A comparative study of calibration methods for low-cost ozone sensors in IoT platforms

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper shows the result of the calibration process of an Internet of Things platform for the measurement of tropospheric ozone (O 3 ). This platform, formed by 60 nodes, deployed in Italy, Spain, and Austria, consisted of 140 metal–oxide O 3 sensors, 25 electro-chemical O 3 sensors, 25 electro-chemical NO 2 sensors, and 60 temperature and relative humidity sensors. As ozone is a seasonal pollutant, which appears in summer in Europe, the biggest challenge is to calibrate the sensors in a short period of time. In this paper, we compare four calibration methods in the presence of a large dataset for model training and we also study the impact of a limited training dataset on the long-range predictions. We show that the difficulty in calibrating these sensor technologies in a real deployment is mainly due to the bias produced by the different environmental conditions found in the prediction with respect to those found in the data training phase.Peer ReviewedPostprint (author's final draft

Perception of Unstructured Environments for Autonomous Off-Road Vehicles

Autonome Fahrzeuge benötigen die Fähigkeit zur Perzeption als eine notwendige Voraussetzung für eine kontrollierbare und sichere Interaktion, um ihre Umgebung wahrzunehmen und zu verstehen. Perzeption für strukturierte Innen- und Außenumgebungen deckt wirtschaftlich lukrative Bereiche, wie den autonomen Personentransport oder die Industrierobotik ab, während die Perzeption unstrukturierter Umgebungen im Forschungsfeld der Umgebungswahrnehmung stark unterrepräsentiert ist. Die analysierten unstrukturierten Umgebungen stellen eine besondere Herausforderung dar, da die vorhandenen, natürlichen und gewachsenen Geometrien meist keine homogene Struktur aufweisen und ähnliche Texturen sowie schwer zu trennende Objekte dominieren. Dies erschwert die Erfassung dieser Umgebungen und deren Interpretation, sodass Perzeptionsmethoden speziell für diesen Anwendungsbereich konzipiert und optimiert werden müssen. In dieser Dissertation werden neuartige und optimierte Perzeptionsmethoden für unstrukturierte Umgebungen vorgeschlagen und in einer ganzheitlichen, dreistufigen Pipeline für autonome Geländefahrzeuge kombiniert: Low-Level-, Mid-Level- und High-Level-Perzeption. Die vorgeschlagenen klassischen Methoden und maschinellen Lernmethoden (ML) zur Perzeption bzw.~Wahrnehmung ergänzen sich gegenseitig. Darüber hinaus ermöglicht die Kombination von Perzeptions- und Validierungsmethoden für jede Ebene eine zuverlässige Wahrnehmung der möglicherweise unbekannten Umgebung, wobei lose und eng gekoppelte Validierungsmethoden kombiniert werden, um eine ausreichende, aber flexible Bewertung der vorgeschlagenen Perzeptionsmethoden zu gewährleisten. Alle Methoden wurden als einzelne Module innerhalb der in dieser Arbeit vorgeschlagenen Perzeptions- und Validierungspipeline entwickelt, und ihre flexible Kombination ermöglicht verschiedene Pipelinedesigns für eine Vielzahl von Geländefahrzeugen und Anwendungsfällen je nach Bedarf. Low-Level-Perzeption gewährleistet eine eng gekoppelte Konfidenzbewertung für rohe 2D- und 3D-Sensordaten, um Sensorausfälle zu erkennen und eine ausreichende Genauigkeit der Sensordaten zu gewährleisten. Darüber hinaus werden neuartige Kalibrierungs- und Registrierungsansätze für Multisensorsysteme in der Perzeption vorgestellt, welche lediglich die Struktur der Umgebung nutzen, um die erfassten Sensordaten zu registrieren: ein halbautomatischer Registrierungsansatz zur Registrierung mehrerer 3D~Light Detection and Ranging (LiDAR) Sensoren und ein vertrauensbasiertes Framework, welches verschiedene Registrierungsmethoden kombiniert und die Registrierung verschiedener Sensoren mit unterschiedlichen Messprinzipien ermöglicht. Dabei validiert die Kombination mehrerer Registrierungsmethoden die Registrierungsergebnisse in einer eng gekoppelten Weise. Mid-Level-Perzeption ermöglicht die 3D-Rekonstruktion unstrukturierter Umgebungen mit zwei Verfahren zur Schätzung der Disparität von Stereobildern: ein klassisches, korrelationsbasiertes Verfahren für Hyperspektralbilder, welches eine begrenzte Menge an Test- und Validierungsdaten erfordert, und ein zweites Verfahren, welches die Disparität aus Graustufenbildern mit neuronalen Faltungsnetzen (CNNs) schätzt. Neuartige Disparitätsfehlermetriken und eine Evaluierungs-Toolbox für die 3D-Rekonstruktion von Stereobildern ergänzen die vorgeschlagenen Methoden zur Disparitätsschätzung aus Stereobildern und ermöglichen deren lose gekoppelte Validierung. High-Level-Perzeption konzentriert sich auf die Interpretation von einzelnen 3D-Punktwolken zur Befahrbarkeitsanalyse, Objekterkennung und Hindernisvermeidung. Eine Domänentransferanalyse für State-of-the-art-Methoden zur semantischen 3D-Segmentierung liefert Empfehlungen für eine möglichst exakte Segmentierung in neuen Zieldomänen ohne eine Generierung neuer Trainingsdaten. Der vorgestellte Trainingsansatz für 3D-Segmentierungsverfahren mit CNNs kann die benötigte Menge an Trainingsdaten weiter reduzieren. Methoden zur Erklärbarkeit künstlicher Intelligenz vor und nach der Modellierung ermöglichen eine lose gekoppelte Validierung der vorgeschlagenen High-Level-Methoden mit Datensatzbewertung und modellunabhängigen Erklärungen für CNN-Vorhersagen. Altlastensanierung und Militärlogistik sind die beiden Hauptanwendungsfälle in unstrukturierten Umgebungen, welche in dieser Arbeit behandelt werden. Diese Anwendungsszenarien zeigen auch, wie die Lücke zwischen der Entwicklung einzelner Methoden und ihrer Integration in die Verarbeitungskette für autonome Geländefahrzeuge mit Lokalisierung, Kartierung, Planung und Steuerung geschlossen werden kann. Zusammenfassend lässt sich sagen, dass die vorgeschlagene Pipeline flexible Perzeptionslösungen für autonome Geländefahrzeuge bietet und die begleitende Validierung eine exakte und vertrauenswürdige Perzeption unstrukturierter Umgebungen gewährleistet

