Smart Shoe Insole Based on Polydimethylsiloxane Composite Capacitive Sensors

Nowadays, the study of the gait by analyzing the distribution of plantar pressure is a well-established technique. The use of intelligent insoles allows real-time monitoring of the user. Thus, collecting and analyzing information is a more accurate process than consultations in so-called gait laboratories. Most of the previous published studies consider the composition and operation of these insoles based on resistive sensors. However, the use of capacitive sensors could provide better results, in terms of linear behavior under the pressure exerted. This behavior depends on the properties of the dielectric used. In this work, the design and implementation of an intelligent plantar insole composed of capacitive sensors is proposed. The dielectric used is a polydimethylsiloxane (PDMS)-based composition. The sensorized plantar insole developed achieves its purpose as a tool for collecting pressure in different areas of the sole of the foot. The fundamentals and details of the composition, manufacture, and implementation of the insole and the system used to collect data, as well as the data samples, are shown. Finally, a comparison of the behavior of both insoles, resistive and capacitive sensor-equipped, is made. The prototype presented lays the foundation for the development of a tool to support the diagnosis of gait abnormalities.22 página

Polymer Optical Fiber Sensors for Healthcare Devices: From the Material Analysis to Practical Applications

tb

A systematic approach to the design and characterization of a smart insole for detecting vertical ground reaction force (vGRF) in gait analysis

Gait analysis is a systematic study of human locomotion, which can be utilized in various applications, such as rehabilitation, clinical diagnostics and sports activities. The various limitations such as cost, non-portability, long setup time, post-processing time etc., of the current gait analysis techniques have made them unfeasible for individual use. This led to an increase in research interest in developing smart insoles where wearable sensors can be employed to detect vertical ground reaction forces (vGRF) and other gait variables. Smart insoles are flexible, portable and comfortable for gait analysis, and can monitor plantar pressure frequently through embedded sensors that convert the applied pressure to an electrical signal that can be displayed and analyzed further. Several research teams are still working to improve the insoles' features such as size, sensitivity of insoles sensors, durability, and the intelligence of insoles to monitor and control subjects' gait by detecting various complications providing recommendation to enhance walking performance. Even though systematic sensor calibration approaches have been followed by different teams to calibrate insoles' sensor, expensive calibration devices were used for calibration such as universal testing machines or infrared motion capture cameras equipped in motion analysis labs. This paper provides a systematic design and characterization procedure for three different pressure sensors: force-sensitive resistors (FSRs), ceramic piezoelectric sensors, and flexible piezoelectric sensors that can be used for detecting vGRF using a smart insole. A simple calibration method based on a load cell is presented as an alternative to the expensive calibration techniques. In addition, to evaluate the performance of the different sensors as a component for the smart insole, the acquired vGRF from different insoles were used to compare them. The results showed that the FSR is the most effective sensor among the three sensors for smart insole applications, whereas the piezoelectric sensors can be utilized in detecting the start and end of the gait cycle. This study will be useful for any research group in replicating the design of a customized smart insole for gait analysis. 2020 by the authors. Licensee MDPI, Basel, Switzerland.This research was partially funded by Qatar National Research Foundation (QNRF), grant number NPRP12S-0227-190164 and Research University Grant DIP-2018-017. The publication of this article was funded by the Qatar National Library. The authors would like to thank Engr. Ayman Ammar, Electrical Engineering, Qatar University for helping in printing the printed circuit boards (PCBs). This research was partially funded by Qatar National Research Foundation (QNRF), grant number NPRP12S-0227-190164 and Research University Grant DIP-2018-017. The publication of this article was funded by the Qatar National Library.Scopu

Wearable Sensors and Smart Devices to Monitor Rehabilitation Parameters and Sports Performance: An Overview

