Human activity detection within smart homes is one of the basis of unobtrusive wellness monitoring of a rapidly aging population in developed countries. Most works in this area use the concept of “activity” as the building block with which to construct applications such as healthcare monitoring or ambient assisted living. The process of identifying a specific activity encompasses the selection of the appropriate set of sensors, the correct preprocessing of their provided raw data and the learning/reasoning using this information. If the selection of the sensors and the data processing methods are wrongly performed, the whole activity detection process may fail, leading to the consequent failure of the whole application. Related to this, the main contributions of this review are the following: first, we propose a classification of the main activities considered in smart home scenarios which are targeted to older people’s independent living, as well as their characterization and formalized context representation; second, we perform a classification of sensors and data processing methods that are suitable for the detection of the aforementioned activities. Our aim is to help researchers and developers in these lower-level technical aspects that are nevertheless fundamental for the success of the complete application.

This study explores the integration of large language models (LLMs), like ChatGPT, to improve attention deficit hyperactivity disorder (ADHD) treatments. Utilizing the Delphi method for its systematic forecasting capabilities, we gathered a panel of child ADHD therapy experts. These experts interacted with our custom ChatGPT through a specialized interface, thus engaging in simulated therapy scenarios with behavioral prompts and commands. Using empirical tests and expert feedback, we aimed to rigorously evaluate ChatGPT’s effectiveness in therapy settings to integrate AI into healthcare responsibly. We sought to ensure that AI contributes positively and ethically to therapy and patient care, thus filling a gap in ADHD treatment methods. Findings show ChatGPT’s empathy, adaptability, and communication strengths, thereby highlighting its potential to significantly improve ADHD care. The study points to ChatGPT’s capacity to transform therapy practices through personalized and responsive patient care. However, it also notes the need for enhancements in privacy, cultural sensitivity, and interpreting nonverbal cues for ChatGPT’s effective healthcare integration. Our research advocates for merging technological innovation with a comprehensive understanding of patient needs and ethical considerations, thereby aiming to pioneer a new era of AI-assisted therapy. We emphasize the ongoing refinement of AI tools like ChatGPT to meet ADHD therapy and patient care requirements more effectively.

In recent years, many efforts have been made to promote a healthcare paradigm shift from the traditional reactive hospital-centered healthcare approach towards a proactive, patient-oriented, and self-managed approach that could improve service quality and help reduce costs while contributing to sustainability. Managing and caring for patients with chronic diseases accounts over 75% of healthcare costs in developed countries. One of the most resource demanding diseases is chronic kidney disease (CKD), which often leads to a gradual and irreparable loss of renal function, with up to 12% of the population showing signs of different stages of this disease. Peritoneal dialysis and home haemodialysis are life-saving home-based renal replacement treatments that, compared to conventional in-center hemodialysis, provide similar long-term patient survival, less restrictions of life-style, such as a more flexible diet, and better flexibility in terms of treatment options and locations. Bioimpedance has been largely used clinically for decades in nutrition for assessing body fluid distributions. Moreover, bioimpedance methods are used to assess the overhydratation state of CKD patients, allowing clinicians to estimate the amount of fluid that should be removed by ultrafiltration. In this work, the initial validation of a handheld bioimpedance system for the assessment of body fluid status that could be used to assist the patient in home-based CKD treatments is presented. The body fluid monitoring system comprises a custom-made handheld tetrapolar bioimpedance spectrometer and a textile-based electrode garment for total body fluid assessment. The system performance was evaluated against the same measurements acquired using a commercial bioimpedance spectrometer for medical use on several voluntary subjects. The analysis of the measurement results and the comparison of the fluid estimations indicated that both devices are equivalent from a measurement performance perspective, allowing for its use on ubiquitous e-healthcare dialysis solutions.

Robotics, Artificial Intelligence (AI), and the Internet of Things (IoT) support various processes in many scenarios of modern life such as e-health and psychological treatments. This article presents the design, development, implementation, and assessment of a Robotic Assistant (RA), named “Atent@”, as a support tool in the homework activities of children with Attention Deficit Hyperactivity Disorder (ADHD). Interacting with the children the RA helps them correct their bad habits and misbehavior caused by the disorder. Its features and functionalities were designed by therapists, implementing AI algorithms to process information and make decisions in real-time to help children to be focused on their homework. This RA interacts with smart objects deployed at home, which are associated with the activity under observation (desk and chair). This solution allows therapists to receive more accurate information about the homework sessions inside the home. At the same time, remote interaction with the child is made possible (through the RA) to provide new instructions and support him/her along with the sessions. This RA is a significant evolution of an earlier version. All the improvements brought to the project by the modifications in technical and qualitative features are explained. Furthermore, the experiment and its results are presented to illustrate the clinical potential. This project shows that the RA can not only make observations with a high degree of precision like an expert (teacher/therapist) but also positively influences the homework performance of children with and without ADHD.

Human activity detection within smart home (SH) is one of the basis of unobtrusive wellness monitoring of a rapidly aging population in developed countries. Most works in this area use the concept of “activity” as the building block with which to con

The Central Nervous System of humans continuously evolves when children engage in new activities. These activities are progressing from learning to eat as a baby over playing during childhood up to homework in all its dimensions. These activities, which are meaningful and relevant to everyone, constitute what occupational therapists call “occupations”. The successful execution of these occupations makes the development of new roles as well as the construction of a correct state of health possible. Due to these reasons, occupational performance is a fundamental part of development throughout childhood. Homework is complex and extends beyond the school context to the home. For many children, the performance of these homework items is a difficult challenge or even impossible to overcome without the help of an adult. This article presents the design, implementation, and functional validation of an intelligent home environment that uses homework activities as a support tool for children with or without attention disabilities. In this project, the Internet of Things (IoT) paradigm is combined with the development of robotic assistance to implement an intelligent home environment. In this environment, we have included intelligent things (where the usual study chairs and desks become smart objects) that determine in real-time the child’s behavior during the development of homework and a robotic assistant which interacts with the children providing the necessary accompaniment (supervision and guidelines) just as a therapist would do. This development has been functionally validated by tests on several school-aged children without pathologies. In a later phase of the study, the proposal will be validated with children with different pathologies with an impact on learning, including Attention Disorder Hyperactivity Disorder (ADHD). The goal is the generation of intelligent places for therapeutic purposes within the home as assistance for children having difficulties to work with their homework assignment due to ADHD.