by Keyword: Kinematics
Ballester, BR, Antenucci, F, Maier, M, Coolen, ACC, Verschure, PFMJ, (2021). Estimating upper-extremity function from kinematics in stroke patients following goal-oriented computer-based training Journal Of Neuroengineering And Rehabilitation 18, 186
Introduction: After a stroke, a wide range of deficits can occur with varying onset latencies. As a result, assessing impairment and recovery are enormous challenges in neurorehabilitation. Although several clinical scales are generally accepted, they are time-consuming, show high inter-rater variability, have low ecological validity, and are vulnerable to biases introduced by compensatory movements and action modifications. Alternative methods need to be developed for efficient and objective assessment. In this study, we explore the potential of computer-based body tracking systems and classification tools to estimate the motor impairment of the more affected arm in stroke patients. Methods: We present a method for estimating clinical scores from movement parameters that are extracted from kinematic data recorded during unsupervised computer-based rehabilitation sessions. We identify a number of kinematic descriptors that characterise the patients' hemiparesis (e.g., movement smoothness, work area), we implement a double-noise model and perform a multivariate regression using clinical data from 98 stroke patients who completed a total of 191 sessions with RGS. Results: Our results reveal a new digital biomarker of arm function, the Total Goal-Directed Movement (TGDM), which relates to the patients work area during the execution of goal-oriented reaching movements. The model's performance to estimate FM-UE scores reaches an accuracy of R-2: 0.38 with an error (sigma: 12.8). Next, we evaluate its reliability (r = 0.89 for test-retest), longitudinal external validity (95% true positive rate), sensitivity, and generalisation to other tasks that involve planar reaching movements (R-2: 0.39). The model achieves comparable accuracy also for the Chedoke Arm and Hand Activity Inventory (R-2: 0.40) and Barthel Index (R-2: 0.35). Conclusions: Our results highlight the clinical value of kinematic data collected during unsupervised goal-oriented motor training with the RGS combined with data science techniques, and provide new insight into factors underlying recovery and its biomarkers.
JTD Keywords: interactive feedback, motion classification, motion sensing, multivariate regression, posture monitoring, rehabilitation, stroke, Adult, Aged, Analytic method, Arm movement, Article, Barthel index, Brain hemorrhage, Cerebrovascular accident, Chedoke arm and hand activity inventory, Clinical protocol, Cognitive defect, Computer analysis, Controlled study, Convergent validity, Correlation coefficient, Disease severity, External validity, Female, Fugl meyer assessment for the upper extremity, Functional assessment, Functional status assessment, General health status assessment, Hemiparesis, Human, Interactive feedback, Ischemic stroke, Kinematics, Major clinical study, Male, Mini mental state examination, Motion classification, Motion sensing, Motor analog scale, Movement, Multivariate regression, Muscle function, Posture monitoring, Probability, Recovery, Rehabilitation, Reliability, Retrospective study, Stroke, Stroke patient, Test retest reliability, Therapy, Total goal directed movement, Upper extremities, Upper limb, Upper-limb, Wolf motor function test
Rokbani, Nizar, Casals, Alicia, Alimi, AdelM, (2015). IK-FA, a New Heuristic Inverse Kinematics Solver Using Firefly Algorithm Computational Intelligence Applications in Modeling and Control (ed. Azar, Ahmad Taher, Vaidyanathan, Sundarapandian), Springer International Publishing (Lausanne, Switzerland) 575, 369-395
In this paper, a heuristic method based on Firefly Algorithm is proposed for inverse kinematics problems in articulated robotics. The proposal is called, IK-FA. Solving inverse kinematics, IK, consists in finding a set of joint-positions allowing a specific point of the system to achieve a target position. In IK-FA, the Fireflies positions are assumed to be a possible solution for joints elementary motions. For a robotic system with a known forward kinematic model, IK-Fireflies, is used to generate iteratively a set of joint motions, then the forward kinematic model of the system is used to compute the relative Cartesian positions of a specific end-segment, and to compare it to the needed target position. This is a heuristic approach for solving inverse kinematics without computing the inverse model. IK-FA tends to minimize the distance to a target position, the fitness function could be established as the distance between the obtained forward positions and the desired one, it is subject to minimization. In this paper IK-FA is tested over a 3 links articulated planar system, the evaluation is based on statistical analysis of the convergence and the solution quality for 100 tests. The impact of key FA parameters is also investigated with a focus on the impact of the number of fireflies, the impact of the maximum iteration number and also the impact of (α, β, γ, δ) parameters. For a given set of valuable parameters, the heuristic converges to a static fitness value within a fix maximum number of iterations. IK-FA has a fair convergence time, for the tested configuration, the average was about 2.3394 × 10−3 seconds with a position error fitness around 3.116 × 10−8 for 100 tests. The algorithm showed also evidence of robustness over the target position, since for all conducted tests with a random target position IK-FA achieved a solution with a position error lower or equal to 5.4722 × 10−9.
JTD Keywords: Robotics, Inverse kinematics, Heuristics, Computational kinematics, Swarm intelligence
Hernandez Bennetts, V. M., Lilienthal, A. J., Khaliq, A. A., Pomareda Sese, V., Trincavelli, M., (2013). Towards real-world gas distribution mapping and leak localization using a mobile robot with 3d and remote gas sensing capabilities 2013 IEEE International Conference on Robotics and Automation (ICRA) (ed. Parker, Lynne E.), IEEE (Karlsruhe, Germany) , 2335-2340
Due to its environmental, economical and safety implications, methane leak detection is a crucial task to address in the biogas production industry. In this paper, we introduce Gasbot, a robotic platform that aims to automatize methane emission monitoring in landfills and biogas production sites. The distinctive characteristic of the Gasbot platform is the use of a Tunable Laser Absorption Spectroscopy (TDLAS) sensor. This sensor provides integral concentration measurements over the path of the laser beam. Existing gas distribution mapping algorithms can only handle local measurements obtained from traditional in-situ chemical sensors. In this paper we also describe an algorithm to generate 3D methane concentration maps from integral concentration and depth measurements. The Gasbot platform has been tested in two different scenarios: an underground corridor, where a pipeline leak was simulated and in a decommissioned landfill site, where an artificial methane emission source was introduced.
JTD Keywords: Laser beams, Measurement by laser beam, Mobile robots, Robot kinematics, Robot sensing systems