scholarly journals Inverse Kinematics for Upper Limb Compound Movement Estimation in Exoskeleton-Assisted Rehabilitation

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Camilo Cortés ◽  
Ana de los Reyes-Guzmán ◽  
Davide Scorza ◽  
Álvaro Bertelsen ◽  
Eduardo Carrasco ◽  
...  
Keyword(s):  
Inverse Kinematics ◽  
Ground Truth ◽  
Computational Method ◽  
Accurate Estimation ◽  
Joint Angles ◽  
Kinematic Chains ◽  
Cord Injury ◽  
Posture Estimation ◽  
Patient Groups ◽  
Parallel Kinematic

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.

scholarly journals Optical Enhancement of Exoskeleton-Based Estimation of Glenohumeral Angles

2016 ◽  
Vol 2016 ◽  
pp. 1-20 ◽  
Author(s):  
Camilo Cortés ◽  
Luis Unzueta ◽  
Ana de los Reyes-Guzmán ◽  
Oscar E. Ruiz ◽  
Julián Flórez
Keyword(s):  
Estimation Method ◽  
Position Estimation ◽  
Accurate Estimation ◽  
Joint Angles ◽  
Estimation Errors ◽  
Marker Position ◽  
Human Movements ◽  
Posture Estimation ◽  
Kinematic Models ◽  
Method Accuracy

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.

scholarly journals MF2C3: Multi-Feature Fuzzy Clustering to Enhance Cell Colony Detection in Automated Clonogenic Assay Evaluation

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 773 ◽  
Author(s):  
Carmelo Militello ◽  
Leonardo Rundo ◽  
Luigi Minafra ◽  
Francesco Paolo Cammarata ◽  
Marco Calvaruso ◽  
...  
Keyword(s):  
Clonogenic Assay ◽  
Ground Truth ◽  
General Purpose ◽  
Computational Method ◽  
Counting Procedure ◽  
Proliferative Effect ◽  
Biological Studies ◽  
Biological Technique ◽  
Fuzzy C Means Clustering ◽  
Cell Colony

A clonogenic assay is a biological technique for calculating the Surviving Fraction (SF) that quantifies the anti-proliferative effect of treatments on cell cultures: this evaluation is often performed via manual counting of cell colony-forming units. Unfortunately, this procedure is error-prone and strongly affected by operator dependence. Besides, conventional assessment does not deal with the colony size, which is generally correlated with the delivered radiation dose or administered cytotoxic agent. Relying upon the direct proportional relationship between the Area Covered by Colony (ACC) and the colony count and size, along with the growth rate, we propose MF2C3, a novel computational method leveraging spatial Fuzzy C-Means clustering on multiple local features (i.e., entropy and standard deviation extracted from the input color images acquired by a general-purpose flat-bed scanner) for ACC-based SF quantification, by considering only the covering percentage. To evaluate the accuracy of the proposed fully automatic approach, we compared the SFs obtained by MF2C3 against the conventional counting procedure on four different cell lines. The achieved results revealed a high correlation with the ground-truth measurements based on colony counting, by outperforming our previously validated method using local thresholding on L*u*v* color well images. In conclusion, the proposed multi-feature approach, which inherently leverages the concept of symmetry in the pixel local distributions, might be reliably used in biological studies.

A Model of the Human Spine: Forward and Inverse Kinematics

1992 ◽  
Author(s):  
Sunil Kumar Agrawal ◽  
Siyan Li ◽  
Glen Desmier
Keyword(s):  
Range Of Motion ◽  
Inverse Kinematics ◽  
Degrees Of Freedom ◽  
Kinematic Model ◽  
Joint Angles ◽  
Human Spine ◽  
Forward And Inverse Kinematics ◽  
Position And Orientation ◽  
Three Degrees Of Freedom ◽  
Adjacent Vertebra

Abstract The human spine is a sophisticated mechanism consisting of 24 vertebrae which are arranged in a series-chain between the pelvis and the skull. By careful articulation of these vertebrae, a human being achieves fine motion of the skull. The spine can be modeled as a series-chain with 24 rigid links, the vertebrae, where each vertebra has three degrees-of-freedom relative to an adjacent vertebra. From the studies in the literature, the vertebral geometry and the range of motion between adjacent vertebrae are well-known. The objectives of this paper are to present a kinematic model of the spine using the available data in the literature and an algorithm to compute the inter vertebral joint angles given the position and orientation of the skull. This algorithm is based on the observation that the backbone can be described analytically by a space curve which is used to find the joint solutions..

