Comparison study of the lower-limb joint kinematics between an optical system of movement capture and a depth camera

Actualment vivim en una era digital, on la presencialitat de les activitats rutinàries s’està veient substituïda a través d’una comunicació en remot, gràcies sobretot a l’internet i als mitjans i l’accessibilitat cada cop més fàcil per part dels usuaris. Alguns dels exemples més comuns poden ser tant a nivell professional (reunions, treball, classes…) com a nivell social (xats, videotrucades, xarxes socials), però hi ha altres camps que també s’afavoreixen d’aquests recursos com pot ser l’àmbit de la medicina, concretament la telerehabilitació. La telerehabilitació permet als metges i als pacients fer visites i fer un seguiment dels tractaments sense la necessitat de trobar-se presencialment. L’objectiu d’aquest treball és analitzar la fiabilitat i precisió de l’aplicació Telerehab. Aquesta aplicació s’orienta a rehabilitar el moviment funcional de persones amb problemes de mobilitat, i utilitza una càmera de profunditat per capturar els moviments del pacient a nivell articular. Per a comprovar la precisió d’aquesta es va realitzar una comparativa amb un sistema de càmeres òptiques, un sistema més precís que la càmera de profunditat però alhora menys econòmic i més complex d’utilitzar. A partir d’aquí es va realitzar una valoració a nivell global. Les captures cinemàtiques d’aquest treball s’han realitzat a les instal·lacions de la facultat de l’Escola d’Enginyeria de Barcelona Est (EEBE), concretament al Laboratori d’Enginyeria Biomèdica.Actualmente vivimos en una era digital, donde la presencialidad de las actividades rutinarias se está viendo sustituida a través de una comunicación a distancia, sobre todo gracias al internet y a los medios y la accesibilidad cada vez más fácil por parte de los usuarios. Algunos de los ejemplos más comunes pueden ser tanto a nivel profesional (reuniones, trabajo, clases…) como a nivel social (chats, video llamadas, redes sociales), pero hay otros campos que también se favorecen de estos recursos como puede ser el ámbito de la medicina, concretamente la telerehabilitación. La telerehabilitación permite a los pacientes realizar visitas y realizar un seguimiento de los tratamientos sin la necesidad de encontrarse presencialmente. El objetivo de este trabajo es analizar la fiabilidad y la precisión de la aplicación Telerehab. Esta aplicación se enfoca en la rehabilitación de la movilidad funcional de las personas con problemas de movilidad y utiliza una cámara de profundidad para capturar los movimientos del paciente a nivel articular. Para comprobar la precisión de este se ha realizado una comparativa con un sistema de cámaras ópticas, un sistema más preciso que la cámara de profundidad, pero a la vez menos económico y más complejo de utilizar. A partir de ahí se realizó una valoración a nivel global. Las capturas cinemáticas de este trabajo se han realizado en las instalaciones de la facultad de la Escola d’Enginyeria de Barcelona Est (EEBE), concretamente en el Laboratorio de Ingeniería Biomédica.We currently live in a digital era, where face-to-face routine activities are being replaced by remote communication, mainly thanks to the internet and media and the increasing ease in accessibility by users. Some of the most common examples can be both at a professional level (meetings, work, classes...) such as at social level (chats, video calls, social networks), but there are other fields that also benefit from these resources such as the medical field, specifically telerehabilitation. Telerehabilitation technology allows doctors and patients to make visits and follow treatments without the need to meet in person. The aim of this work is to analyze the reliability and accuracy of Telerehab. This application aims to help functional mobility recovery in patients with mobility problems and uses a depth camera to capture patient’s joints movement. To check the accuracy of it, a comparison was performed with a system of optical cameras, more precise than the depth camera but at the same time less economical and more complex. At this point a global analysis was carried out. The kinematic captures of this work have been recorded in the Escola d’Enginyeria de Barcelona Est (EEBE) faculty facilities, specifically in the Biomedical Engineering Laboratory

Human motion capture for movement limitation analysis using an RGB-D camera in spondyloarthritis: a validation study

