support where users can interact anonymously. Methods: This study presents the results of our research on classifying depression in a specific community of religious people by analyzing texts posted on social media using semantic techniques, such as a comparative analysis of texts from users using ontologies. A supporting objective of the research was to create a natural language processing tool for classifying depression to obtain the corpus necessary for ontology creation. Results: The resulting ontologies were analyzed and compared with each other and also with existing ontologies in the literature on general depression. Discussion: The comparison was both qualitative and quantitative, taking into consideration similarity ratios for the quantitative comparison.