SOCIALGYM 2.0: Simulator for Multi-Agent Social Robot Navigation in Shared Human Spaces

We present SocialGym 2, a multi-agent navigation simulator for social robot research. Our simulator models multiple autonomous agents, replicating real-world dynamics in complex environments, including doorways, hallways, intersections, and roundabouts. Unlike traditional simulators that concentrate on single robots with basic kinematic constraints in open spaces, SocialGym 2 employs multi-agent reinforcement learning (MARL) to develop optimal navigation policies for multiple robots with diverse, dynamic constraints in complex environments. Built on the PettingZoo MARL library and Stable Baselines3 API, SocialGym 2 offers an accessible python interface that integrates with a navigation stack through ROS messaging. SocialGym 2 can be easily installed and is packaged in a docker container, and it provides the capability to swap and evaluate different MARL algorithms, as well as customize observation and reward functions. We also provide scripts to allow users to create their own environments and have conducted benchmarks using various social navigation algorithms, reporting a broad range of social navigation metrics. Projected hosted at: https://amrl.cs.utexas.edu/social_gym/index.htmlComment: Submitted to RSS 202

Decentralized Multi-Robot Social Navigation in Constrained Environments via Game-Theoretic Control Barrier Functions

We present an approach to ensure safe and deadlock-free navigation for decentralized multi-robot systems operating in constrained environments, including doorways and intersections. Although many solutions have been proposed to ensure safety, preventing deadlocks in a decentralized fashion with global consensus remains an open problem. We first formalize the objective as a non-cooperative, non-communicative, partially observable multi-robot navigation problem in constrained spaces with multiple conflicting agents, which we term as social mini-games. Our approach to ensuring safety and liveness rests on two novel insights: (i) deadlock resolution is equivalent to deriving a mixed-Nash equilibrium solution to a social mini-game and (ii) this mixed-Nash strategy can be interpreted as an analogue to control barrier functions (CBFs), that can then be integrated with standard CBFs, inheriting their safety guarantees. Together, the standard CBF along with the mixed-Nash CBF analogue preserves both safety and liveness. We evaluate our proposed game-theoretic navigation algorithm in simulation as well on physical robots using F1/10 robots, a Clearpath Jackal, as well as a Boston Dynamics Spot in a doorway, corridor intersection, roundabout, and hallway scenario. We show that (i) our approach results in safer and more efficient navigation compared to local planners based on geometrical constraints, optimization, multi-agent reinforcement learning, and auctions, (ii) our deadlock resolution strategy is the smoothest in terms of smallest average change in velocity and path deviation, and most efficient in terms of makespan (iii) our approach yields a flow rate of 2.8 - 3.3 (ms)^{-1 which is comparable to flow rate in human navigation at 4 (ms)^{-1}.Comment: arXiv admin note: text overlap with arXiv:2306.0881

Deep Learning Algorithms for Efficient Analysis of ECG Signals to Detect Heart Disorders

Electrocardiography (ECG) has been a reliable method for monitoring the proper functioning of the cardiovascular system for decades. Recently, there has been a lot of research focusing on accurately analyzing the heart condition through ECG. In recent days, numerous attempts are being made to analyze these signals using deep learning algorithms, including the implementation of artificial neural networks like convolutional neural networks, recurrent neural networks, and the like. In this context, this chapter intends to present some important techniques for classifying heartbeats based on deep neural networks with 1D CNN. Five ECG signals (N, S, V, F, and Q) standardization are based on the AAMI EC57 standard. The primary focus of this chapter is to discuss the techniques to classify ECG signals in those classes with promising accuracy and draw a clear picture of the current state-of-the-art in this sphere of study

Knowledge, Attitude and Practice Regarding Medication use among Pregnant Women Attending Antenatal Clinic: A Cross-sectional Study

