IoT DEVELOPMENT FOR HEALTHY INDEPENDENT LIVING

The rise of internet connected devices has enabled the home with a vast amount of enhancements to make life more convenient. These internet connected devices can be used to form a community of devices known as the internet of things (IoT). There is great value in IoT devices to promote healthy independent living for older adults. Fall-related injuries has been one of the leading causes of death in older adults. For example, every year more than a third of people over 65 in the U.S. experience a fall, of which up to 30 percent result in moderate to severe injury. Therefore, this thesis proposes an IoT-based fall detection system for smart home environments that not only to send out alerts, but also launches interaction models, such as voice assistance and camera monitoring. Such connectivity could allow older adults to interact with the system without concern of a learning curve. The proposed IoT-based fall detection system will enable family and caregivers to be immediately notified of the event and remotely monitor the individual. Integrated within a smart home environment, the proposed IoT-based fall detection system can improve the quality of life among older adults. Along with the physical concerns of health, psychological stress is also a great concern among older adults. Stress has been linked to emotional and physical conditions such as depression, anxiety, heart attacks, stroke, etc. Increased susceptibility to stress may accelerate cognitive decline resulting in conversion of cognitively normal older adults to MCI (Mild Cognitive Impairment), and MCI to dementia. Thus, if stress can be measured, there can be countermeasures put in place to reduce stress and its negative effects on the psychological and physical health of older adults. This thesis presents a framework that can be used to collect and pre-process physiological data for the purpose of validating galvanic skin response (GSR), heart rate (HR), and emotional valence (EV) measurements against the cortisol and self-reporting benchmarks for stress detection. The results of this framework can be used for feature extraction to feed into a regression model for validating each combination of physiological measurement. Also, the potential of this framework to automate stress protocols like the Trier Social Stress Test (TSST) could pave the way for an IoT-based platform for automated stress detection and management

inContexto: framework to obtain people context using wearable sensors and social network sites

