A Dynamical System-based Approach to Modeling Stable Robot Control Policies via Imitation Learning

Despite tremendous advances in robotics, we are still amazed by the proficiency with which humans perform movements. Even new waves of robotic systems still rely heavily on hardcoded motions with a limited ability to react autonomously and robustly to a dynamically changing environment. This thesis focuses on providing possible mechanisms to push the level of adaptivity, reactivity, and robustness of robotic systems closer to human movements. Specifically, it aims at developing these mechanisms for a subclass of robot motions called “reaching movements”, i.e. movements in space stopping at a given target (also referred to as episodic motions, discrete motions, or point-to-point motions). These reaching movements can then be used as building blocks to form more advanced robot tasks. To achieve a high level of proficiency as described above, this thesis particularly seeks to derive control policies that: 1) resemble human motions, 2) guarantee the accomplishment of the task (if the target is reachable), and 3) can instantly adapt to changes in dynamic environments. To avoid manually hardcoding robot motions, this thesis exploits the power of machine learning techniques and takes an Imitation Learning (IL) approach to build a generic model of robot movements from a few examples provided by an expert. To achieve the required level of robustness and reactivity, the perspective adopted in this thesis is that a reaching movement can be described with a nonlinear Dynamical System (DS). When building an estimate of DS from demonstrations, there are two key problems that need to be addressed: the problem of generating motions that resemble at best the demonstrations (the “how-to-imitate” problem), and most importantly, the problem of ensuring the accomplishment of the task, i.e. reaching the target (the “stability” problem). Although there are numerous well-established approaches in robotics that could answer each of these problems separately, tackling both problems simultaneously is challenging and has not been extensively studied yet. This thesis first tackles the problem mentioned above by introducing an iterative method to build an estimate of autonomous nonlinear DS that are formulated as a mixture of Gaussian functions. This method minimizes the number of Gaussian functions required for achieving both local asymptotic stability at the target and accuracy in following demonstrations. We then extend this formulation and provide sufficient conditions to ensure global asymptotic stability of autonomous DS at the target. In this approach, an estimation of the underlying DS is built by solving a constraint optimization problem, where the metric of accuracy and the stability conditions are formulated as the optimization objective and constraints, respectively. In addition to ensuring convergence of all motions to the target within the local or global stability regions, these approaches offer an inherent adaptability and robustness to changes in dynamic environments. This thesis further extends the previous approaches and ensures global asymptotic stability of DS-based motions at the target independently of the choice of the regression technique. Therefore, it offers the possibility to choose the most appropriate regression technique based on the requirements of the task at hand without compromising DS stability. This approach also provides the possibility of online learning and using a combination of two or more regression methods to model more advanced robot tasks, and can be applied to estimate motions that are represented with both autonomous and non-autonomous DS. Additionally, this thesis suggests a reformulation to modeling robot motions that allows encoding of a considerably wider set of tasks ranging from reaching movements to agile robot movements that require hitting a given target with a specific speed and direction. This approach is validated in the context of playing the challenging task of minigolf. Finally, the last part of this thesis proposes a DS-based approach to realtime obstacle avoidance. The presented approach provides a modulation that instantly modifies the robot’s motion to avoid collision with multiple static and moving convex obstacles. This approach can be applied on all the techniques described above without affecting their adaptability, swiftness, or robustness. The techniques that are developed in this thesis have been validated in simulation and on different robotic platforms including the humanoid robots HOAP-3 and iCub, and the robot arms KATANA, WAM, and LWR. Throughout this thesis we show that the DS-based approach to modeling robot discrete movements can offer a high level of adaptability, reactivity, and robustness almost effortlessly when interacting with dynamic environments

The Effect of Hibiscus Sabdariffa on Lipid Profile, Creatinine, and Serum Electrolytes: A Randomized Clinical Trial

