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Epelde G.,eHealth and Biomedical Applications | Mujika A.,eHealth and Biomedical Applications | Leskovsky P.,eHealth and Biomedical Applications | de Mauro A.,eHealth and Biomedical Applications
Advances in Intelligent Systems and Computing | Year: 2015

This paper presents a public access architecture design of a remote lab developed for remote experimentation with an emulation of the nervous system of C. elegans nematode. The objective of this system is to emulate the neural system and the virtual embodiment of a C. elegans worm and to let the biologists’ and neuroscientists’ community remotely evaluate different neuronal models, and observe and analyse the behaviour of the virtual worm corresponding to the neuronal model being evaluated. This paper first analyses related remote lab technologies which then is followed by the description of the adopted architecture design, selected cloud deployment option and remote user interface approach. © Springer International Publishing Switzerland 2015.


Cortes C.,EHealth and Biomedical Applications | Cortes C.,EAFIT University | De Los Reyes-Guzman A.,National Hospital for Spinal Cord Injury | Scorza D.,EHealth and Biomedical Applications | And 5 more authors.
BioMed Research International | Year: 2016

Robot-Assisted Rehabilitation (RAR) is relevant for treating patients affected by nervous system injuries (e.g., stroke and spinal cord injury). The accurate estimation of the joint angles of the patient limbs in RAR is critical to assess the patient improvement. The economical prevalent method to estimate the patient posture in Exoskeleton-based RAR is to approximate the limb joint angles with the ones of the Exoskeleton. This approximation is rough since their kinematic structures differ. Motion capture systems (MOCAPs) can improve the estimations, at the expenses of a considerable overload of the therapy setup. Alternatively, the Extended Inverse Kinematics Posture Estimation (EIKPE) computational method models the limb and Exoskeleton as differing parallel kinematic chains. EIKPE has been tested with single DOF movements of the wrist and elbow joints. This paper presents the assessment of EIKPE with elbow-shoulder compound movements (i.e., object prehension). Ground-truth for estimation assessment is obtained from an optical MOCAP (not intended for the treatment stage). The assessment shows EIKPE rendering a good numerical approximation of the actual posture during the compound movement execution, especially for the shoulder joint angles. This work opens the horizon for clinical studies with patient groups, Exoskeleton models, and movements types. © 2016 Camilo Cortés et al.


Cortes C.,EHealth and Biomedical Applications | Cortes C.,EAFIT University | Ardanza A.,EHealth and Biomedical Applications | Molina-Rueda F.,Rey Juan Carlos University | And 6 more authors.
BioMed Research International | Year: 2014

New motor rehabilitation therapies include virtual reality (VR) and robotic technologies. In limb rehabilitation, limb posture is required to (1) provide a limb realistic representation in VR games and (2) assess the patient improvement. When exoskeleton devices are used in the therapy, the measurements of their joint angles cannot be directly used to represent the posture of the patient limb, since the human and exoskeleton kinematic models differ. In response to this shortcoming, we propose a method to estimate the posture of the human limb attached to the exoskeleton. We use the exoskeleton joint angles measurements and the constraints of the exoskeleton on the limb to estimate the human limb joints angles. This paper presents (a) the mathematical formulation and solution to the problem, (b) the implementation of the proposed solution on a commercial exoskeleton system for the upper limb rehabilitation, (c) its integration into a rehabilitation VR game platform, and (d) the quantitative assessment of the method during elbow and wrist analytic training. Results show that this method properly estimates the limb posture to (i) animate avatars that represent the patient in VR games and (ii) obtain kinematic data for the patient assessment during elbow and wrist analytic rehabilitation. © 2014 Camilo Cortés et al.