A quantitative evaluation of kinetic parameters, the joint’s range of motion, heart rate, and breathing rate, can be employed in sports performance tracking and rehabilitation monitoring following injuries or surgical operations. However, many of the current detection systems are expensive and designed for clinical use, requiring the presence of a physician and medical staff to assist users in the device’s positioning and measurements. The goal of wearable sensors is to overcome the limitations of current devices, enabling the acquisition of a user’s vital signs directly from the body in an accurate and non–invasive way. In sports activities, wearable sensors allow athletes to monitor performance and body movements objectively, going beyond the coach’s subjective evaluation limits. The main goal of this review paper is to provide a comprehensive overview of wearable technologies and sensing systems to detect and monitor the physiological parameters of patients during post–operative rehabilitation and athletes’ training, and to present evidence that supports the efficacy of this technology for healthcare applications. First, a classification of the human physiological parameters acquired from the human body by sensors attached to sensitive skin locations or worn as a part of garments is introduced, carrying important feedback on the user’s health status. Then, a detailed description of the electromechanical transduction mechanisms allows a comparison of the technologies used in wearable applications to monitor sports and rehabilitation activities. This paves the way for an analysis of wearable technologies, providing a comprehensive comparison of the current state of the art of available sensors and systems. Comparative and statistical analyses are provided to point out useful insights for defining the best technologies and solutions for monitoring body movements. Lastly, the presented review is compared with similar ones reported in the literature to highlight its strengths and novelties

Human and Biological Skin-Inspired Electronic Skins for Advanced Sensory Functions and Multifunctionality

Department of Energy Engineering (Energy Engineering)The electronic skin (e-skin) technology is an exciting frontier to drive next generation of wearable electronics owing to its high level of wearability to curved human body, enabling high accuracy to harvest information of users and their surroundings. Altough various types of e-skins, based on several signal-transduction modes, including piezoresistive, capacitive, piezoelectric, triboelectric modes, have been developed, their performances (i.e. sensitivity, working range, linearity, multifunctionality, etc.) should be improved for the wearable applications. Recently, biomimicry of the human and biological skins has become a great inspiration for realizing novel wearable e-skin systems with exceptional multifunctionality as well as advanced sensory functions. As an ideal sensory organ, tactile sensing capabilities of human skin was emulated for the development of e-skins with enhanced sensor performances. In particular, the unique geometry and systematic sensory system of human skin have driven new opportunities in multifunctional and highly sensitive e-skin applications. In addition, extraordinary architectures for protection, locomotion, risk indication, and camouflage in biological systems provide great possibilities for second skin applications on user-interactive, skin-attachable, and ultrasensitive e-skins, as well as soft robots. Benefitting from their superior perceptive functions and multifunctionality, human and biological skins-inspired e-skins can be considered to be promising candidates for wearable device applications, such as body motion tracking, healthcare devices, acoustic sensor, and human machine interfaces (HMI). This thesis covers our recent studies about human and biological skin-inspired e-skins for advanced sensory functions and multifunctionality. First, chapter 1 highlights various types of e-skins and recent research trends in bioinspired e-skins mimicking perceptive features of human and biological skins. In chapter 2, we demonstrate highly sensitive and tactile-direction-sensitive e-skin based on human skin-inspired interlocked microdome structures. Owing to the stress concentration effect, the interlocked e-skin experiences significant change of contact area between the interlocked microdomes, resulting in high pressure sensitivity. In addition, because of the different deformation trends between microstructures in mutual contact, the interlocked e-skin can differentiate and decouple sensor signals under different directional forces, such as pressure, tensile strain, shear, and bending. In chapter 3, interlocked e-skins were designed with multilayered geometry. Although interlocked e-skin shows highly sensitive pressure sensing performances, their pressure sensing range is narrow and pressure sensitivity continuously decreases with increasing pressure level. The multilayer interlocked microdome geometry can enhance the pressure-sensing performances of e-skins, such as sensitivity, working range, and linearity. As another approach of e-skin with multilayered geometry, we demonstrate multilayered e-skin based on conductivity-gradient conductive materials in chapter 4. The conducive polymer composites with different conductivity were coated on the microdome pattern and designed as interlocked e-skin with coplanar electrode design, resulting in exceptionally high pressure-sensing performances compared with previous literatures. In chapter 5, inspired by responsive color change in biological skins, we developed mechanochromic e-skin with a hierarchical nanoparticle-in-micropore architecture. The novel design of hierarchical structure enables effective stress concentration at the interface between nanoparticle and porous structure, resulting in impressive color change under mechanical stimuli. In chapter 6, we emulate ultrahigh temperature sensitivity of human and snake skin for temperature-sensitive e-skin. The thermoresponsive composite based on semi-crystalline polymer, temperature sensor shows ultrahigh temperature sensitivity near the melting point of semi-crystalline polymer. In addition, integration of thermochromic composite, mimicking biological skins, enables dual-mode temperature sensors by electrical and colorimetric sensing capabilities. Finally, in chapter 7, we summarize this thesis along with future perspective that should be considered for next-generation e-skin electronics. Our e-skins, inspired by human and biological skin, can provide a new paradigm for realizing novel wearable electronic systems with exceptional multifunctionality as well as advanced sensory functions.clos