A human motion capture system using an RGB-D camera could be a good option to understand the trunk limitations in spondyloarthritis. The aim of this study is to validate a human motion capture system using an RGB-D camera to analyse trunk movement limitations in spondyloarthritis patients. Cross-sectional study was performed where spondyloarthritis patients were diagnosed with a rheumatologist. The RGB-D camera analysed the kinematics of each participant during seven functional tasks based on rheumatologic assessment. The OpenNI2 library collected the depth data, the NiTE2 middleware detected a virtual skeleton and the MRPT library recorded the trunk positions. The gold standard was registered using an inertial measurement unit. The outcome variables were angular displacement, angular velocity and lineal acceleration of the trunk. Criterion validity and the reliability were calculated. Seventeen subjects (54.35 (11.75) years) were measured. The Bending task obtained moderate results in validity (r=0.55–0.62) and successful results in reliability (ICC=0.80–0.88) and validity and reliability of angular kinematic results in Chair task were moderate and (r=0.60–0.74, ICC=0.61–0.72). The kinematic results in Timed Up and Go test were less consistent. The RGB-D camera was documented to be a reliable tool to assess the movement limitations in spondyloarthritis depending on the functional tasks: Bending task. Chair task needs further research and the TUG analysis was not validated.Funding for open access charge: Universidad de Málaga/CBUA. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature

Wearables for Movement Analysis in Healthcare

Quantitative movement analysis is widely used in clinical practice and research to investigate movement disorders objectively and in a complete way. Conventionally, body segment kinematic and kinetic parameters are measured in gait laboratories using marker-based optoelectronic systems, force plates, and electromyographic systems. Although movement analyses are considered accurate, the availability of specific laboratories, high costs, and dependency on trained users sometimes limit its use in clinical practice. A variety of compact wearable sensors are available today and have allowed researchers and clinicians to pursue applications in which individuals are monitored in their homes and in community settings within different fields of study, such movement analysis. Wearable sensors may thus contribute to the implementation of quantitative movement analyses even during out-patient use to reduce evaluation times and to provide objective, quantifiable data on the patients’ capabilities, unobtrusively and continuously, for clinical purposes

Image-Based Force Estimation and Haptic Rendering For Robot-Assisted Cardiovascular Intervention

Clinical studies have indicated that the loss of haptic perception is the prime limitation of robot-assisted cardiovascular intervention technology, hindering its global adoption. It causes compromised situational awareness for the surgeon during the intervention and may lead to health risks for the patients. This doctoral research was aimed at developing technology for addressing the limitation of the robot-assisted intervention technology in the provision of haptic feedback. The literature review showed that sensor-free force estimation (haptic cue) on endovascular devices, intuitive surgeon interface design, and haptic rendering within the surgeon interface were the major knowledge gaps. For sensor-free force estimation, first, an image-based force estimation methods based on inverse finite-element methods (iFEM) was developed and validated. Next, to address the limitation of the iFEM method in real-time performance, an inverse Cosserat rod model (iCORD) with a computationally efficient solution for endovascular devices was developed and validated. Afterward, the iCORD was adopted for analytical tip force estimation on steerable catheters. The experimental studies confirmed the accuracy and real-time performance of the iCORD for sensor-free force estimation. Afterward, a wearable drift-free rotation measurement device (MiCarp) was developed to facilitate the design of an intuitive surgeon interface by decoupling the rotation measurement from the insertion measurement. The validation studies showed that MiCarp had a superior performance for spatial rotation measurement compared to other modalities. In the end, a novel haptic feedback system based on smart magnetoelastic elastomers was developed, analytically modeled, and experimentally validated. The proposed haptics-enabled surgeon module had an unbounded workspace for interventional tasks and provided an intuitive interface. Experimental validation, at component and system levels, confirmed the usability of the proposed methods for robot-assisted intervention systems

Low-Cost Sensors and Biological Signals

Many sensors are currently available at prices lower than USD 100 and cover a wide range of biological signals: motion, muscle activity, heart rate, etc. Such low-cost sensors have metrological features allowing them to be used in everyday life and clinical applications, where gold-standard material is both too expensive and time-consuming to be used. The selected papers present current applications of low-cost sensors in domains such as physiotherapy, rehabilitation, and affective technologies. The results cover various aspects of low-cost sensor technology from hardware design to software optimization

Recent Advances in Motion Analysis

The advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application