Review of machine learning methods in soft robotics

Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots

Active Emulsions: Physicochemical Hydrodynamics and Collective Behavior

Active matter is a collection of constituent elements that constantly consume energy, convert it to mechanical work, and interact with their counterparts. These materials operate out of equilibrium and exhibit fascinating collective dynamics such as spontaneous pattern formation. Self-organization of bio-polymers within a cell, collective migration of bacteria in search of nutrition, and the bird flocks are paragons of active living matter and the primary source of our knowledge on it. To understand the overarching physical principles of active matter, it is desirable to build artificial systems that are capable of imitating living active matter while ruling out the biological complexities. The goal of this thesis is to study active micro-droplets as a paradigm for biomimetic artificial active particles, using fundamental principles of fluid dynamics and statistical physics. The Marangoni-driven motility in these droplets is reminiscent of the locomotion of some protozoal organisms, known as squirmers. The main scientific objectives of this research are to (i) investigate the potential biomimetic features of active droplets including compartmentalization, adaptability (e.g. multi-gait motility), and information processing (signaling and sensing) and (ii) study the implications of those features in the collective dynamics of active emulsions governed by hydrodynamic and autochemotactic interactions. These objectives are addressed experimentally using microfluidics and microscopy, integrated with quantitative image analysis. The quantitative experimental results are then compared with the predictions from theory or simulations. The findings of this thesis are presented in five chapters. First, we address the challenge of compartmentalizing active droplets. We use microfluidics to generate liquid shells (double emulsions). We propose and successfully prove the use of a nematic liquid crystal oil to stabilize the liquid shells, which are otherwise susceptible to break up during motility. We investigate the propulsion dynamics and use that insight to put forward routes to control shell motion via topology, chemical signaling, and topography. In the second results chapter, we establish the bimodal dynamics of chaotic motility in active droplets; a regime that emerges as a response to the increase of viscosity in the swimming medium. To establish the physical mechanism of this dynamical transition, we developed a novel technique to simultaneously visualize the hydrodynamic and chemical fields around the droplet. The results are rationalized by quantitative comparison to established advection-diffusion models. We further observe that the droplets undergo self-avoiding random walks as a result of interaction with the self-generated products of their activity, secreted in the environment. The third results chapter presents a review of the dynamics of chemotactic droplets in complex environments, highlighting the effects of self-generated chemical interactions on the droplet dynamics. In the fourth results chapter, we investigate how active droplets sense and react to the chemical gradients generated by their counterparts--- a behavior known as autochemotaxis. Then, we study the collective dynamics governed by these autochemotactic interactions, in two and three dimensions. For the first time, we report the observation of ‘history caging’, where swimmers are temporarily trapped in an evolving network of repulsive chemical trails. The caging results in a plateau in the mean squared displacement profiles as observed for dense colloidal systems near the glass transition. In the last results chapter, we investigate the collective dynamics in active emulsions, governed by hydrodynamic interactions. We report the emergence of spontaneously rotating clusters. We show that the rotational dynamics originates from a novel symmetry breaking mechanism for single isotropic droplets. By extending our understanding to the collective scale, we show how the stability and dynamics of the clusters can be controlled by droplet activity and cluster size. The experimental advancements and the findings presented in this thesis lay the groundwork for future investigations of emergent dynamics in active emulsions as a model system for active matter. In the outlook section, we present some of the new questions that have developed in the course of this research work and discuss a perspective on the future directions of the research on active droplets.2022-01-1

Updated Perspectives on the Role of Biomechanics in COPD: Considerations for the Clinician

Patients with chronic obstructive pulmonary disease (COPD) demonstrate extra-pulmonary functional decline such as an increased prevalence of falls. Biomechanics offers insight into functional decline by examining mechanics of abnormal movement patterns. This review discusses biomechanics of functional outcomes, muscle mechanics, and breathing mechanics in patients with COPD as well as future directions and clinical perspectives. Patients with COPD demonstrate changes in their postural sway during quiet standing compared to controls, and these deficits are exacerbated when sensory information (eg, eyes closed) is manipulated. If standing balance is disrupted with a perturbation, patients with COPD are slower to return to baseline and their muscle activity is differential from controls. When walking, patients with COPD appear to adopt a gait pattern that may increase stability (eg, shorter and wider steps, decreased gait speed) in addition to altered gait variability. Biomechanical muscle mechanics (ie, tension, extensibility, elasticity, and irritability) alterations with COPD are not well documented, with relatively few articles investigating these properties. On the other hand, dyssynchronous motion of the abdomen and rib cage while breathing is well documented in patients with COPD. Newer biomechanical technologies have allowed for estimation of regional, compartmental, lung volumes during activity such as exercise, as well as respiratory muscle activation during breathing. Future directions of biomechanical analyses in COPD are trending toward wearable sensors, big data, and cloud computing. Each of these offers unique opportunities as well as challenges. Advanced analytics of sensor data can offer insight into the health of a system by quantifying complexity or fluctuations in patterns of movement, as healthy systems demonstrate flexibility and are thus adaptable to changing conditions. Biomechanics may offer clinical utility in prediction of 30-day readmissions, identifying disease severity, and patient monitoring. Biomechanics is complementary to other assessments, capturing what patients do, as well as their capability

