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

Estimadores de fuerza y movimiento para el control de un robot de rehabilitación de extremidad superior

[Resumen] Con el fin de controlar adecuadamente los robots de rehabilitación, es imprescindible conocer la fuerza y el movimiento de interacción entre el usuario y el robot. Sin embargo, la medición directa a través de sensores de fuerza y posición no sólo aumenta la complejidad del sistema, sino que eleva el coste del dispositivo. Como alternativa a la medición directa, en este trabajo, se presentan nuevos estimadores de fuerza y movimiento para el control del robot de rehabilitación de extremidades superiores Universal Haptic Pantograph (UHP). Estos estimadores están basados en el modelo cinemático y dinámico del robot UHP y en las mediciones de sensores de bajo coste. Con el objetivo de demostrar su eficacia, se han realizado varias pruebas experimentales. Estas pruebas comparan la respuesta del controlador con sensores adicionales y con los nuevos estimadores de fuerza y movimiento. Los resultados han revelado que el rendimiento del controlador es similar con los dos enfoques (inferior a 1N de diferencia en el error cuadrático medio). Esto indica que los estimadores de fuerza y movimiento propuestos pueden facilitar la implementación de controladores de robots de rehabilitación.Ministerio de Economía y Competitividad; DPI-2012-32882Ministerio de Economía y Competitividad; BES-2013-066142Gobierno Vasco; PRE-2014-1-152Gobierno Vasco; IT914-16Universidad del País Vasco/Euskal Herriko Unibertsitatea; PPG17/5

Robot de rehabilitación configurable para terapias del miembro superior

La rehabilitación basada en dispositivos robóticos precisa de un robot capaz de adaptarse al estado de recuperación motora del paciente. En este trabajo se presenta un robot de rehabilitación reconfigurable denominado Universal Haptic Pantograph (UHP). Este dispositivo robótico, gracias a su estructura multiconfigurable, permite la rehabilitación del miembro superior (hombro, codo y muñeca) con un único dispositivo. Además, ha sido diseñado para trabajar con diferentes modalidades de interacción como son las asistidas, correctoras y opositoras, pudiendo así adaptarse al estado funcional progresivo del paciente durante el proceso de rehabilitación. Con el objetivo de garantizar el correcto funcionamiento de este sistema robótico se han realizado diferentes ensayos experimentales. Los resultados demuestran que el robot de rehabilitación UHP funciona correctamente con diferentes tareas de rehabilitación, realizando movimientos suaves que garantizan la seguridad del usuario en todo momento.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 la beca PRE-2014-1-152 y el proyecto IT914-16 del Gobierno Vasco, el proyecto PPG17/56 de la UPV/EHU y por Euskampus Fundazioa. Además, los autores desean expresar su agradecimiento al centro de investigación Tecnalia por su colaboración y por prestar su robot de rehabilitación UHP

Pre-clinical validation of the UHP multifunctional upper-limb rehabilitation robot based platform

Interest in robotic devices for rehabilitation has increased in the last years, due to the increasing number of patients that require rehabilitation therapies, and the need to optimize existing resources. The UHP rehabilitation robot is a multifunctional device that allows to execute robotized therapies for the upper-limb using a simple pantograph based reconfigurable structure and the implementation of advanced position/force control approaches. However, in applications such as rehabilitation, where the robotic device interacts directly with the user, complying with the demands of the users is as important as complying with the functional requirements. Otherwise, the patient will reject the robotic device. Therefore, in this work the pre-clinical validation of the UHP upper-limb rehabilitation robotic platform is presented. 25 subjects of different physical characteristics have participated in the evaluation of the device, evaluating not only the correct behaviour of the device, but also its safety and adaptativity. Results show the correct behaviour of the platform, and a good acceptance rate of the device.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

Inclusive and seamless control framework for safe robot-mediated therapy for upper limbs rehabilitation

Robot-based rehabilitation requires not only the use of a suitable robot, but also an optimal strategy to guarantee that the interaction forces with the patient fit his or her impairment level. In this work, an inclusive and seamless control framework for upper limb rehabilitation robots is presented and validated. The proposed control framework involves 1) a complete set of training modes (assistive, corrective and resistive) that can be adapted to the needs of the different states of the patient’s recovery, and 2) three different advanced controllers (position, force, impedance) to track safely the force and motion references defined by the aforementioned training modes. In addition, the proposed framework allows one to tune the parameters critical to the safety of the user, such as the maximum interaction forces or the maximum speed of the robot movement. In order to validate the proposed control framework, a set of experiments have been carried out in the Universal Haptic Pantograph (UHP) upperlimb rehabilitation robot. Results show that the proposed control framework for robot-mediated therapy works properly in terms of adaptability, robustness, and safety, which are crucial factors for use with patients.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