Echeverria R.,eHealth and Biomedical Applications | Echeverria R.,Public University of Navarra | Cortes C.,eHealth and Biomedical Applications | Cortes C.,EAFIT University | And 4 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016

Algorithms based on the unscented Kalman filter (UKF) have been proposed as an alternative for registration of point clouds obtained from vertebral ultrasound (US) and computerised tomography (CT) scans, effectively handling the US limited depth and low signal to-noise ratio. Previously proposed methods are accurate, but their convergence rate is considerably reduced with initial misalignments of the datasets greater than 30º or 30 mm. We propose a novel method which increases robustness by adding a coarse alignment of the datasets’ principal components and batch-based point inclusions for the UKF. Experiments with simulated scans with full coverage of a single vertebra show the method’s capability and accuracy to correct misalignments as large as 180º and 90 mm. Furthermore, the method registers datasets with varying degrees of missing data and datasets with outlier points coming from adjacent vertebrae. © Springer International Publishing Switzerland 2016.


Cortes C.,EHealth and Biomedical Applications | Cortes C.,EAFIT University | Unzueta L.,EHealth and Biomedical Applications | De Los Reyes-Guzman A.,National Hospital for Spinal Cord Injury | And 2 more authors.
Applied Bionics and Biomechanics | Year: 2016

In Robot-Assisted Rehabilitation (RAR) the accurate estimation of the patient limb joint angles is critical for assessing therapy efficacy. In RAR, the use of classic motion capture systems (MOCAPs) (e.g., optical and electromagnetic) to estimate the Glenohumeral (GH) joint angles is hindered by the exoskeleton body, which causes occlusions and magnetic disturbances. Moreover, the exoskeleton posture does not accurately reflect limb posture, as their kinematic models differ. To address the said limitations in posture estimation, we propose installing the cameras of an optical marker-based MOCAP in the rehabilitation exoskeleton. Then, the GH joint angles are estimated by combining the estimated marker poses and exoskeleton Forward Kinematics. Such hybrid system prevents problems related to marker occlusions, reduced camera detection volume, and imprecise joint angle estimation due to the kinematic mismatch of the patient and exoskeleton models. This paper presents the formulation, simulation, and accuracy quantification of the proposed method with simulated human movements. In addition, a sensitivity analysis of the method accuracy to marker position estimation errors, due to system calibration errors and marker drifts, has been carried out. The results show that, even with significant errors in the marker position estimation, method accuracy is adequate for RAR. © 2016 Camilo Cortés et al.


Cortes C.,eHealth and Biomedical Applications | Cortes C.,EAFIT University | Cortes C.,Biodonostia Health Research Center | Kabongo L.,eHealth and Biomedical Applications | And 5 more authors.
Smart Innovation, Systems and Technologies | Year: 2016

The use of ultrasound (US) imaging as an alternative for real-time computer assisted interventions is increasing. Growing usage of US occurs despite of US lower imaging quality compared to other techniques and its difficulty to be used with image analysis algorithms. On the other hand, it is still difficult to find sufficient data to develop and assess solutions for navigation, registration and reconstruction at medical research level. At present, manually acquired available datasets present significant usability obstacles due to their lack of control of acquisition conditions, which hinders the study and correction of algorithm design parameters. To address these limitations, we present a database of robotically acquired sequences of US images from medical phantoms, ensuring the trajectory, pose and force control of the probe. The acquired dataset is publicly available, and it is specially useful for designing and testing registration and volume reconstruction algorithms. © Springer International Publishing Switzerland 2016.


Cortes C.A.,EHealth and Biomedical Applications | Cortes C.A.,EAFIT University | Barandiaran I.,EHealth and Biomedical Applications | Ruiz O.E.,EAFIT University | De Mauro A.,EHealth and Biomedical Applications
Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2013 | Year: 2013

In the context of surgery, it is very common to face challenging scenarios during the preoperative plan implementation. The surgical technique's complexity, the human anatomical variability and the occurrence of unexpected situations generate issues for the intervention's goals achievement. To support the surgeon, robotic systems are being integrated to the operating room. However, current commercial solutions are specialized for a particular technique or medical application, being difficult to integrate with other systems. Thus, versatile and modular systems are needed to conduct several procedures and to help solving the problems that surgeons face. This article aims to describe the implementation of a robotic research platform prototype that allows novel applications in the field of image-guided surgery. In particular, this research is focused on the topics of medical image acquisition during surgery, patient registration and surgical/medical equipment operation. In this paper, we address the implementation of the general purpose teleoperation and path following modes of the platform, which constitute the base of future developments. Also, we discuss relevant aspects of the system, as well as future directions and application fields to investigate.


