Dispositivo Robótico Multifuncional para la Rehabilitación de las Extremidades Superiores

This work presents an innovative rehabilitation device called Universal Haptic Pantograph (UHP). This robot, thanks to its multi-configurable structure allows the rehabilitation of all joints of the upper limb with a single mechanical device. In addition, it has been designed with the ability to perform different assistive and resistive tasks, allowing its adaptation to the recovery status of the patient. Finally, a support software, the Telereha generates a virtual reality environment, facilitating the execution of the exercise, while increasing the motivation of the patient. For the correct execution of the rehabilitation tasks the proposed algorithms have been implemented in real time. Also, different experimental tests have been carried out. Observing the results, it is concluded that the UHP rehabilitation robot works correctly with different rehabilitation tasks.Este trabajo ha sido parcialmente financiado por el Ministerio de Economía y Competitividad MINECO & FEDER en el marco del proyecto DPI-2012-32882, así como por las becas PRE-2014-1-152 del Gobierno Vasco y BES-2013-066142 del Ministerio de Economía y Competitividad, el proyecto IT914- 16 del Gobierno Vasco, el proyecto PPG17/56 de la UPV/EHU, por Euskampus Fundazioa, por FIK y por el Ministerio de Ciencia e Innovación en el marco del proyecto PDI-020100-2009- 21

PIAAC Bibliography - 2008-2019

In order to enhance the performance of rehabilitation robots, it is imperative to know both force and motion caused by the interaction between user and robot. However, common direct measurement of both signals through force and motion sensors not only increases the complexity of the system but also impedes affordability of the system. As an alternative of the direct measurement, in this work, we present new force and motion estimators for the proper control of the upper-limb rehabilitation Universal Haptic Pantograph (UHP) robot. The estimators are based on the kinematic and dynamic model of the UHP and the use of signals measured by means of common low-cost sensors. In order to demonstrate the effectiveness of the estimators, several experimental tests were carried out. The force and impedance control of the UHP was implemented first by directly measuring the interaction force using accurate extra sensors and the robot performance was compared to the case where the proposed estimators replace the direct measured values. The experimental results reveal that the controller based on the estimators has similar performance to that using direct measurement (less than 1 N difference in root mean square error between two cases), indicating that the proposed force and motion estimators can facilitate implementation of interactive controller for the UHP in robot-mediated rehabilitation trainings

Kinematical and dynamical modeling of a multipurpose upper limbs rehabilitation robot

Knowing accurate model of a system is always beneficial to design a robust and safe control while allowing reduction of sensors-related cost as the system outputs are predictable using the model. In this context, this paper addresses the kinematical and dynamical model identification of the multipurpose rehabilitation robot, Universal Haptic Pantograph (UHP), and present experimental validations of the identified models. The UHP is a Pantograph based innovative robot actuated by two SEAs (Series Elastic Actuator), aiming at training impaired upper limbs after a stroke. This novel robot, thanks to its lockable/unlockable joints, can change its mechanical structure so that it enables stroke patient to perform different training exercises of the shoulder, elbow and wrist. This work focuses on the ARM mode, which is a training mode used to rehabilitate elbow and shoulder. The kinematical model of UHP is identified based on the loop vector equations, while the dynamical model is derived based on the Lagrangian formulation. To demonstrate the accuracy of the models, several experimental tests were performed. The results reveal that the mean position error between estimated values with the model and actual measured values stays in 3 mm (less than 2% of the maximum motion range). Moreover, the error between estimated and measured interaction force is smaller than 10% of maximum force range. So, the developed models can be adopted to estimate motion and force of UHP as well as control it without the need of additional sensors such as a force sensor, resulting in the reduction of total robot cost.This work was supported in part by the Basque Country Governments (GV/EJ) under grant PRE-2014-1-152, UPV/EHU’s PPG17/56 project, Basque Country Governments IT914-16 project, Spanish Ministry of Economy and Competitiveness’ MINECO & FEDER inside DPI- 2012-32882 projects, Spanish Ministry of Economy and Competitiveness BES-2013-066142 grant, Euskampus, FIK

Sensorized Tip for Monitoring People with Multiple Sclerosis that Require Assistive Devices for Walking