Low-Cost Sensors and Biological Signals

Many sensors are currently available at prices lower than USD 100 and cover a wide range of biological signals: motion, muscle activity, heart rate, etc. Such low-cost sensors have metrological features allowing them to be used in everyday life and clinical applications, where gold-standard material is both too expensive and time-consuming to be used. The selected papers present current applications of low-cost sensors in domains such as physiotherapy, rehabilitation, and affective technologies. The results cover various aspects of low-cost sensor technology from hardware design to software optimization

????????????/???????????? ?????? ?????? ?????? ???????????? ??? ????????? ??????????????????

Department of Energy EngineeringElectronic skins (e-skins) enabling to detect various mechanical/chemical stimuli and environmental conditions by converting into various electrical and optical signals have attracted much attentions for various fields including wearable electronics, intelligent/medical robotics, healthcare monitoring devices, and haptic interfaces. Conventional e-skins have been widely used for the realization of these applications, however it is still considered that new e-skins with enhanced sensor performances (i.e. sensitivity, flexibility, multifunctionality, etc.) should be developed. In accordance with these demands, two approaches to explore novel functional materials or to modify device architectures have been introduced for enhancing sensor performance and acquiring multifunctional sensing capabilities. Firstly, a synthesis of multifunctional materials combined with conductive fillers (carbon nanotube, graphene oxide) and functional polymer matrix (i.e. ferroelectric polymer, elastomer) can provide the multimodal sensing capability of various stimuli and stretchability. Secondly, controlling design of device structures into various micro/nanostructures enables a significant improvement on sensing capabilities of e-skins with sensitivity and multidirectional force sensing, resulting from structural advantages such as large surface area, effective stress propagation, and anisotropic deformation. Therefore, a demonstration of e-skin combined with the functional composites and uniquely designed microstructures can offer a powerful platform to realize ideal sensor systems for next generation applications such as wearable electronics, healthcare devices, acoustic sensor, and haptic interface devices. In this thesis, we introduce the novel multifunctional and high performance electronic skins combined with various types of composite materials and nature-inspired 3D microstructures. Firstly, Chapter 1 briefly introduces various types of e-skins and the latest research trends of microstructured e-skins and summarizes the key components for their promising application fields. In chapters 2 and 3, mimicked by interlocking system between epidermal and dermal layers in human skin, we demonstrate the piezoresistive e-skins based on CNT/PDMS composite materials with interlocked microdome arrays for great pressure sensitivity and multidirectional force sensing capabilities. In chapter 4, we conduct in-depth study on giant tunneling piezoresistance in interlocking system and investigate systematically on the geometrical effect of microstructures on multidirectional force sensitivity and selectivity in interlocking sensor systems. In chapter 5, we demonstrate the ferroelectric e-skin that can detect and discriminate the static/dynamic touches and temperature inspired by multi-stimuli detection of various mechanoreceptors in human skin. Using the multifunctional sensing capabilities, we demonstrated our e-skin to the temperature-dependent pressure monitoring of artery vessel, high-precision acoustic sound detection, and surface texture recognition of various surfaces. In chapter 6, we demonstrate the linear and wide range pressure sensor with multilayered composite films having interlocked microdomes. In chapter 7, we present a new-concept of e-skin based on mechanochromic polymer and porous structures for overcoming limitations in conventional mechanochromic systems with low mechanochromic performances and limited stretchability. In addition, our mechanochromic e-skins enable the dual-mode detection of static and dynamic forces without any external power. Our e-skins based on functional composites and uniquely designed microstructures can provide a solid platform for next generation eskin in wearable electronics, humanoid robotics, flexible sensors, and wearable medical diagnostic systems.clos

