Marlon Alberto Piñeres-Melo

Human activity recognition—HAR—plays a crucial role in the lives of patients battling neurodegenerative diseases. These debilitating conditions, such as Alzheimer’s or Parkinson’s, affect individuals’ ability to perform daily tasks autonomously and safely. HAR technology offers an invaluable solution by enabling real-time monitoring and assistance, helping to maintain independence and quality of life for patients. Additionally, this technology provides a valuable data source for doctors and caregivers, allowing for more precise and personalized care, which can make a difference in managing and treating these neurodegenerative diseases. The objective of this review is to identify the contribution of Transfer Learning and Reinforcement Learning in supporting the processes of daily activity recognition, thus enhancing the quality of life for patients. As this is a trending topic, the literature surrounding it is quite dispersed, which is why this review aims to present the current line of research in this field. To carry out this analysis, the science tree paradigm was used, which establishes two fundamental stages of analysis. The first is delimited by scientometrics, where the leading countries in the application of such technologies can be identified. This review highlights the evolution in the use of transfer learning and reinforcement learning in HAR in the healthcare field, where these techniques have significantly improved the accuracy and adaptability of real-time monitoring systems. The studies reviewed indicate that transfer learning has allowed models to adapt to data variations without requiring large volumes of manual labeling, which is essential in clinical and patient monitoring contexts. Additionally, reinforcement learning has optimized decision-making in complex scenarios, enabling activity recognition systems to dynamically adjust monitoring parameters, enhancing detection and response to critical or unusual activities in multi-user environments. These advances demonstrate that, by integrating these approaches, greater personalization and robustness can be achieved in human activity recognition, thereby improving the quality of life for patients in clinical settings.

The monitoring of cardio-cerebrovascular diseases increasingly underscores the importance of understanding and utilizing advanced devices, particularly those enhanced by Internet of Things (IoT) and Artificial Intelligence (AI) technologies. These innovations have significantly transformed healthcare, especially in managing heart failure—a chronic condition affecting 26 million people globally and the leading cause of hospitalization among individuals over 65 years age. Monitoring systems track critical variables, including heart rate, blood pressure, oxygen saturation, body weight, dyspnea, and lower limb edema, providing essential data for evaluating treatment efficacy. A key advantage of these technologies is their ability to enable home-based monitoring, reducing hospital readmissions and lowering healthcare costs. This chapter examines how AI-driven technologies facilitate timely and effective interventions for heart failure, enhancing patient outcomes and quality of life. Furthermore, it traces the evolution of these advancements, highlighting their contributions to the field and potential to optimize disease management. By integrating precise monitoring with advanced analytics, these systems offer a transformative approach to medical care, demonstrating their value in improving both clinical and economic aspects of health management.

Home care and telemedicine are crucial for physical and mental health. Although there is a lot of information on these topics, it is scattered across various sources, making it difficult to identify key contributions and authors. This study conducts a scientometric analysis to consolidate the most relevant information. The methodology is divided into two parts: first, a scientometric mapping that analyzes scientific production by country, journal, and author; second, the identification of prominent contributions using the Tree of Science (ToS) tool. The goal is to identify trends and support decision-making in the health sector by providing guidelines based on the most relevant research.