Multiple Sclerosis (MS) is a neurological degenerative disease with high impact on our society. In order to mitigate its effects, proper rehabilitation therapy is mandatory, in which individualisation is a key factor. Technological solutions can provide the information required for this purpose, by monitoring patients and extracting relevant indicators. In this work, a novel Sensorized Tip is proposed for monitoring People with Multiple Sclerosis (PwMS) that require Assistive Devices for Walking (ADW) such as canes or crutches. The developed Sensorized Tip can be adapted to the personal ADW of each patient to reduce its impact, and provides sensor data while naturally walking in the everyday activities. This data that can be processed to obtain relevant indicators that helps assessing the status of the patient. Different from other approaches, a full validation of the proposed processing algorithms is carried out in this work, and a preliminary study-case is carried out with PwMS considering a set of indicators obtained from the Sensorized Tip’s processed data. Results of the preliminary study-case demonstrate the potential of the device to monitor and characterise patient status

Assembled PTO based on an array of double-acting hydraulic cylinders for WECs: From Conceptual Design to an Adjusted Detailed Model

All Wave Energy Converters (WECs) based on wave activated bodies comprises a Power Take- Off (PTO) system among other subsystems like the reaction mechanism, the supervisor of the system and the electrical generator. One of the most applied PTO systems consists of oil high pressure hydraulic devices. These devices are able to apply high forces, to store large quantities of energy through accumulators and to provide smoother power output to the motor coupled to a generator. In these systems the poor efficiency and the oil leakages contaminating the environment are considered main drawbacks. Despite of this, they are widely used in several promising WECs with the aim of optimizing the harvested wave energy along the time. The initial challenge to absorb an oscillating movement of ±30º at 5rad/s as a maximum angular speed absorbing up to 16000Nm from a specific WEC, led to the development of a simplified hydraulic model before manufacturing a PTO prototype to be verified in a Test Bench (Figure 1). The experimental results of PTO under different conditions have been used to adjust a full detailed PTO Model using Mathworks® software platform. This work presents a patented oil high pressure hydraulic PTO prototype based on an array of four double-acting hydraulic cylinders. This prototype has been designed and completely modelled as a proof concept at 1:4 scale being able to apply a variable Coulomb type damping torque through the activation of each hydraulic cylinder independently and through the modification of geometrical parameters easily. The complete model of the PTO has been accurately tuned up through adjustment of model parameters using the results of the experimental tests. This will allow the study of control strategies to optimize the extracted wave energy from a specific WEC, like point-absorbers

Extension of the PWM-based encoding-decoding algorithm for Spiking Neural Networks

[Resumen] Las Redes Neuronales de Impulsos (Spiking Neural Networks, SNN) son la última generación de redes neuronales y tratan de imitar con mayor fidelidad el funcionamiento del cerebro humano codificando la información a través de spikes o series de impulsos. Debido a que la mayoría de procesos reales son analógicos, para emplear este tipo de redes es necesario el uso de algoritmos de codificación y decodificación. El algoritmo de codificación basado en PWM es un novedoso algoritmo temporal de codificación que supera con creces a sus algoritmos predecesores en la precisión a la hora de construir y reconstruir la señal original. A pesar de sus múltiples ventajas, este algoritmo necesita dos puntos cronológicos de la serie temporal original para poder codificar. En este sentido, resulta de interés poder aplicar este tipo de codificación en otro tipo de aplicaciones, como el tratamiento de imágenes, en las que no existe orden cronológico. Por tanto, en este trabajo se presenta una extensión de este algoritmo de codificación para que no sea necesario el uso de dos valores temporales consecutivos y así poder aplicarlo a cualquier tipo de aplicación. Además, la nueva extensión permite reducir en más de un 50% el coste computacional de los procesos de codificación y decodificación.[Abstract] Spiking Neural Networks (SNN) are the latest generation of neural networks and attempt to mimic human brain functioning more closely by encoding the information through spike trains. Since most of the real processes are analog, SNN requires the use of encoding-decoding algorithms. The PWM-based encoding-decoding algorithm is a novel temporal encoding algorithm that surpasses its predecessor algorithms in terms of precision. Despite its many advantages, this algorithm requires two chronological values from the original time series in order to encode a spike. In this sense, it is also interesting to be able to apply this algorithm to other types of application, such as image processing, where it does not exist a chronogical order of the points. Hence, this paper proposes an extension of the PWM-based encoding-decoding algorithm, in which is not necessary to employ two consecutive values in the encoding process, enabling the algorithm to be applied to any type of application. In addition, the new extension reduces the computational cost of encoding and decoding processes by more than 50 %.Gobierno Vasco; PIBA 2020 1 0008Gobierno Vasco; IT1726-22Ministerio de Ciencia e Innovación; PID2020-112667RB-I0