Mención Internacional en el título de doctorAmbient Intelligent (AmI) technology is developing fast and will promote a new generation of applications with some characteristics in the area of context awareness, anticipatory behavior, home security, monitoring, Health Care and video surveillance. AmI Environments should be surrounded by multiples sensors in order to discover people needs. These kind of scenarios are characterized by intelligent environments, which are able to recognize inconspicuously the presence of individuals and react to their needs. In such systems, people are conceived as the main actor, always in control, playing multiple roles, and this is perhaps the new real facet of research related to AmI: it introduces a new dimension creating synergies between the user and the environment. The AmI paradigm sets the principles to design pervasive and transparent infrastructures being capable of observing people without prying into their lives, and also adapting to their needs. There are several basis concepts to consider for retrieving people context, however the most important for users is that sensors devices must be unobtrusive. Many technologies are conceived as hand-held or wearable, taking advantage of the intelligence embedded in the environment. Mobile technologies and Social Network Sites make it possible to collect people information anywhere at anytime, and provide users with up-to-date information ready for decision-making processes. Nevertheless, the management of these sensors for collecting user context poses several challenges. Besides the limited computational capabilities of mobile devices, mobile systems face specific problems that cannot be solved by traditional knowledge management methodologies and tools, and thus require creative new solutions. This dissertation proposes a set of techniques, interfaces and algorithms for the implementation of inferring context information from new kind of sensors (Smartphones and Social Networking). The huge potential of both new sensors have motivated us to design a framework that can intelligently capture different sensory data in real-time. Smartphones may obtain and process physical phenomena from embedded sensors (Accelerometer, gyroscope, compass, magnetometer, proximity sensor, light sensor, GPS, etc.) and SNS the affective ones. Subsequently this information could be transmitted to remote locations without any human intervention. The mechanisms proposed here are based on the implementation of a basic framework that modifies information from the raw data to the most descriptive action. To this end, the development of this thesis starts from a inContexto framework which exploits off-the-shelf sensor-enabled mobile phones and SNS people presence to automatically infer people’s context. The main goals of our architecture are: (i) Collection, storage, analyse, and sharing of the user context information, (ii) Plug-and-play support for a wide variety of sensing devices, (iii) Privacy preservation of individuals sharing their data, and (iv) Easy application development. Furthermore our inContexto has been implemented to allow third party application to participate and improve people context.La Inteligencia Ambiental (AmI) está sufriendo una evolución rápida y en un futuro cercano saldrán a la luz una nueva generación de aplicaciones en el área de los sistemas basados en contexto, seguridad en el hogar, monitorización, salud y video vigilancia. Los entornos AmI se caracterizan por estar plagados de sensores los cuales, están encargados de capturar información de la gente que hay en ellos para describir sus necesidades. Este tipo de escenarios se caracterizan por ser entornos inteligentes, capaces de reconocer autónomamente la presencia de personas y reaccionar a sus necesidades. En dichos sistemas, las personas o usuarios se conciben como el actor principal, siempre en control, jugando múltiples roles, y esto es una nueva característica dentro del marco de la investigación relacionada con AmI: introducir nuevas sinergias entre el usuario y el entorno que le rodea. El paradigma AmI establece los principios para el diseño de arquitecturas generales que son capaces de capturar información relevante de las personas sin entrometerse en su vida, y además adaptar dicha información a las necesidades del mismo. Existen diferentes conceptos a tener en cuenta para la captura del contexto de las personas, sin embargo, el factor más importante es que los dispositivos usados deben ser transparentes para el usuario, es decir que trabajen de manera autónoma y sin la ayuda del mismo. Los nuevos teléfonos móviles inteligentes o smarpthone y las redes sociales permiten extraer información de las personas en cualquier lugar en cualquier momento, y así poder proporcionar a los usuarios ayuda para la toma de decisiones en las actividades de su vida real. Sin embargo, la gestión de la información de estos sensores, los cuales nos permiten inferir el contexto, plantean varios desafíos a resolver En primer lugar la limitación de las capacidades tanto computacionales como de disponibilidad (consumo de energía) de los dispositivos móviles, los sistemas móviles se enfrentan a problemas específicos que no pueden ser resueltos por las metodologías y herramientas de gestión del conocimiento tradicional, y por lo tanto requieren de nuevas soluciones creativas. En esta tesis se propone un conjunto de técnicas, interfaces y algoritmos para inferir la información de contexto de las personas a través de nuevos sensores, los cuales han sido infrautilizados hasta el momento como son los smartphone y Redes Sociales. Gracias al enorme potencial de estos nuevos sensores nos ha motivado para diseñar un framework que de manera transparente al usuario puede capturar diferentes datos sensoriales en tiempo real. A través de los Smartphone se puede obtener y procesar los fenómenos físicos (Correr, Andar, etc.) de las personas, utilizando los sensores embebidos como el acelerómetro, giroscopio, brújula, magnetómetro, sensor de proximidad, sensor de luz, GPS, etc. Además a través de las redes sociales se podría obtener información de los fenómenos afectivos del usuario. Posteriormente, esta información se transmitirá para su procesamiento y búsqueda de nuevas inferencias sin la colaboración del usuario, de manera transparente. Los mecanismos propuestos en esta tesis se basan en la aplicación de un framework, inContexto, que recoge la información de los sensores (Señales, palabras, etc.) para posteriormente generar una acción más descriptiva y entendible por el usuario. Los principales objetivos que presenta inContexto son: (i) Recogida, almacenamiento, análisis e intercambio de la información de contexto de usuario, (ii) el apoyo Plug-and-play para una amplia variedad de dispositivos, (iii) la preservación de privacidad de los las personas, y (iv) el desarrollo de nuevas aplicaciones fácilmente, permitiendo a través de inContexto el acceso a los datos a aplicaciones de terceros para mejorar la información recogida.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Juan Pavón Mestras.- Secretario: Miguel Ángel Patricio Guisado.- Vocal: Nayat Sánchez P

Effectiveness of Music-Based Respiratory Biofeedback in Reducing Stress during Visually Demanding Tasks