Wearable Biosensors to Understand Construction Workers' Mental and Physical Stress

Occupational stress is defined as harmful physical and mental responses when job requirements are greater than a worker's capacity. Construction is one of the most stressful occupations because it involves physiologically and psychologically demanding tasks performed in a hazardous environment this stress can jeopardize construction safety, health, and productivity. Various instruments, such as surveys and interviews, have been used for measuring workers’ perceived mental and physical stress. However valuable, such instruments are limited by their invasiveness, which prevents them from being used for continuous stress monitoring. The recent advancement of wearable biosensors has opened a new door toward the non-invasive collection of a field worker’s physiological signals that can be used to assess their mental and physical status. Despite these advancements, challenges remain: acquiring physiological signals from wearable biosensors can be easily contaminated from diverse sources of signal noise. Further, the potential of these devices to assess field workers’ mental and physical status has not been examined in the naturalistic work environment. To address these issues, this research aims to propose and validate a comprehensive and efficient stress-measurement framework that recognizes workers mental and physical stress in a naturalistic environment. The focus of this research is on two wearable biosensors. First, a wearable EEG headset, which is a direct measurement of brain waves with the minimal time lag, but it is highly vulnerable to various artifacts. Second, a very convenient wristband-type biosensor, which may be used as a means for assessing both mental and physical stress, but there is a time lag between when subjects are exposed to stressors and when their physiological signals change. To achieve this goal, five interrelated and interdisciplinary studies were performed to; 1) acquire high-quality EEG signals from the job site; 2) assess construction workers’ emotion by measuring the valence and arousal level by analyzing the patterns of construction workers’ brainwaves; 3) recognize mental stress in the field based on brain activities by applying supervised-learning algorithms;4) recognize real-time mental stress by applying Online Multi-Task Learning (OMTL) algorithms; and 5) assess workers’ mental and physical stress using signals collected from a wristband biosensor. To examine the performance of the proposed framework, we collected physiological signals from 21 workers at five job sites. Results yielded a high of 80.13% mental stress-recognition accuracy using an EEG headset and 90.00% physical stress-recognition accuracy using a wristband sensor. These results are promising given that stress recognition with wired physiological devices within a controlled lab setting in the clinical domain has, at best, a similar level of accuracy. The proposed wearable biosensor-based, stress-recognition framework is expected to help us better understand workplace stressors and improve worker safety, health, and productivity through early detection and mitigation of stress at human-centered, smart and connected construction sites.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149965/1/hjebelli_1.pd