Virtual Sensors For Advanced Controllers In Rehabilitation Robotics

In order to properly control rehabilitation robotic devices, the measurement of interaction force and motion between patient and robot is an essential part. Usually, however, this is a complex task that requires the use of accurate sensors which increase the cost and the complexity of the robotic device. In this work, we address the development of virtual sensors that can be used as an alternative of actual force and motion sensors for the Universal Haptic Pantograph (UHP) rehabilitation robot for upper limbs training. These virtual sensors estimate the force and motion at the contact point where the patient interacts with the robot using the mathematical model of the robotic device and measurement through low cost position sensors. To demonstrate the performance of the proposed virtual sensors, they have been implemented in an advanced position/force controller of the UHP rehabilitation robot and experimentally evaluated. The experimental results reveal that the controller based on the virtual sensors has similar performance to the one using direct measurement (less than 0.005 m and 1.5 N difference in mean error). Hence, the developed virtual sensors to estimate interaction force and motion can be adopted to replace actual precise but normally high-priced sensors which are fundamental components for advanced control of rehabilitation robotic devices.This work was supported in part by the Basque Country Governments (GV/EJ) under grant PRE-2014-1-152, UPV/EHU's PPG17/56 project, Basque Country Governments IT914-16 project, Spanish Ministry of Economy and Competitiveness' MINECO & FEDER inside DPI2017-82694-R project, Euskampus, FIK and Spanish Ministry of Science and Innovation PDI-020100-2009-21 project

Kinematical and dynamical modelling of a multipurpose upper limbs rehabilitation robot

Knowing accurate model of a system is always beneficial to design a robust and safe control while allowing reduction of sensors-related cost as the system outputs are predictable using the model. In this context, this paper addresses the kinematical and dynamical model identification of the multipurpose rehabilitation robot, Universal Haptic Pantograph (UHP), and present experimental validations of the identified models. The UHP is a Pantograph based innovative robot actuated by two SEAs (Series Elastic Actuator), aiming at training impaired upper limbs after a stroke. This novel robot, thanks to its lockable/unlockable joints, can change its mechanical structure so that it enables stroke patient to perform different training exercises of the shoulder, elbow and wrist. This work focuses on the ARM mode, which is a training mode used to rehabilitate elbow and shoulder. The kinematical model of UHP is identified based on the loop vector equations, while the dynamical model is derived based on the Lagrangian formulation. To demonstrate the accuracy of the models, several experimental tests were performed. The results reveal that the mean position error between estimated values with the model and actual measured values stays in 3 mm (less than 2% of the maximum motion range). Moreover, the error between estimated and measured interaction force is smaller than 10% of maximum force range. So, the developed models can be adopted to estimate motion and force of UHP as well as control it without the need of additional sensors such as a force sensor, resulting in the reduction of total robot cost.This work was supported in part by the Basque Country Governments (GV/EJ) under grant PRE-2014-1-152, UPV/EHU’s PPG17/56 project, Basque Country Governments IT914-16 project, Spanish Ministry of Economy and Competitiveness’ MINECO & FEDER inside DPI2012-32882 projects, Spanish Ministry of Economy and Competitiveness BES-2013-066142 grant, Euskampus, FIK

Intelligent Sitting Posture Classifier for Wheelchair Users

In recent years, there has been growing interest in postural monitoring while seated, thus preventing the appearance of ulcers and musculoskeletal problems in the long term. To date, postural control has been carried out by means of subjective questionnaires that do not provide continuous and quantitative information. For this reason, it is necessary to carry out a monitoring that allows to determine not only the postural status of wheelchair users, but also to infer the evolution or anomalies associated with a specific disease. Therefore, this paper proposes an intelligent classifier based on a multilayer neural network for the classification of sitting postures of wheelchair users. The posture database was generated based on data collected by a novel monitoring device composed of force resistive sensors. A training and hyperparameter selection methodology has been used based on the idea of using a stratified K-Fold in weight groups strategy. This allows the neural network to acquire a greater capacity for generalization, thus allowing, unlike other proposed models, to achieve higher success rates not only in familiar subjects but also in subjects with physical complexions outside the standard. In this way, the system can be used to support wheelchair users and healthcare professionals, helping them to automatically monitor their posture, regardless physical complexions.This work was supported in part by the Ministry of Science and Innovation-StateResearch Agency/Project funded by MCIN/State Research Agency(AEI)/10.13039/501100011033 under Grant PID2020-112667RB-I00,in part by the Basque Government under Grant IT1726-22, and in part by the Predoctoral Contracts of the Basque Government under Grant PRE-2021-1-0001 and Grant PRE-2021-1-021