A preliminary analysis of gait performance of patients with multiple sclerosis using a sensorized crutch tip

The quality of life and functional mobility of patients with Multiple Sclerosis (MS) can significantly improve with exercise and a rehabilitation therapy adjusted to the needs of each patient. The assessment of gait and functional mobility of patients with MS is usually done based on clinical scales and tests, which have various limitations. This work presents the preliminary results of a clinical study carried out with patients with MS walking with a sensorized crutch tip. This tip allows to define new indicators that can be correlated with the clinical assessment scales and provide further objective and quantitative information to assess gait performance and level of impairment of patients with MS, and characterize their gait patterns. The results suggest that parameters such as the average cycle time and the average percentage of body weight might be useful to evaluate the gait performance and level of disability. Moreover, parameters related with the pitch angle of the crutch allow to determine crutch usage patterns and spot differences between patients with similar functional performance.This work was supported by the Government of the Basque Country (grant PRE-2018-2-210), by the University of the Basque Country (project GIU19/45), by the Ministerio de Ciencia e Innovacion (MCI) under grant number DPI2017-82694-R (AEI/FEDER, UE), by Fundacion Euskampus and Fundacion BB

Robot de rehabilitación configurable para terapias del miembro superior

La rehabilitación basada en dispositivos robóticos precisa de un robot capaz de adaptarse al estado de recuperación motora del paciente. En este trabajo se presenta un robot de rehabilitación reconfigurable denominado Universal Haptic Pantograph (UHP). Este dispositivo robótico, gracias a su estructura multiconfigurable, permite la rehabilitación del miembro superior (hombro, codo y muñeca) con un único dispositivo. Además, ha sido diseñado para trabajar con diferentes modalidades de interacción como son las asistidas, correctoras y opositoras, pudiendo así adaptarse al estado funcional progresivo del paciente durante el proceso de rehabilitación. Con el objetivo de garantizar el correcto funcionamiento de este sistema robótico se han realizado diferentes ensayos experimentales. Los resultados demuestran que el robot de rehabilitación UHP funciona correctamente con diferentes tareas de rehabilitación, realizando movimientos suaves que garantizan la seguridad del usuario en todo momento.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 la beca PRE-2014-1-152 y el proyecto IT914-16 del Gobierno Vasco, el proyecto PPG17/56 de la UPV/EHU y por Euskampus Fundazioa. Además, los autores desean expresar su agradecimiento al centro de investigación Tecnalia por su colaboración y por prestar su robot de rehabilitación UHP

Development of a sensorized tip for physical activity classification and fall detection.