Foot Motion-Based Falling Risk Evaluation for Patients with Parkinson’s Disease

Parkinson’s disease (PD) affects motor functionalities, which are closely associated with increased risks of falling and decreased quality of life. However, there is no easy-to-use definitive tools for PD patients to quantify their falling risks at home. To address this, in this dissertation, we develop Monitoring Insoles (MONI) with advanced data processing techniques to score falling risks of PD patients following Falling Risk Questionnaire (FRQ) developed by the U.S. Centers for Disease Control and Prevention (CDC). To achieve this, we extract motion tasks from daily activities and select the most representative features associated with PD that facilitate accurate falling risk scoring. To address the challenge in uncontrolled daily life environments and to identify the most representative features associated with PD and falling risks, the proposed data processing method firstly recognizes foot motions such as walking and toe tapping from continuous movements with stride detection and fast labeling framework, and then extracts time-axis and acceleration-axis features from the motion tasks, at the end provides a score of falling risks using regression. The data processing method can be integrated into a mobile game to be used at home with MONI. The main contributions of this dissertation includes: (i) developing MONI as a low power solution for daily life use; (ii) utilizing stride detection and developing fast labeling framework for motion recognition that improves recognition accuracy for daily life applications; (iii) analyzing two walking and two toe tapping tasks that are close to real life scenarios and identifying important features associated with PD and falling risks; (iv) providing falling scores as quantitative evaluation to PD patients in daily life through simple foot motion tasks and setups

Wearable pressure sensing for intelligent gesture recognition

The development of wearable sensors has become a major area of interest due to their wide range of promising applications, including health monitoring, human motion detection, human-machine interfaces, electronic skin and soft robotics. Particularly, pressure sensors have attracted considerable attention in wearable applications. However, traditional pressure sensing systems are using rigid sensors to detect the human motions. Lightweight and flexible pressure sensors are required to improve the comfortability of devices. Furthermore, in comparison with conventional sensing techniques without smart algorithm, machine learning-assisted wearable systems are capable of intelligently analysing data for classification or prediction purposes, making the system ‘smarter’ for more demanding tasks. Therefore, combining flexible pressure sensors and machine learning is a promising method to deal with human motion recognition. This thesis focuses on fabricating flexible pressure sensors and developing wearable applications to recognize human gestures. Firstly, a comprehensive literature review was conducted, including current state-of-the-art on pressure sensing techniques and machine learning algorithms. Secondly, a piezoelectric smart wristband was developed to distinguish finger typing movements. Three machine learning algorithms, K Nearest Neighbour (KNN), Decision Tree (DT) and Support Vector Machine (SVM), were used to classify the movement of different fingers. The SVM algorithm outperformed other classifiers with an overall accuracy of 98.67% and 100% when processing raw data and extracted features. Thirdly, a piezoresistive wristband was fabricated based on a flake-sphere composite configuration in which reduced graphene oxide fragments are doped with polystyrene spheres to achieve both high sensitivity and flexibility. The flexible wristband measured the pressure distribution around the wrist for accurate and comfortable hand gesture classification. The intelligent wristband was able to classify 12 hand gestures with 96.33% accuracy for five participants using a machine learning algorithm. Moreover, for demonstrating the practical applications of the proposed method, a realtime system was developed to control a robotic hand according to the classification results. Finally, this thesis also demonstrates an intelligent piezoresistive sensor to recognize different throat movements during pronunciation. The piezoresistive sensor was fabricated using two PolyDimethylsiloxane (PDMS) layers that were coated with silver nanowires and reduced graphene oxide films, where the microstructures were fabricated by the polystyrene spheres between the layers. The highly sensitive sensor was able to distinguish throat vibrations from five different spoken words with an accuracy of 96% using the artificial neural network algorithm