Advancements in modern medicine have bolstered the usage of home-based rehabilitation services for patients, particularly those recovering from diseases or conditions that necessitate a structured rehabilitation process. Understanding the technological factors that can influence the efficacy of home-based rehabilitation is crucial for optimizing patient outcomes. As technologies continue to evolve rapidly, it is imperative to document the current state of the art and elucidate the key features of the hardware and software employed in these rehabilitation systems. This narrative review aims to provide a summary of the modern technological trends and advancements in home-based shoulder rehabilitation scenarios. It specifically focuses on wearable devices, robots, exoskeletons, machine learning, virtual and augmented reality, and serious games. Through an in-depth analysis of existing literature and research, this review presents the state of the art in home-based rehabilitation systems, highlighting their strengths and limitations. Furthermore, this review proposes hypotheses and potential directions for future upgrades and enhancements in these technologies. By exploring the integration of these technologies into home-based rehabilitation, this review aims to shed light on the current landscape and offer insights into the future possibilities for improving patient outcomes and optimizing the effectiveness of home-based rehabilitation programs.

Shoulder rehabilitation is a process that requires physical therapy sessions to recover the mobility of the affected limbs. However, these sessions are often limited by the availability and cost of specialized technicians, as well as the patient’s travel to the session locations. This paper presents a novel smartphone-based approach using a pose estimation algorithm to evaluate the quality of the movements and provide feedback, allowing patients to perform autonomous recovery sessions. This paper reviews the state of the art in wearable devices and camera-based systems for human body detection and rehabilitation support and describes the system developed, which uses MediaPipe to extract the coordinates of 33 key points on the patient’s body and compares them with reference videos made by professional physiotherapists using cosine similarity and dynamic time warping. This paper also presents a clinical study that uses QTM, an optoelectronic system for motion capture, to validate the methods used by the smartphone application. The results show that there are statistically significant differences between the three methods for different exercises, highlighting the importance of selecting an appropriate method for specific exercises. This paper discusses the implications and limitations of the findings and suggests directions for future research.

In this paper, we present and discuss an innovative approach to solve Job Shop scheduling problems based on machine learning techniques. Traditionally, when choosing how to solve Job Shop scheduling problems, there are two main options: either use an efficient heuristic that provides a solution quickly, or use classic optimization approaches (e.g., metaheuristics) that take more time but will output better solutions, closer to their optimal value. In this work, we aim to create a novel architecture that incorporates reinforcement learning into scheduling systems in order to improve their overall performance and overcome the limitations that current approaches present. It is also intended to investigate the development of a learning environment for reinforcement learning agents to be able to solve the Job Shop scheduling problem. The reported experimental results and the conducted statistical analysis conclude about the benefits of using an intelligent agent created with reinforcement learning techniques. The main contribution of this work is proving that reinforcement learning has the potential to become the standard method whenever a solution is necessary quickly, since it solves any problem in very few seconds with high quality, approximate to the optimal methods.

User modeling and user adaptive interaction has become a central research issue to understand users as they interact with technology. The importance of the development of well adapted interfaces to several kinds of users and the differences that characterize them is the basis of the successful interaction. User Personas is a technique that allows the discovery and definition of the archetype users of a system. With that knowledge, the system should shape itself, inferring the user expertise to provide its users with the best possible experience. In this paper, an architecture that combines User Personas and a dynamic, evolving system is proposed, along with an evaluation by its target users. The proposed system is able to infer the user and its matching Persona, and keeps shaping itself in parallel with the user's discovery of the system.

Physical rehabilitation is crucial for injury recovery, offering pain relief and faster healing. However, traditional methods rely heavily on in-person professional feedback, which can be time-consuming, expensive, and prone to human error, limiting accessibility and effectiveness. As a result, patients are often encouraged to perform exercises at home; however, due to the lack of professional guidance, motivation dwindles and adherence becomes a challenge. To address this, this paper proposes a smartphone-based solution that enables patients to receive exercise feedback independently. This paper reviews current Computer Vision systems for assessing rehabilitation exercises and introduces an intelligent system designed to assist patients in their recovery. Our proposed system uses motion tracking based on Computer Vision, analyzing videos recorded with a smartphone. With accessibility as a priority, the system is evaluated against the advanced Qualysis Motion Capture System using a dataset labeled by expert physicians. The framework focuses on human pose detection and movement quality assessment, aiming to reduce recovery times, minimize human error, and make rehabilitation more accessible. This proof-of-concept study was conducted as a pilot evaluation involving 15 participants, consistent with earlier work in the field, and serves to assess feasibility before scaling to larger datasets. This innovative approach has the potential to transform rehabilitation, providing accurate feedback and support to patients without the need for in-person supervision or specialized equipment.