255 p.The need for rehabilitation has increased over the years. The individualisation of rehabilitation plays an important role in improving the lives of people in need. Within this individualisation of rehabilitation, the use of physical activity data can be very useful to know the condition of the person. But the knowledge of constant physical activity is almost impossible for therapists.The use of technology, with the purpose of helping to know the physical activity performed by people, is becoming more and more common. Through the sensorization of the person, very interesting data can be obtained to help in rehabilitation. In addition, thanks to this sensorization, it is possible to know other types of events, such as a fall, whose rapid action can avoid increasing the need for rehabilitation.Typically this type of sensorization is done using wearable sensors, but several researchers have proposed the use of sensors integrated into assistive devices for walking for those who need them, as this solution is less invasive. While many of the devices can measure physical activity, they have the disadvantage that the sensing elements are fixed to the assistive devices for walking. This may prevent the user from being comfortable, as it is not the assistive device for walking that he/she normally uses. For this reason, the design of an interchangeable sensor tip between different assistive devices for walking is very important.This paper presents an innovative prototype of a sensorized tip that can be interchanged between different assistive devices for walking. It is capable of classifying different physical activities as well as detecting falls. The developed tip is able to adapt to different assistive devices for walking on the market. In addition, this sensorized tip consists of a series of sensors that provide the information needed to classify physical activities and detect falls. These sensors are: a force sensor to measure axial forces; a 3-axis accelerometer, a 3-axis gyroscope and a 3-axis magnetometer to measure movement; and a barometer to measure height variations. In addition, the data from these sensors is sent via Bluetooth Low Energy, making the devices autonomy very high. Because people when using an assistive device for walking do not always use it in the most appropriate way and tend to twist the device, an algorithm is proposed to estimate what is the angle of advance. In this way the data of the Lateromedial and Anteroposterior angles can be obtained in a simple way. This sensorized tip is validated by a series of tests to achieve good measurement errors.In order to know the physical activity carried out by the assistive device for walking user, a classifier capable of classifying 5 different physical activities is created. The use of Machine Learning techniques is considered for this purpose. In order to use these techniques, it is proposed the segmentation of the data in windows by events to create a series of features for each window. Since the number of features is very high, a dimensionality reduction using Random Forest is proposed in order to use the most relevant ones. Once these most important features are known, it is proposed to use K-Nearest Neighbors (K-NN), Support Vector Machine (SVM) and Artificial Neural Network (ANN) to classify between walking, fast walking, going up stairs, going down stairs and standing still. The classifiers performed are compared with each other, achieving results between 92% and 97%, using only 7 features.Moreover, a fall detector is proposed through the use of the sensorized tip. This fall detector, like the physical activity classifier, follows a methodology in which the data is first divided into windows and then a set of features is generated. This is followed by dimensionality reduction of the features and an SVM-based detector. This classifier is based on two modules, one of which detects when the assistive device for walking is dropped and the other one detects when the person with the assistive device for walking is fallen. Results higher than 0.96 F-Score are achieved. Finally, the results obtained by the sensorized tip-based detector are compared with the results obtained by wearable sensorsErrehabilitazio beharra areagotu egin da urteak igaro ahala. Indibidualizazioak oso eginkizun garrantzitsua du errehabilitazio beharra duten pertsonen bizitza hobetzeko. Errehabilitazioaren indibidualizazio horren barruan, jarduera fisikoari buruzko datuak erabiltzea oso baliagarria izan daiteke pertsonaren egoera ezagutzeko. Baina jarduera fisiko etengabea ezagutzea ia ezinezkoa da terapeutentzat.Teknologiaren erabilera, pertsonek egiten duten jarduera fisikoa ezagutzeko, gero eta ohikoagoa da. Pertsonaren sentsorizazioaren bidez, oso datu interesgarriak lor daitezke errehabilitazioan laguntzeko. Gainera, sentsorizazio horri esker, beste gertaera mota batzuk ezagutzea lor daiteke, hala nola erorketa bat gertatzen denean, eta horien jarduera azkarrak errehabilitazio beharra areagotzea ekidin dezake.Oro har, sentsorizazio mota hori sentsore eramangarrien bidez egiten da, baina hainbat ikertzailek proposatu dute sentsore integratuak erabiltzea laguntza teknikoko gailuetan, behar duten pertsonentzat, eta soluzio hori ez da hain inbaditzailea. Gailu askok jarduera fisikoa neur badezakete ere, elementu sentsoreak ibiltzeko laguntza gailuetan finko egotearen eragozpena dute. Horrek erabiltzailea eroso egotea eragotz dezake, ez baita normalean erabiltzen duen ibiltzeko laguntza gailua. Hori dela eta, ibiltzeko laguntza gailu ezberdinen artean alda daitekeen punta sentsorizatu baten diseinua oso garrantzitsua da.Lan honek ibiltzeko laguntza gailu ezberdinen artean trukatzeko gai den punta sentsorizatuaren prototipo berritzailea aurkezten du. Jarduera fisikoak sailkatzeko eta erorikoak detektatzeko gai da. Garatutako punta merkatuko ibiltzeko laguntza gailuetara egokitzeko gai da. Gainera, sentsorizatutako punta honek sentsoreak ditu, jarduera fisikoen sailkapena egiteko eta erorikoak detektatzeko informazioa ematen dutenak. Sentsore hauek dira: indar axialak neurtzeko indar-sentsore bat; 3 ardatzeko azelerometro bat, 3 ardatzeko giroskopio bat eta 3 ardatzeko magnetometro bat mugimendua neurtzen dutenak; eta altuera aldaketak neurtzeko barometro bat. Gainera, sentsore horien datuak Bluetooth Low Energy bidez bidaltzen dira, gailuaren autonomia oso handia izan dadin. Jendeak ibiltzeko laguntza gailu bat erabiltzen duenean ez duenez beti modurik egokienean erabiltzen eta gailua okertzen duenez, aurrerapen angelua zein den kalkulatzeko algoritmo bat proposatzen da. Horrela, Lateromedial eta Anteroposterior angeluetako datuak erraz lor daitezke. Punta sentsorizatu hori proba batzuen bidez balioztatzen da, eta neurketa-errore onak lortzen diraIbiltzeko laguntza gailuaren erabiltzaileak egindako jarduera fisikoa ezagutu ahal izateko, 5 jarduera fisiko sailkatzeko gai den sailkatzaile bat egiten da. Machine Learningen teknikak helburu horretarako erabiltzea proposatzen da. Teknika horiek erabili ahal izateko, datuak gertaeren arabera leihoetan segmentatzea proposatzen da, leiho bakoitzeko hainbat ezaugarri sortzeko. Ezaugarrien kopurua oso handia denez, Random Forest erabiliz dimentsionaltasuna murriztea proposatzen da, garrantzitsuenak erabili ahal izateko. Ezaugarri garrantzitsu horiek ezagutu ondoren, K-Nearest Neighbors (K-NN), Support Vector Machine (SVM) eta Artificial Neural Network (ANN) erabiltzea proposatzen da, ibiltzeko, azkar ibiltzeko, eskailerak igotzeko, eskailerak jaisteko eta geldirik egoteko jarduera fisikoak sailkatzeko. Egindako sailkatzaileak elkarren artean alderatzen dira, %92 eta %97 arteko emaitzak lortuz.Gainera, erorikoak detektatzeko punta sentsorizatua erabiltzea proposatzen da. Erorikoa detektagailu horrek, jarduera fisikoen sailkatzaileak bezala, metodologia bati jarraitzen dio. Metodologia horretan, lehenik eta behin, datuak leihoetan zatitzen dira, ondoren zenbait ezaugarri sortzeko. Ondoren, dimentsionaltasun murrizketa eta SVMn oinarritutako detektagailua egiten dira. Sailkatzaile hori bi moduluren arabera egiten da; modulu batek ibiltzeko laguntza gailua erori denean detektatzeko balio du, eta besteak ibiltzeko laguntza gailua duen pertsona erori denean detektatzeko. F-Score-ren 0.96tik gorako emaitzak lortzen dira. Azkenik, sentsorizatutako puntan oinarritutako detektagailuak lortutako emaitzak sentsore eramangarrien bidez lortutako emaitzekin alderatzen dira