Introduction: Medication use during pregnancy is a major concern in India and poor awareness is driven by non scientific information sources. Primary care providers play a role in providing information on risk of teratogenic and folate deficiency birth defects. Aim: To assess Knowledge, Attitude and Practice (KAP) of pregnant women attending antenatal clinic regarding medication use and self-medication during pregnancy. Materials and Methods: This cross-sectional study was conducted on 100 pregnant women attending the antenatal clinic in a tertiary care teaching hospital of Eastern India from August to October 2021. The study looked at sources of drug information, attitudes regarding medication use and practice of medication use and self-medication among pregnant mothers attending antenatal clinic of the hospital. Consenting women were enrolled in the study and the qualitative data gathered from the women were analysed using tools of descriptive statistics. Results: This study included 100 pregnant women with mean age of 22±2.0 years of which 42% were primigravida. Of the participants 80% had atleast high school education. Two third of the mothers 66% cited their family members as source of their drug information and 76% were aware of the risks of self-medication during pregnancy. Self medication practice was seen in 25% pregnant women mainly with Paracetamol use for pain or over-the-counter drugs to control acid reflux and morning sickness symptoms. All the participants (100%) took their iron and folic acid supplements as advised. Conclusion: The pregnant women attending the hospital showed adequate knowledge and satisfactory practices regarding medication use. Counselling of the mothers attending the clinic regarding drug use and possible harms to the mother and baby can help reduce long-term risks

Targeted Learning: A Hybrid Approach to Social Robot Navigation

Empowering robots to navigate in a socially compliant manner is essential for the acceptance of robots moving in human-inhabited environments. Previously, roboticists have developed classical navigation systems with decades of empirical validation to achieve safety and efficiency. However, the many complex factors of social compliance make classical navigation systems hard to adapt to social situations, where no amount of tuning enables them to be both safe (people are too unpredictable) and efficient (the frozen robot problem). With recent advances in deep learning approaches, the common reaction has been to entirely discard classical navigation systems and start from scratch, building a completely new learning-based social navigation planner. In this work, we find that this reaction is unnecessarily extreme: using a large-scale real-world social navigation dataset, SCAND, we find that classical systems can be used safely and efficiently in a large number of social situations (up to 80%). We therefore ask if we can rethink this problem by leveraging the advantages of both classical and learning-based approaches. We propose a hybrid strategy in which we learn to switch between a classical geometric planner and a data-driven method. Our experiments on both SCAND and two physical robots show that the hybrid planner can achieve better social compliance in terms of a variety of metrics, compared to using either the classical or learning-based approach alone

Analysis of novel geometry-independent method for dialysis access pressure-flow monitoring

Abstract Background End-stage renal disease (ESRD) confers a large health-care burden for the United States, and the morbidity associated with vascular access failure has stimulated research into detection of vascular access stenosis and low flow prior to thrombosis. We present data investigating the possibility of using differential pressure (ΔP) monitoring to estimate access flow (Q) for dialysis access monitoring, with the goal of utilizing micro-electro-mechanical systems (MEMS) pressure sensors integrated within the shaft of dialysis needles. Methods A model of the arteriovenous graft fluid circuit was used to study the relationship between Q and the ΔP between two dialysis needles placed 2.5–20.0 cm apart. Tubing was varied to simulate grafts with inner diameters of 4.76–7.95 mm. Data were compared with values from two steady-flow models. These results, and those from computational fluid dynamics (CFD) modeling of ΔP as a function of needle position, were used to devise and test a method of estimating Q using ΔP and variable dialysis pump speeds (variable flow) that diminishes dependence on geometric factors and fluid characteristics. Results In the fluid circuit model, ΔP increased with increasing volume flow rate and with increasing needle-separation distance. A nonlinear model closely predicts this ΔP-Q relationship (R2 > 0.98) for all graft diameters and needle-separation distances tested. CFD modeling suggested turbulent needle effects are greatest within 1 cm of the needle tip. Utilizing linear, quadratic and combined variable flow algorithms, dialysis access flow was estimated using geometry-independent models and an experimental dialysis system with the pressure sensors separated from the dialysis needle tip by distances ranging from 1 to 5 cm. Real-time ΔP waveform data were also observed during the mock dialysis treatment, which may be useful in detecting low or reversed flow within the access. Conclusion With further experimentation and needle design, this geometry-independent approach may prove to be a useful access flow monitoring method.http://deepblue.lib.umich.edu/bitstream/2027.42/112774/1/12976_2008_Article_178.pd