Prosthetic Control and Sensory Feedback for Upper Limb Amputees

Hand amputation could dramatically degrade the life quality of amputees. Many amputees use prostheses to restore part of the hand functions. Myoelectric prosthesis provides the most dexterous control. However, they are facing high rejection rate. One of the reasons is the lack of sensory feedback. There is a need for providing sensory feedback for myoelectric prosthesis users. It can improve object manipulation abilities, enhance the perceptual embodiment of myoelectric prostheses and help reduce phantom limb pain. This PhD work focuses on building bi-directional prostheses for upper limb amputees. In the introduction chapter, first, an overview of upper limb amputee demographics and upper limb prosthesis is given. Then the human somatosensory system is briefly introduced. The next part reviews invasive and non-invasive sensory feedback methods reported in the literature. The rest of the chapter describes the motivation of the project and the thesis organization. The first step to build a bi-directional prostheses is to investigate natural and robust multifunctional prosthetic control. Most of the commerical prostheses apply non-pattern recognition based myoelectric control methods, which offers only limited functionalities. In this thesis work, pattern recognition based prosthetic control employing three commonly used and representative machine learning algorithms is investigated. Three datasets involving different levels of upper arm movements are used for testing the algorithm effectiveness. The influence of time-domain features, window and increment sizes, algorithms, and post-processing techniques are analyzed and discussed. The next three chapters address different aspects of providing sensory feedback. The first focus of sensory feedback process is the automatic phantom map detection. Many amputees have referred sensation from their missing hand on their residual limbs (phantom maps). This skin area can serve as a target for providing amputees with non-invasive tactile sensory feedback. One of the challenges of providing sensory feedback on the phantom map is to define the accurate boundary of each phantom digit because the phantom map distribution varies from person to person. Automatic phantom map detection methods based on four decomposition support vector machine algorithms and three sampling methods are proposed. The accuracy and training/ classification time of each algorithm using a dense stimulation array and two coarse stimulation arrays are presented and compared. The next focus of the thesis is to develop non-invasive tactile display. The design and psychophysical testing results of three types of non-invasive tactile feedback arrays are presented: two with vibrotactile modality and one with multi modality. For vibrotactile, two types of miniaturized vibrators: eccentric rotating masses (ERMs) and linear resonant actuators (LRAs) were first tested on healthy subjects and their effectiveness was compared. Then the ERMs are integrated into a vibrotactile glove to assess the feasibility of providing sensory feedback for unilateral upper limb amputees on the contralateral hand. For multimodal stimulation, miniature multimodal actuators integrating servomotors and vibrators were designed. The actuator can be used to deliver both high-frequency vibration and low-frequency pressures simultaneously. By utilizing two modalities at the same time, the actuator stimulates different types of mechanoreceptors and thus h

Advances in Bioengineering

The technological approach and the high level of innovation make bioengineering extremely dynamic and this forces researchers to continuous updating. It involves the publication of the results of the latest scientific research. This book covers a wide range of aspects and issues related to advances in bioengineering research with a particular focus on innovative technologies and applications. The book consists of 13 scientific contributions divided in four sections: Materials Science; Biosensors. Electronics and Telemetry; Light Therapy; Computing and Analysis Techniques

Non-invasive wearable sensing systems for continuous health monitoring and long-term behavior modeling

