Marina Vallés

Rehabilitation robotics is evolving from automation-driven systems towards collaborative approaches that adapt to the physiological needs of individual patients. However, most current control strategies remain based on externally measurable variables such as kinematics, trajectories, and interaction forces, providing only an indirect representation of the mechanisms underlying motor recovery. This chapter introduces the concept of Muscle Space Control, a framework that integrates real-time MSK modelling into human-robot collaboration. By estimating internal biomechanical variables, particularly muscle forces, MSK models enable robotic systems to incorporate the user's muscular state into therapy planning and control. The chapter reviews the foundations of muscle-targeted robotic assistance, discusses the requirements for real-time MSK models, and presents strategies for their integration into rehabilitation control architectures. Muscle Space Control is proposed as a pathway towards more personalised, objective, and physiologically informed rehabilitation technologies.

Background: Knee proprioception is essential for injury prevention, stability, and performance improvement. Reliable proprioception measurement tools are crucial for accurate assessment and effective rehabilitation. Thus, the aim of the present study was to determine the validity and reliability of a parallel robotic system with musculoskeletal model-based feedback to assess knee joint position sense (JPS) in healthy people. Material/Methods: Fourteen healthy participants (7 men and 7 women) (mean (SD) age = 35.21 (9.32) years) volunteered for the study. The validity, inter-rater and intra-rater reliability of a parallel robotic system for measuring JPS were evaluated. Results: The results indicate moderate to strong reliability in the 30° JPS test (ICC = 0.41-0.66; SEM = 0.27-0.37), and strong to excellent reliability in the 50° JPS test (ICC = 0.64-0.87; SEM = 0.31-0.45). Significant concurrent validity with correlations of variable strength was detected between the inclinometer and the robot in the 30° JPS tests (Pearson’s correlation = 0.52-0.66; SEM = 0.30-0.43), and in the 50° JPS tests (Pearson’s correlation = 0.55; SEM = 0.44) but only for the passive motion in closed kinetic chain. Conclusions: A parallel robotic system with musculoskeletal model-based knee measurement provides a valid and reliable method for assessing knee JPS in healthy people. Its precision makes it a promising tool for both clinical use and future research applications.

The present work introduces a novel method to determine the force vector that a subject must exert at the end of a limb in order to achieve the desired mus

Conventional assistive and rehabilitative robotic systems often overlook human biomechanics, particularly muscular forces, as they predominantly operate in joint or task space and focus on position and exchanged forces. Similarly, traditional manual rehabilitation techniques employed by physiotherapists struggle to obtain quantitative measurements and make precise modifications to key human variables, resulting in predominantly qualitative methods and outcomes. In response to these limitations, this article introduces an innovative assistive robot controller that operates in the muscular space, targeting specific muscles in the lower limb, and distinguishing itself from existing solutions that focus primarily on joint or task space. A key innovation of our approach is the real-time measurement of muscular forces during dynamic tasks, obtained from a calibrated musculoskeletal model. These measurements enable the establishment of a multistep closed-loop controller, with the outer loop precisely tracking the desired muscular forces. Implemented within a configurable viscous environment, the controller provides a natural response for the user. Experimental evaluations conducted using a parallel robot designed for rehabilitation demonstrate the controller’s efficacy. Incorporating the outer loop reduced the median relative error of the tracked muscular force by nearly 80% and decreased the variability of this error by over 85% compared to a pure viscous environment defined as the baseline. These findings highlight the potential applications of this control framework in areas, such as assistive robotics and precision rehabilitation. By achieving objective measurement and control, the system may enhance rehabilitation outcomes, offering tailored exercises that match the individual needs, capabilities, and engagement of each patient.

Gracias a su precisión y su gran capacidad de carga, en la actualidad el uso de robots paralelos en aplicaciones de interacción humano-robot es habitual. No obstante, en su espacio de trabajo pueden aparecer configuraciones problemáticas (singularidades Tipo II) donde se pierde el control del robot, comprometiendo la seguridad y limitando las técnicas de control aplicables. En este artículo, se establecen criterios clave para la detección y evasión de singularidades Tipo II en tiempo real. De la misma forma, se presenta la implementación de un algoritmo de evasión de singularidades Tipo II con un control de fuerza-posición en un robot paralelo de 4 grados de libertad para rehabilitación de rodilla. El algoritmo de evasión detecta las singularidades Tipo II midiendo la posición del efector final e identificando los actuadores responsables de la singularidad. Los resultados obtenidos verifican la efectividad de los puntos clave propuestos en el desarrollo aplicaciones de interacción humano-robot en tiempo real para rehabilitación física con robots paralelos.

Rehabilitation tasks demand robust and accurate trajectory-tracking performance, mainly achieved with parallel robots. In this field, limiting the value of the force exerted on the patient is crucial, especially when an injured limb is involved. In human-robot interaction studies, the admittance controller modifies the location of the robot according to the user efforts driving the end-effector to an arbitrary location within the workspace. However, a parallel robot has singularities within the workspace, making implementing a conventional admittance controller unsafe. Thus, this study proposes an admittance controller that overcomes the limitations of singular configurations by using a real-time singularity avoidance algorithm. The singularity avoidance algorithm modifies the original trajectory based on the actual location of the parallel robot. The complemented admittance controller is applied to a 4 degrees of freedom parallel robot for knee rehabilitation. In this case, the actual location is measured by a 3D tracking system because the location calculated by the forward kinematics is inaccurate in the vicinity of a singularity. The experimental results verify the effectiveness of the proposed admittance controller for safe knee rehabilitation exercises

Parallel robots (PRs) are closed-chain manipulators with diverse applications due to their accuracy and high payload. However, there are configurations within the workspace named Type II singularities where the PRs lose control of the end-effector movements. Type II singularities are a problem for applications where complete control of the end-effector is essential. Trajectory planning produces accurate movements of a PR by avoiding Type II singularities. Generally, singularity avoidance is achieved by optimising a geometrical path with a velocity profile considering singular configurations as obstacles. This research presents an algorithm that avoids Type II singularities by modifying the trajectory of a subset of the actuators. The subset of actuators represents the limbs responsible for a Type II singularity, and they are identified by the angle between two Output Twist Screws. The proposed avoidance algorithm does not require optimisation procedures, which reduces the computational cost for offline trajectory planning and makes it suitable for online trajectory planning. The avoidance algorithm is implemented in offline trajectory planning for a pick and place planar PR and a spatial knee rehabilitation PR

Although parallel manipulators (PMs) started with the introduction of architectures with 6 Degrees of Freedom (DoF), a vast number of applications require less than 6 DoF. Consequently, scholars have proposed architectures with 3 DoF and 4 DoF, but relatively few 4 DoF PMs have become prototypes, especially of the two rotation (2R) and two translation (2T) motion types. In this paper, we explain the mechatronics design, prototype and control architecture design of a 4 DoF PM with 2R2T motions. We chose to design a 4 DoF manipulator based on the motion needed to complete the tasks of lower limb rehabilitation. To the author's best knowledge, PMs between 3 and 6 DoF for rehabilitation of lower limbs have not been proposed to date. The developed architecture enhances the three minimum DoF required by adding a 4 DoF which allows combinations of normal or tangential efforts in the joints, or torque acting on the knee. We put forward the inverse and forward displacement equations, describe the prototype, perform the experimental setup, and develop the hardware and control architecture. The tracking accuracy experiments from the proposed controller show that the manipulator can accomplish the required application.