Background. Hibiscus Sabdariffa L. (HS), a member of malvaceae family, is a medicinal plant with a worldwide fame. Its effect on reducing serum lipids is mentioned in several studies. The purpose of this study was to assess the efficacy of this plant in reducing the serum's lipids in hypertensive patients. Materials and Methods. Ninety hypertensive patients were randomly assigned to receive Hibiscus Sabdariffa (HS) tea or black tea for 15 days. The patients were asked to drink the tea within 20 minutes following its preparation. This process had to be repeated two times, daily. Patient's FBS and lipid profile were collected at the first visit day (day 0) and on the day 30. Results. There was no significant differences between pre and post experiment values within the two groups. An upward trend in total cholesterol, HDL, and LDL cholesterol was evident in both groups. The increase in total and HDL cholesterol in both groups relative to their initial values were significant. Conclusion. Hibiscus Sabdariffa is probably a safe medicinal plant. No significant harmful changes in cholesterol, triglyceride, BUN, serum creatinine, Na and K levels were observed within 15 days after the discontinuation of the medication

Sharing health data to create value: A systematic review

Objective: With the introduction of digital crypto currency, the worth of data has been obvious to everybody. Of course, this research is in the sphere of medicine. The goal of this research is to perform a comprehensive evaluation of studies in the area of health information system data sharing and secondary uses of health data, with the goal of generating value in multiple dimensions by sharing these data. Material & Methods: The researchers used the intelligent Web of science and IEEE search engines to conduct a systematic evaluation of English-language keyword searches. Two tactics have been studied in this respect, one using keywords related to "health data value" and the other using terms linked to "health data sharing." Results: Although several studies have suggested solutions for decreasing obstacles to health information system data sharing, they have often simply addressed the problem, according to one criticism of the papers examined. The sharing barrier has no remedy, and its relevance is simply emphasized. Conclusion: It was discovered that not all of the successful components of data sharing, particularly data from health information systems, were addressed in the evaluated studies.                     &nbsp

The derivatives of the SEDS optimization cost function and constraints with respect to the learning parameters

This technical report provides supplementary information for the optimization problems defined for Stable Estimator of Dynamical Systems (SEDS). Reading of this report is not necessary for researchers who only want to use SEDS learning algorithm. The report is aimed at helping those persons who want to develop SEDS, or to write their own optimization program. All the formulations reported here are developed for SEDS models; however, they can also be used for general Gaussian Mixture Model (GMM) formulations. In the case of the latter, they should be slightly modified to consider the general form of GMM. Hopefully, the report should be clear enough to help readers in that

BM: An Iterative Method to Learn Stable Non-Linear Dynamical Systems with Gaussian Mixture Models

We model the dynamics of non-linear discrete (i.e. point-to- point) robot motions as a time-independent system described by an autonomous dynamical system (DS). We propose an iterative algorithm to estimate the form of the DS through a mixture of Gaussian distributions. We prove that the resulting model is asymptotically stable at the target. We validate the accuracy of the model on a library of 2D human motions and to learn a control policy through human demonstrations for two multi- degrees of freedom robots. We show the real-time adaptation to perturbations of the learned model when controlling the two kinematically-driven robots

Melatonin inhibits endothelin-1 and induces endothelial nitric oxide synthase genes expression throughout hepatic ischemia/reperfusion in rats

The production of reactive oxygen species (ROS) and dysfunction of vasculature play a central role in the pathophysiology of hepatic ischemia/reperfusion (I/R) injury. The aim of this study was to evaluate the beneficial effects of melatonin on reducing liver I/R injury in rats. Four study groups were formed: (1) saline - administered, control group (Control), (2) melatonin-administered group (MEL), (3) saline -administered I/R group (I/R) and (4) melatonin-administered I/R group (MEL+ I/R). Melatonin was injected intraperitoneally (15 mg/kg) 20 min before ischemia and immediately after reperfusion. After reperfusion, blood and ischemic liver tissues were collected. The group subjected to ischemia showed a significant increase in the serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels, as well as an increase in hepatic malondialdehyde (MDA) concentration. These increases were significantly inhibited by melatonin. Although, I/R augmented the endothelin-1 (ET-1) gene expression and the level of big endothelin-1 (big ET-1) in liver tissue, melatonin attenuated these increases. Conversely, non-significant decrease in endothelial nitric oxide synthase (eNOS) mRNA expression in I/R group was significantly elevated by melatonin in MEL+ I/R group. Melatonin exerts beneficial effects on ischemia/reperfusion liver injury through its anti-oxidative function as well as regulation of hepatic microcirculation.Key words: Melatonin, oxidative stress, ischemia/reperfusion injury, endothelin and nitric oxide synthase

