Heart Failure a Malignant Disease- Insights from the REFERENCE Study

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Automatic Denavit-Hartenberg parameter identification for serial manipulators

An automatic algorithm to identify Standard Denavit-Hartenberg parameters of serial manipulators is proposed. The method is based on geometric operations and dual vector algebra to process and determine the relative transformation matrices, from which it is computed the Standard Denavit-Hartenberg (DH) parameters (ai, ai, di, θi). The algorithm was tested in several serial robotic manipulators with varying kinematic structures and joint types: the KUKA LBR iiwa R800, the Rethink Robotics Sawyer, the ABB IRB 140, the Universal Robots UR3, the KINOVA MICO, and the Omron Cobra 650. For all these robotic manipulators, the proposed algorithm was capable of correctly identifying a set of DH parameters. The algorithm source code as well as the test scenarios are publicly available.FCT - Fundação para a Ciência e a Tecnologia(SFRH/BD/86499/2012

Feature extraction using Poincaré plots for gait classification

The aim of this study is to evaluate different features, extracted from a Poincaré plot of gait signals, in their ability to classify the gait of patients with neurodegenerative diseases: Parkinson’s disease (PD) and Huntington’s disease (HD). Five different features that describe gait variability were extracted from the Poincaré plots of two gait signals: stride time and percentage of stride time spent in swing phase. Among the set of extracted features, those that displayed significant differences between the two groups and were not correlated with each other, were used as input to the support vector machine classifier. It was found that all extracted features (with exception of one feature in PD vs healthy group comparison) are significantly different between healthy and pathological subjects and are suitable to discriminate them (with accuracies greater than 80%). When comparing PD vs HD, just three features were significantly different, however, a relatively good classification accuracy (around 72%) was achieved using two of them. The results demonstrate that it is feasible to apply variability measures extracted from Poincaré plots of gait data signals in gait classification problems

Robotic implantation of intracerebral electrodes for deep brain stimulation

This dissertation objective is to contribute for the development of a robotic system towards neurosurgery assistance in Deep Brain Stimulation (DBS) stereotactic procedures. Being DBS neurosurgery typically a long, physically and cognitively demanding procedure; the introduction of a robotic assistant to hold, manipulate and position instrumentation would improve the medical team working conditions and lead to better surgery outcomes. Upon understanding how could the robot be used and what robotic systems were adequate to the task, we implemented a simulation environment to emulate several industrial robot manipulators and the operating room. It was also developed each robot geometric and differential kinematic equations, and control algorithms specifically oriented for DBS neurosurgery assistance. Taking into account the operating room arrangement, the robot characteristics and task requirements, we selected the most apt industrial robotic manipulator and further elaborated on its placement and orientation to achieve utmost performance.This work has been partially financed by projects FP7 Marie Curie ITN - NETT (project no289146), FCT FCOMP-01-0124-FEDER-022674, Pest-C/MAT-UI0013/2011 (FCT grant ref. UMINHO/BIC/8/2012) and FCT PhD grant (ref. SFRH/BD/86499/2012)

Attractor dynamics approach to joint transportation by autonomous robots: theory, implementation and validation on the factory floor

This paper shows how non-linear attractor dynamics can be used to control teams of two autonomous mobile robots that coordinate their motion in order to transport large payloads in unknown environments, which might change over time and may include narrow passages, corners and sharp U-turns. Each robot generates its collision-free motion online as the sensed information changes. The control architecture for each robot is formalized as a non-linear dynamical system, where by design attractor states, i.e. asymptotically stable states, dominate and evolve over time. Implementation details are provided, and it is further shown that odometry or calibration errors are of no significance. Results demonstrate flexible and stable behavior in different circumstances: when the payload is of different sizes; when the layout of the environment changes from one run to another; when the environment is dynamice.g. following moving targets and avoiding moving obstacles; and when abrupt disturbances challenge team behavior during the execution of the joint transportation task.- This work was supported by FCT-Fundacao para a Ciencia e Tecnologia within the scope of the Project PEst-UID/CEC/00319/2013 and by the Ph.D. Grants SFRH/BD/38885/2007 and SFRH/BPD/71874/2010, as well as funding from FP6-IST2 EU-IP Project JAST (Proj. Nr. 003747). We would like to thank the anonymous reviewers, whose comments have contributed to improve the paper