Smart Clothing Framework for Health Monitoring Applications

Wearable technologies are making a significant impact on people’s way of living thanks to the advancements in mobile communication, internet of things (IoT), big data and artificial intelligence. Conventional wearable technologies present many challenges for the continuous monitoring of human health conditions due to their lack of flexibility and bulkiness in size. Recent development in e-textiles and the smart integration of miniature electronic devices into textiles have led to the emergence of smart clothing systems for remote health monitoring. A novel comprehensive framework of smart clothing systems for health monitoring is proposed in this paper. This framework provides design specifications, suitable sensors and textile materials for smart clothing (e.g., leggings) development. In addition, the proposed framework identifies techniques for empowering the seamless integration of sensors into textiles and suggests a development strategy for health diagnosis and prognosis through data collection, data processing and decision making. The conceptual technical specification of smart clothing is also formulated and presented. The detailed development of this framework is presented in this paper with selected examples. The key challenges in popularizing smart clothing and opportunities of future development in diverse application areas such as healthcare, sports and athletics and fashion are discussed

Mobile Diagnosis 2.0

Mobile sensing and diagnostic capabilities are becoming extremely important for a wide range of emerging applications and fields spanning mobile health, telemedicine, point-of-care diagnostics, global health, field medicine, democratization of sensing and diagnostic tools, environmental monitoring, and citizen science, among many others. The importance of low-cost mobile technologies has been underlined during this current COVID-19 pandemic, particularly for applications such as the detection of pathogens, including bacteria and viruses, as well as for prediction and management of different diseases and disorders. This book focuses on some of these application areas and provides a timely summary of cutting-edge results and emerging technologies in these interdisciplinary fields

Biomedical Sensing and Imaging

This book mainly deals with recent advances in biomedical sensing and imaging. More recently, wearable/smart biosensors and devices, which facilitate diagnostics in a non-clinical setting, have become a hot topic. Combined with machine learning and artificial intelligence, they could revolutionize the biomedical diagnostic field. The aim of this book is to provide a research forum in biomedical sensing and imaging and extend the scientific frontier of this very important and significant biomedical endeavor

A NEUROMORPHIC APPROACH TO TACTILE PERCEPTION

Ph.DDOCTOR OF PHILOSOPH

Design of a pneumatic soft robotic actuator using model-based optimization

In this thesis, the design and optimization process of a novel soft intelligent modular pad (IntelliPad) for the purpose of pressure injury prevention is presented. The structure of the IntelliPad consists of multiple individual multi-chamber soft pneumatic-driven actuators that use pressurized air and vacuum. Each actuator is able to provide both vertical and horizontal motions that can be controlled independently. An analytical modeling approach using multiple cantilever beams and virtual springs connected in a closed formed structure was developed to analyze the mechanical performance of the actuator. The analytical approach was validated by a finite element analysis. For optimizing the actuator\u27s mechanical performance, firefly algorithm and deep reinforcement learning-based design optimization frameworks were developed with the purpose of maximizing the horizontal motion of the top surface of the actuators, while minimizing its corresponding effect on the vertical motion. Four optimized designs were fabricated. The actuators were tested and validated experimentally to demonstrate their required mechanical performance in order to regulate normal and shear stresses at the skin-pad interface for pressure injury prevention applications

Low-power Wearable Healthcare Sensors

Advances in technology have produced a range of on-body sensors and smartwatches that can be used to monitor a wearer’s health with the objective to keep the user healthy. However, the real potential of such devices not only lies in monitoring but also in interactive communication with expert-system-based cloud services to offer personalized and real-time healthcare advice that will enable the user to manage their health and, over time, to reduce expensive hospital admissions. To meet this goal, the research challenges for the next generation of wearable healthcare devices include the need to offer a wide range of sensing, computing, communication, and human–computer interaction methods, all within a tiny device with limited resources and electrical power. This Special Issue presents a collection of six papers on a wide range of research developments that highlight the specific challenges in creating the next generation of low-power wearable healthcare sensors