A specific challenge in robotic control applications is the identification and regulation of actuators that provide mechanical traction and motion to the robot links. The design of actuator control laws, grounded in parametric identification and experimental motor characterization, enables numerical simulations to explore diverse operating scenarios. This article presents the initial phases in the development of a robotic rehabilitation system, focused on the kinematic modeling of a parallelogram-configuration robot for upper-limb therapy, the fuzzy identification of its actuators, and their closed-loop evaluation using a fuzzy Parallel Distributed Compensation (PDC) controller with state feedback (Ackermann), whose poles are optimized via the Grey Wolf Optimizer (GWO) metaheuristic. This controller was selected for its congruence with the nonlinear universe of discourse defined by the identified model, a key feature for operation within specific functional ranges in medical applications. The simulation and hardware platform results provide evidence that fuzzy dynamic models constitute a valuable tool for application in rehabilitation systems. This work serves as a foundation for future physical implementations with the fully coupled robotic system, in order to ensure operational safety prior to the start of clinical trials.

This article presents the conceptual design of a robotic rehabilitation device focused on the recovery of the elbow joint, aimed primarily at individuals who have suffered a cerebrovascular disease or trauma to that joint. The device was developed following Ullman’s mechanical design methodology, which allowed the project to be structured systematically. The robot enables repetitive flexion-extension movements of the elbow, with the goal of restoring mobility to the affected upper limb. To ensure the mechanical integrity and functional reliability of the proposed system, a structural validation was conducted through finite element analysis, along with studies for the selection and design of the electronic and instrumentation components. This development represents a step forward toward accessible, functional, and efficient rehabilitation solutions.

The integration of Artificial Intelligence (AI) into healthcare has open a new era of medical diagnosis and treatment. This chapter critically examines a collection of scholarly articles that delve into the multifaceted applications of AI in the healthcare domain such as diagnosis using image and chemical studies, patient familiar history and big data acquired through the use of wearables. Also, we dive into the ethical and legal aspects of training IA with private data and which repercussions can have.

Robot-assisted rehabilitation effectively enhances motor recovery in patients with mobility impairments. This study examines the REHAP system—a two-DOF mechatronic rehabilitation device for passive physiotherapy—focusing on dynamic modelling and energy-efficiency analysis. The Euler–Lagrange method was employed to derive the dynamic model, incorporating actuator parameters obtained through experimental characterisation of DC motors. We assessed energy consumption under various control strategies and mechanical-loading conditions. Results indicate that the choice of control strategy and the tuning of actuator parameters significantly impact system efficiency, highlighting the critical need for accurate model calibration. Integrating dynamic modelling improves both motion precision and energy economy, thereby enabling more sustainable rehabilitation technologies. This research underscores how energy-aware control strategies can enhance both performance and sustainability in robotic physiotherapy systems.

This chapter delves into the transformative impact of haptic devices systems in the medical field. It highlights how these technologies have revolutionized healthcare delivery by enhancing surgical precision and efficacy The chapter reviews recent literature that reflects a burgeoning interest in understanding how these tools enhance the capabilities of medical practitioners, particularly in high-stakes environments such as surgery and rehabilitation. Studies have shown that haptic devices significantly improve surgeon dexterity in teleoperated environments, leading to enhanced patient outcomes and reduced surgical complications. Furthermore, the incorporation of tactile feedback in robotic surgery has been shown to enhance the surgeon's situational awareness and precision during procedures.

Stroke and neuromuscular trauma constitute a leading global cause of severe long-term upper-limb motor disability. Although rehabilitation robotics has proven effective in inducing activity-dependent cortical neuroplasticity through high-intensity training, a significant translational gap persists between complex laboratory prototypes and commercially accessible home-based systems. To address this, a systematic review was conducted following PRISMA 2020 guidelines, analyzing 45 studies from high-impact databases (2016–2026) focused on mechatronic design and clinical validations. The findings reveal a critical transition from rigid-link anthropomorphic exoskeletons toward cable-driven and soft robotic architectures, successfully reducing arm-coupled inertia to under 1.5 kg and mitigating kinematic misalignment risks at the human-robot interface. However, a severe validation gap persists, with less than 10% of studies evaluating prototypes on actual post-stroke cohorts. Additionally, the high computational cost and noise susceptibility of advanced sEMG-driven Assist-as-Needed (AAN) controllers currently limit their viability for autonomous home use. The review concludes that democratizing robotic telerehabilitation requires prioritizing intrinsic mechanical compliance, affordability, and IoT telemetry over redundant structural complexity, alongside an urgent shift toward multicenter Randomized Controlled Trials (RCTs). Spanish-language metadata / Metadatos en españolTítulo en español:Exoesqueletos robóticos de miembro superior para la rehabilitación neuromuscular: una revisión sistemática sobre arquitecturas ligeras y control biocooperativo Resumen:El accidente cerebrovascular y los traumatismos neuromusculares constituyen una de las principales causas a nivel mundial de discapacidad motora grave y prolongada de los miembros superiores. Aunque la robótica de rehabilitación ha demostrado ser eficaz para inducir neuroplasticidad cortical dependiente de la actividad mediante entrenamiento de alta intensidad, persiste una importante brecha traslacional entre los complejos prototipos de laboratorio y los sistemas de uso domiciliario comercialmente accesibles. Para abordar esta problemática, se realizó una revisión sistemática siguiendo las directrices PRISMA 2020, en la que se analizaron 45 estudios procedentes de bases de datos de alto impacto (2016–2026), centrados en el diseño mecatrónico y las validaciones clínicas. Los hallazgos revelan una transición significativa desde los exoesqueletos antropomórficos de eslabones rígidos hacia arquitecturas robóticas accionadas por cables y estructuras blandas, que han logrado reducir la inercia acoplada al brazo a menos de 1,5 kg y mitigar los riesgos de desalineación cinemática en la interfaz humano-robot. Sin embargo, persiste una importante brecha de validación, ya que menos del 10 % de los estudios evaluaron los prototipos en cohortes reales de pacientes que habían sufrido un accidente cerebrovascular. Además, el elevado coste computacional y la susceptibilidad al ruido de los controladores avanzados de asistencia según necesidad (Assist-as-Needed, AAN) basados en sEMG limitan actualmente su viabilidad para un uso domiciliario autónomo. La revisión concluye que la democratización de la telerehabilitación robótica requiere priorizar la conformidad mecánica intrínseca, la asequibilidad y la telemetría mediante Internet de las Cosas (IoT) frente a una complejidad estructural redundante, junto con una transición urgente hacia ensayos controlados aleatorizados (RCT) multicéntricos. Palabras Claves:Rehabilitación, robótica, robot de rehabilitación. Smart citations:SciteAI. Dimensions.Open Alex.