IMAC: An Interference-aware Duty-cycle MAC Protocol for Wireless Sensor Networks Employing Multipath Routing

Abstract-The main source of energy consumption in the current MAC protocols for wireless sensor networks is idle listening. To mitigate this problem, duty cycling is used. However, it increases data delivery latency. In this paper, we propose an Interference-aware duty-cycle MAC (IMAC) protocol for wireless sensor networks that uses cross-layer information to reserve multiple paths for each source and send data packets along them efficiently. IMAC also handles the existing interference between these paths such that data packets can be delivered in the minimum required number of cycles. Simulation results in ns-2 show that the proposed algorithm has an average reduction of 49% in data delivery latency compared to a current solution called RMAC

LayeredCast -A Hybrid Peer-to-Peer Live Layered Video Streaming Protocol

Abstract-Peer-to-Peer overlay networks are an attractive foundation for video streaming. However, live Peer-to-Peer media streaming systems face many challenges such as bandwidth heterogeneity, node churn, and selfish nodes. Although many tree based and mesh based streaming protocols have been proposed, each has its own drawbacks such as unreliability and unfairness in tree based and long startup delay and complex scheduling in mesh based protocols. In this paper, we propose a new video streaming protocol called LayeredCast main features of which are: 1) Hybrid: Drawbacks of the simple approaches are compensated using a hybrid of mesh and tree overlays. 2) Layered Video: Provides an adaptive scheme to enhance the video quality using a layered video codec for heterogeneous clients. 3) QoS: LayeredCast scheduling aims at moving complexity of Multi-Service network core to the network clients application layer, thus providing better QoS over simple regular networks. LayeredCast's tree network pushes the base layer to all peers while the enhancement layers and missing base layer segments are pulled over a mesh network by peers with extra bandwidth using a new data-driven scheduling scheme. We have evaluated the performance of LayeredCast on an innovative simulation framework. Simulation results verify better performance of LayeredCast in term of decodable video frames over CoolStreaming, especially when network resources are limited

Health Data Sharing with the Goal of Value Creation; Trying to Develop a Framework Using Qualitative Content Analysis

Introduction: Within the field of data sharing, discussions surrounding privacy concerns and big data management are extensive. This study aimed to provide a comprehensive framework for health data sharing with the objective of creating value. Methods: This study is a qualitative content analysis, which was conducted using a combination of written sources through a systematic review method, in conjunction with content derived from interviews with experts in information technology and healthcare within hospital and emergency settings. Grounded theory serves as the qualitative methodology, involving three coding phases: open, axial, and selective, facilitated by MAXQDA software. Results: Qualitative content analysis of the interviews revealed seven main (core) categories and 44 subcategories as driving factors in promoting healthcare data sharing. Simultaneously, inhibiting factors resulted in six main categories and 36 subcategories. The driving factors encompassed technology, education, patient management improvement, data utilization for various purposes, data-related considerations, legal and regulatory aspects, and health-related factors. Conversely, inhibiting factors encompassed security and privacy concerns, legal issues, external organizational influences, monitoring and control activities, financial considerations, and inter-organizational challenges. Conclusion: This study has identified key driving and inhibiting factors that influence the sharing of healthcare data. These factors contribute to a more comprehensive understanding of the dynamics surrounding data sharing within the healthcare information system

Effect of general health status on chronicity of low back pain in industrial workers

Recognizing patients at a higher risk of developing chronic low back pain (LBP) is important in industrial medicine. This study aimed to assess the power and quality of General Health Questionnaire (GHQ) for prediction of the odds of chronicity of acute LBP. This study was conducted on industrial workers. All subjects with acute LBP who met the inclusion criteria were enrolled. Demographic characteristics, occupational, physical, and mental parameters and the general health status of subjects were evaluated; they were followed up for developing chronic LBP for one year. Cigarette smoking, high body mass index, job stress, physical load and high GHQ scores were found to be the risk factors for the progression of acute LBP to chronic LBP (P0.05). High GHQ score can be a risk factor for progression of acute LBP to chronic LBP. The GHQ in combination with the Job Content Questionnaire can be used as a quick and simple screening tool for detection of subjects at high risk of chronic LBP when evaluating acute LBP in an occupational setting. © 2016 Tehran University of Medical Sciences. All rights reserved.