Evaluation of a sensorized tip to detect gait pattern changes

[Resumen] Las terapias personalizadas han demostrado ser eficaces para mejorar las capacidades físicas, y por tanto, la calidad de vida de las personas con problemas de movilidad. Sin embargo, para diseñar dichas terapias, es necesario conocer el estado funcional de cada paciente y detectar los cambios que puedan ocurrir en él. Los sistemas tradicionales de evaluación suelen requerir tiempo y dedicación por parte de los especialistas, por lo que la periodicidad entre las sesiones suele ser elevada. Ante esta problemática, varios estudios han propuesto emplear dispositivos de ayuda técnica como sistemas de monitorización para extraer indicadores que ayuden al terapeuta en dicha evaluación. En base a ello, en este trabajo, se evalúa la capacidad de una contera sensorizada para detectar cambios en el patrón personal de marcha, tanto en escenarios simulados, como en personas con esclerosis múltiple.[Abstract] Personalized therapies have proven to be effective in improving the physical abilities, and therefore, the quality of life of people with motor impairments. However, in order to design such therapies, it is necessary to know the functional state of each patient and to detect any changes that may occur. Traditional assessment systems require time and dedication, so the frequency between sessions is often low. In order to overcome these limitations, several studies have proposed the use of assistive devices for walking as monitoring systems to extract indicators that can help the therapist in the assessment. Based on this, the present study evaluates the capacity of a sensorized tip to detect changes in the gait pattern, both in simulated scenarios and in people with multiple sclerosis.Este trabajo ha sido financiado por la Universidad del País Vasco UPV/EHU (GIU19/045), Gobierno Vasco (IT1726-22), FEDER/Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación/PID2020-112667RB-I00, y la ayuda FPU del Ministerio de Ciencia, Innovación y Universidades (FPU19/04874).Universidad del País Vasco/Euskal Herriko Unibertsitatea; GIU 19/045Gobierno Vasco; IT1726-2

Application of Machine Learning Techniques for Activity Classification Using an Intelligent Crutch for Multiple Sclerosis

[Resumen] El nivel de actividad física diaria que un paciente de Esclerosis Múltiple es capaz de realizar se ha demostrado que es una importante fuente de información para el seguimiento de la enfermedad y la adaptación individualizada de las terapias. Así, en este trabajo se propone el diseño de un clasificador de actividades de la vida diaria, realizado mediante la combinación de dos técnicas de Inteligencia Artificial (RandomForest y Redes Neuronales Artificiales), el cual podría facilitar información de valor a las y los terapeutas.[Abstract] The level of daily physical activity that a Multiple Sclerosis patient is able to perform has been shown to be an important source of information in order to monitorise the disease and adapt therapies in a personalised way. Consequently, in this work a novel desing of a daily physical activity classifier is proposed by the combination of two Artificial Intelligence techniques (a RandomForest and Artificial Neural Networks), which can provide an useful information for the therapists.Universidad del País Vasco/Euskal Herriko Unibertsitatea; GIU19/045Universidad del País Vasco/Euskal Herriko Unibertsitatea; PIF18/067Este trabajo ha sido financiado por la Universidad del País Vasco UPV/EHU (GIU19/045), FEDER/Ministerio de Ciencia, Innovación y Universidades – Agencia Estatal de Investigación/DPDPI2017-82694-R y Universidad del País Vasco UPV/EHU (PIF18/067)https://doi.org/10.17979/spudc.978849749804