PubMed | eHealth and Biomedical Applications, EAFIT University and National Hospital for Spinal Cord Injury
Type: | Journal: BioMed research international | Year: 2016

Robot-Assisted Rehabilitation (RAR) is relevant for treating patients affected by nervous system injuries (e.g., stroke and spinal cord injury). The accurate estimation of the joint angles of the patient limbs in RAR is critical to assess the patient improvement. The economical prevalent method to estimate the patient posture in Exoskeleton-based RAR is to approximate the limb joint angles with the ones of the Exoskeleton. This approximation is rough since their kinematic structures differ. Motion capture systems (MOCAPs) can improve the estimations, at the expenses of a considerable overload of the therapy setup. Alternatively, the Extended Inverse Kinematics Posture Estimation (EIKPE) computational method models the limb and Exoskeleton as differing parallel kinematic chains. EIKPE has been tested with single DOF movements of the wrist and elbow joints. This paper presents the assessment of EIKPE with elbow-shoulder compound movements (i.e., object prehension). Ground-truth for estimation assessment is obtained from an optical MOCAP (not intended for the treatment stage). The assessment shows EIKPE rendering a good numerical approximation of the actual posture during the compound movement execution, especially for the shoulder joint angles. This work opens the horizon for clinical studies with patient groups, Exoskeleton models, and movements types.


PubMed | eHealth and Biomedical Applications, EAFIT University and National Hospital for Spinal Cord Injury
Type: | Journal: Applied bionics and biomechanics | Year: 2016

In Robot-Assisted Rehabilitation (RAR) the accurate estimation of the patient limb joint angles is critical for assessing therapy efficacy. In RAR, the use of classic motion capture systems (MOCAPs) (e.g., optical and electromagnetic) to estimate the Glenohumeral (GH) joint angles is hindered by the exoskeleton body, which causes occlusions and magnetic disturbances. Moreover, the exoskeleton posture does not accurately reflect limb posture, as their kinematic models differ. To address the said limitations in posture estimation, we propose installing the cameras of an optical marker-based MOCAP in the rehabilitation exoskeleton. Then, the GH joint angles are estimated by combining the estimated marker poses and exoskeleton Forward Kinematics. Such hybrid system prevents problems related to marker occlusions, reduced camera detection volume, and imprecise joint angle estimation due to the kinematic mismatch of the patient and exoskeleton models. This paper presents the formulation, simulation, and accuracy quantification of the proposed method with simulated human movements. In addition, a sensitivity analysis of the method accuracy to marker position estimation errors, due to system calibration errors and marker drifts, has been carried out. The results show that, even with significant errors in the marker position estimation, method accuracy is adequate for RAR.


PubMed | eHealth and Biomedical Applications, Rey Juan Carlos University and EAFIT University
Type: | Journal: BioMed research international | Year: 2014

New motor rehabilitation therapies include virtual reality (VR) and robotic technologies. In limb rehabilitation, limb posture is required to (1) provide a limb realistic representation in VR games and (2) assess the patient improvement. When exoskeleton devices are used in the therapy, the measurements of their joint angles cannot be directly used to represent the posture of the patient limb, since the human and exoskeleton kinematic models differ. In response to this shortcoming, we propose a method to estimate the posture of the human limb attached to the exoskeleton. We use the exoskeleton joint angles measurements and the constraints of the exoskeleton on the limb to estimate the human limb joints angles. This paper presents (a) the mathematical formulation and solution to the problem, (b) the implementation of the proposed solution on a commercial exoskeleton system for the upper limb rehabilitation, (c) its integration into a rehabilitation VR game platform, and (d) the quantitative assessment of the method during elbow and wrist analytic training. Results show that this method properly estimates the limb posture to (i) animate avatars that represent the patient in VR games and (ii) obtain kinematic data for the patient assessment during elbow and wrist analytic rehabilitation.

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