Characterization of vascular strain during in-vitro angioplasty with high-resolution ultrasound speckle tracking

<p>Abstract</p> <p>Background</p> <p>Ultrasound elasticity imaging provides biomechanical and elastic properties of vascular tissue, with the potential to distinguish between tissue motion and tissue strain. To validate the ability of ultrasound elasticity imaging to predict structurally defined physical changes in tissue, strain measurement patterns during angioplasty in four bovine carotid artery pathology samples were compared to the measured physical characteristics of the tissue specimens.</p> <p>Methods</p> <p>Using computational image-processing techniques, the circumferences of each bovine artery specimen were obtained from ultrasound and pathologic data.</p> <p>Results</p> <p>Ultrasound-strain-based and pathology-based arterial circumference measurements were correlated with an R²value of 0.94 (p = 0.03). The experimental elasticity imaging results confirmed the onset of deformation of an angioplasty procedure by indicating a consistent inflection point where vessel fibers were fully unfolded and vessel wall strain initiated.</p> <p>Conclusion</p> <p>These results validate the ability of ultrasound elasticity imaging to measure localized mechanical changes in vascular tissue.</p

Intra- and inter-individual genetic differences in gene expression

Genetic variation is known to influence the amount of mRNA produced by a gene. Given that the molecular machines control mRNA levels of multiple genes, we expect genetic variation in the components of these machines would influence multiple genes in a similar fashion. In this study we show that this assumption is correct by using correlation of mRNA levels measured independently in the brain, kidney or liver of multiple, genetically typed, mice strains to detect shared genetic influences. These correlating groups of genes (CGG) have collective properties that account for 40-90% of the variability of their constituent genes and in some cases, but not all, contain genes encoding functionally related proteins. Critically, we show that the genetic influences are essentially tissue specific and consequently the same genetic variations in the one animal may up-regulate a CGG in one tissue but down-regulate the same CGG in a second tissue. We further show similarly paradoxical behaviour of CGGs within the same tissues of different individuals. The implication of this study is that this class of genetic variation can result in complex inter- and intra-individual and tissue differences and that this will create substantial challenges to the investigation of phenotypic outcomes, particularly in humans where multiple tissues are not readily available.&#xd;&#xa;&#xd;&#xa

Principles and Guidelines for Evaluating Social Robot Navigation Algorithms

A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation. While the field of social navigation has advanced tremendously in recent years, the fair evaluation of algorithms that tackle social navigation remains hard because it involves not just robotic agents moving in static environments but also dynamic human agents and their perceptions of the appropriateness of robot behavior. In contrast, clear, repeatable, and accessible benchmarks have accelerated progress in fields like computer vision, natural language processing and traditional robot navigation by enabling researchers to fairly compare algorithms, revealing limitations of existing solutions and illuminating promising new directions. We believe the same approach can benefit social navigation. In this paper, we pave the road towards common, widely accessible, and repeatable benchmarking criteria to evaluate social robot navigation. Our contributions include (a) a definition of a socially navigating robot as one that respects the principles of safety, comfort, legibility, politeness, social competency, agent understanding, proactivity, and responsiveness to context, (b) guidelines for the use of metrics, development of scenarios, benchmarks, datasets, and simulators to evaluate social navigation, and (c) a design of a social navigation metrics framework to make it easier to compare results from different simulators, robots and datasets.Comment: 43 pages, 11 figures, 6 table