Drive System Sizing of a 6-DOF Parallel Robotic Platform

2014 ◽  
Author(s):  
Hermes Giberti ◽  
Davide Ferrari
Keyword(s):  
Optimal Solution ◽  
Parallel Robot ◽  
Drive System ◽  
Pareto Optimal ◽  
Hardware In The Loop ◽  
Motion Simulation ◽  
Inverse Dynamic ◽  
Kinematic Chains ◽  
Fixed Base ◽  
Parallel Kinematic

In this work, it is considered a 6-DoF robotic device intended to be applied for hardware-in-the-loop (HIL) motion simulation with wind tunnel models. The requirements have led to a 6-PUS parallel robot whose linkages consist of six closed-loop kinematic chains, connecting the fixed base to the mobile platform with the same sequence of joints: actuated Prism (P), Universal (U), and Spherical (S). As is common for parallel kinematic manipulators (PKMs), the actual performances of the robot depend greatly on its dimensions. Therefore, a kinematic synthesis has been performed and several Pareto-optimal solutions have been obtained through a multi-objective optimization of the machine geometric parameters, using a genetic algorithm. In this paper, the inverse dynamic analysis of the robot is presented. Then, the results are used for the mechanical sizing of the drive system, comparing belt- to screw-driven units and selecting the motor-reducer groups. Finally, the best compromise Pareto-optimal solution is definitely chosen.

scholarly journals A Recursive Algorithm for the Forward Kinematic Analysis of Robotic Systems Using Euler Angles

Robotics ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 15
Author(s):  
Fernando Gonçalves ◽  
Tiago Ribeiro ◽  
António Fernando Ribeiro ◽  
Gil Lopes ◽  
Paulo Flores
Keyword(s):  
Recursive Algorithm ◽  
Kinematic Chain ◽  
Robotic Systems ◽  
Mobile Manipulator ◽  
Euler Angles ◽  
Joint Angles ◽  
Main Research ◽  
Kinematic Chains ◽  
Research Fields ◽  
Forward Kinematic

Forward kinematics is one of the main research fields in robotics, where the goal is to obtain the position of a robot’s end-effector from its joint parameters. This work presents a method for achieving this using a recursive algorithm that builds a 3D computational model from the configuration of a robotic system. The orientation of the robot’s links is determined from the joint angles using Euler Angles and rotation matrices. Kinematic links are modeled sequentially, the properties of each link are defined by its geometry, the geometry of its predecessor in the kinematic chain, and the configuration of the joint between them. This makes this method ideal for tackling serial kinematic chains. The proposed method is advantageous due to its theoretical increase in computational efficiency, ease of implementation, and simple interpretation of the geometric operations. This method is tested and validated by modeling a human-inspired robotic mobile manipulator (CHARMIE) in Python.

scholarly journals Agronomic Traits Analysis of Ten Winter Wheat Cultivars Clustered by UAV-Derived Vegetation Indices

2020 ◽  
Vol 12 (2) ◽  
pp. 249 ◽  
Author(s):  
Stefano Marino ◽  
Arturo Alvino
Keyword(s):  
Winter Wheat ◽  
Agronomic Traits ◽  
Vegetation Indices ◽  
Ground Truth ◽  
Accurate Estimation ◽  
Wheat Cultivars ◽  
Yield Data ◽  
Related Data ◽  
High Data ◽  
Winter Wheat Cultivars

Timely and accurate estimation of crop yield variability before harvest is crucial in precision farming. This study is aimed to evaluate the ability of cluster analysis based on Vegetation Indices (VIs) that were obtained from UAVs to predict the spatial variability on agronomic traits of ten winter wheat cultivars. Five VIs groups were identified and the ground truth yield-related data were analyzed for clusters validation. The yield data revealed a value of 6.91 t ha−1 for the first cluster with the highest VIs value and a decrease of −12%, −21%, and −27% for the 2nd, 3rd, and 4th clusters; respectively; the 5th cluster; with the lowest VIs value showed the lower yield values (4 t ha−1). Agronomic traits, such as dry biomass, spike numbers, and weight were grouped according to VIs clusters and analyzed and showed the same trends. The analysis of spatial distribution and agronomic data of the ten cultivars within the single clusters highlighted that the most productive varieties showing a greater value of spike weight and numbers and a greater presence of areas with high values of VIs and vice versa the less productive once, though two cultivars showed productions not linked to cluster classification and high data range variability were recorded. Cluster identified by high-resolution UAV vegetation indices can be a valid strategy although its effectiveness is closely linked to the cultivar component and, therefore, requires extensive verification.

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