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2006.Includes bibliographical references (p. 212-228).Deploying new healthcare technologies for proactive health and elder care will become a major priority over the next decade, as medical care systems worldwide become strained by the aging populations. This thesis presents LiveNet, a distributed mobile system based on low-cost commodity hardware that can be deployed for a variety of healthcare applications. LiveNet embodies a flexible infrastructure platform intended for long-term ambulatory health monitoring with real-time data streaming and context classification capabilities. Using LiveNet, we are able to continuously monitor a wide range of physiological signals together with the user's activity and context, to develop a personalized, data-rich health profile of a user over time. Most clinical sensing technologies that exist have focused on accuracy and reliability, at the expense of cost-effectiveness, burden on the patient, and portability. Future proactive health technologies, on the other hand, must be affordable, unobtrusive, and non-invasive if the general population is going to adopt them.(cont.) In this thesis, we focus on the potential of using features derived from minimally invasive physiological and contextual sensors such as motion, speech, heart rate, skin conductance, and temperature/heat flux that can be used in combination with mobile technology to create powerful context-aware systems that are transparent to the user. In many cases, these non-invasive sensing technologies can completely replace more invasive diagnostic sensing for applications in long-term monitoring, behavior and physiology trending, and real-time proactive feedback and alert systems. Non-invasive sensing technologies are particularly important in ambulatory and continuous monitoring applications, where more cumbersome sensing equipment that is typically found in medical and clinical research settings is not usable. The research in this thesis demonstrates that it is possible to use simple non-invasive physiological and contextual sensing using the LiveNet system to accurately classify a variety of physiological conditions. We demonstrate that non-invasive sensing can be correlated to a variety of important physiological and behavioral phenomenon, and thus can serve as substitutes to more invasive and unwieldy forms of medical monitoring devices while still providing a high level of diagnostic power.(cont.) From this foundation, the LiveNet system is deployed in a number of studies to quantify physiological and contextual state. First, a number of classifiers for important health and general contextual cues such as activity state and stress level are developed from basic non-invasive physiological sensing. We then demonstrate that the LiveNet system can be used to develop systems that can classify clinically significant physiological and pathological conditions and that are robust in the presence of noise, motion artifacts, and other adverse conditions found in real-world situations. This is highlighted in a cold exposure and core body temperature study in collaboration with the U.S. Army Research Institute of Environmental Medicine. In this study, we show that it is possible to develop real-time implementations of these classifiers for proactive health monitors that can provide instantaneous feedback relevant in soldier monitoring applications. This thesis also demonstrates that the LiveNet platform can be used for long-term continuous monitoring applications to study physiological trends that vary slowly with time.(cont.) In a clinical study with the Psychiatry Department at the Massachusetts General Hospital, the LiveNet platform is used to continuously monitor clinically depressed patients during their stays on an in-patient ward for treatment. We show that we can accurately correlate physiology and behavior to depression state, as well as to track changes in depression state over time through the course of treatment. This study demonstrates how long-term physiology and behavioral changes can be captured to objectively measure medical treatment and medication efficacy. In another long-term monitoring study, the LiveNet platform is used to collect data on people's everyday behavior as they go through daily life. By collecting long-term behavioral data, we demonstrate the possibility of modeling and predicting high-level behavior using simple physiologic and contextual information derived solely from ambulatory mobile sensing technology.by Michael Sung.Ph.D

Aplicacion de modelos matematicos para el mantenimiento predictivo

149 p.La presente memoria de tesis presenta una revisión sobre la actividad de investigación aplicada que se ha realizado mediante varios proyectos relacionados con el mantenimiento predictivo asociado a procesos industriales. Uno de los resultados principales es la realización de una herramienta web que permite al operador consultar el tiempo estimado hasta el fallo en un proceso de mecanizado y junto a ello un histórico de datos del sistema. Se han obtenido otros resultados que generan una evolución en el mantenimiento de los sistemas estudiados, lo que reduce el coste y aumenta la productividad de estos. Para ello se han aplicado metodologías híbridas donde el objetivo principal radicaba en la creación de una metodología de mantenimiento predictivo para cada uno de los procesos y en algún caso la posibilidad de generalización de esta a procesos similares