Analysis of Genes Involved in Oxidative Stress and Iron Metabolism in Heart Failure: A Step Forward in Risk Stratification

Introduction: Heart failure (HF) is a clinical syndrome characterized by cardinal symptoms that may be accompanied by signs. It results from structural and/or functional abnormalities of the heart leading to elevated intracardiac pressures and/or inadequate cardiac output at rest and/or during exercise. The prevalence of iron deficiency and anemia justifies the current guidelines recommendation of screening. Genes HP, ACE, MTHFR, HFE, and CYBA are involved in oxidative mechanisms, iron metabolism, and hematologic homeostasis. This study investigates the contribution of variants Hp1/2 (HP), I/D (ACE), C677T (MTHFR), C282Y and H63D (HFE), and C242T (CYBA) to the development of HF, either independently or in epistasis. Methods: We used a database of 389 individuals, 143 HF patients, and 246 healthy controls. Genotypes were characterized through PAGE electrophoresis, PCR, PCR-RFLP, and multiplex-ARMS. Data analysis was performed with the SPSS® 26.0 software (IBM Corp., Armonk, NY). Results: We observed a significant association between the MTHFR gene and HF predisposition. The presence of allele T and genotype CT constituted risk, while genotype CC granted protection. Epistatic interactions revealed risk between genotype II of the ACE gene and genotypes CC (C282Y) or HH (H63D) of the HFE gene. Risk was also observed for interactions between genotype CC (CYBA)and genotypes 2-2 (HP), CT (MTHFR), or HH (HFE-H63D). Conclusion: We concluded that genes HP, ACE, MTHFR, HFE, and CYBA contribute to the susceptibility for HF, individually or in epistasis. This study contributes to the clarification of the role that genes involved in oxidative mechanisms and iron metabolism play in the physiopathology of HF. It is, therefore, a step forward in risk stratification and personalized medicine.info:eu-repo/semantics/publishedVersio

Discrimination of idiopathic Parkinson's disease and vascular parkinsonism based on gait time series and the levodopa effect

Idiopathic Parkinson's disease (IPD) and vascular parkinsonism (VaP) present highly overlapping phenotypes, making it challenging to distinguish between these two parkinsonian syndromes. Recent evidence suggests that gait assessment and response to levodopa medication may assist in the objective evaluation of clinical differences. In this paper, we propose a new approach for gait pattern differentiation that uses convolutional neural networks (CNNs) based on gait time series with and without the influence of levodopa medication. Wearable sensors positioned on both feet were used to acquire gait data from 14 VaP patients, 15 IPD patients, and 34 healthy subjects. An individual's gait features are affected by physical characteristics, including age, height, weight, sex, and walking speed or stride length. Therefore, to reduce bias due to intersubject variations, a multiple regression normalization approach was used to obtain gait data. Recursive feature elimination using the linear support vector machine, lasso, and random forest were applied to infer the optimal feature subset that led to the best results. CNNs were implemented by means of various hyperparameters and feature subsets. The best CNN classifiers achieved accuracies of 79.33%±6.46, 82.33%±10.62, and 86.00%±7.12 without (off state), with (on state), and with the simultaneous consideration of the effect of levodopa medication (off/on state), respectively. The response to levodopa medication improved classification performance. Based on gait time series and response to medication, the proposed approach differentiates between IPD and VaP gait patterns and reveals a high accuracy rate, which might prove useful when distinguishing other diseases related to movement disorders.This research was partially financed by NORTE2020 and FEDER within the project NORTE-01–0145-FEDER- 000026 (DeM-Deus Ex Machina) and by Portuguese Funds through FCT (Fundação para a Ciência e a Tecnologia) within the Projects UIDB/00013/2020, UIDP/00013/2020 and UIDB/00319/2020