Biofeedback techniques have shown to be effective to manage stress and improve task performance. Biofeedback generally can be divided into two steps (i) measuring physiological functions (e.g. respiration, heart rate) via sensors and (ii) conveying the physiological signals to the user to improve self-awareness. Current systems require costly and invasive sensors to measure physiology, which are not comfortable and are not readily accessible to the general population. Additionally, current feedback mechanisms may be physically unpleasant or may hinder multitasking, especially in visually-demanding environments. To overcome these problems, we developed two tools: a music-based biofeedback tool that uses music as the medium of feedback, and a tool to measure breathing rate using a smartphone camera. The music biofeedback tool encourages slow breathing by adjusting the quality of the music in response to the user’s breathing rate. This intervention combines the benefits of biofeedback and music to help users regulate their stress response while performing a visual task (driving a car simulator). We evaluate the intervention on a 2×2 design with music and auditory biofeedback as independent variables. Our results indicate that music-biofeedback leads to lower arousal (as measured by electrodermal activity and heart rate variability) than music alone, auditory biofeedback alone, and a control condition. Music biofeedback also reduces driving errors when compared to the other three conditions. While our results suggest that the music-based biofeedback tool is useful and enjoyable, it still requires expensive physiological sensors which are intrusive in nature. Hence, we present a second tool to measure breathing rate in real-time via smartphone camera, which makes it easily accessible given the pervasiveness of smartphones. Our algorithm measures breathing rate by obtaining the photoplethysmographic signal and performing spectral analysis using Goertzel algorithm. We validated the method under a range of controlled breathing rate conditions, and our results show a high degree of agreement between our estimates and ground truth measurements obtained via standard respiratory sensors. These results show that it is possible to accurately compute breathing rate in real-time using a smartphone

Real Time Pedestrian Protection System: Pedestrian Environmental Awareness Detection and Augmented Reality Warning System

To improve the safety of the pedestrians via V2X communications, it is important to detect pedestrians' environmental awareness and give warnings to those with low environmental awareness. Based on the characteristics of pedestrians, a real-time algorithm was developed to detect pedestrians'environmental awareness, then an Augmented Reality (AR) warning system was developed to carry out the warning to the pedestrians who have low environmental awareness. In this study, the heart rate variability (HRV) analysis and phone position analysis were used to understand the mental state and distractions of pedestrians, the projection method was used to develop the AR warning system.The HRV analysis was used to detect the fatigue and alert states of the pedestrians, and the phone position was used to define the phone distractions of the pedestrians. Support Vector Machines (SVM) algorithms were used to classify the pedestrians' mental state. After the user analysis, the AR warning system was developed based on the perspective projection method. After the data collection and experiment, the results show that the accuracy of the pedestrian state detection was about 85%for the mental state detection and 100% for the phone position detection. Also,the AR warning system works well for to carry out the warning to the pedestrian.Master of Science in EngineeringIndustrial and Systems Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/145590/1/thesis v6.pdfDescription of thesis v6.pdf : Thesi

Real-Time Anomaly Detection in Cold Chain Transportation Using IoT Technology

There are approximately 88 million tonnes of food waste generated annually in the EU alone. Food spoilage during distribution accounts for some of this waste. To minimise this spoilage, it is of utmost importance to maintain the cold chain during the transportation of perishable foods such as meats, fruits, and vegetables. However, these products are often unfortunately wasted in large quantities when unpredictable failures occur in the refrigeration units of transport vehicles. This work proposes a real-time IoT anomaly detection system to detect equipment failures and provide decision support options to warehouse staff and delivery drivers, thus reducing potential food wastage. We developed a bespoke Internet of Things (IoT) solution for real-time product monitoring and alerting during cold chain transportation, which is based on the Digital Matter Eagle cellular data logger and two temperature probes. A visual dashboard was developed to allow logistics staff to perform monitoring, and business-defined temperature thresholds were used to develop a text and email decision support system, notifying relevant staff members if anomalies were detected. The IoT anomaly detection system was deployed with Musgrave Marketplace, Ireland’s largest grocery distributor, in three of their delivery vans operating in the greater Belfast area. Results show that the LTE-M cellular IoT system is power efficient and avoids sending false alerts due to the novel alerting system which was developed based on trip detection

Driver Drowsiness Detection: A Machine Learning Approach on Skin Conductance

The majority of car accidents worldwide are caused by drowsy drivers. Therefore, it is important to be able to detect when a driver is starting to feel drowsy in order to warn them before a serious accident occurs. Sometimes, drivers are not aware of their own drowsiness, but changes in their body signals can indicate that they are getting tired. Previous studies have used large and intrusive sensor systems that can be worn by the driver or placed in the vehicle to collect information about the driver’s physical status from a variety of signals that are either physiological or vehicle-related. This study focuses on the use of a single wrist device that is comfortable for the driver to wear and appropriate signal processing to detect drowsiness by analyzing only the physiological skin conductance (SC) signal. To determine whether the driver is drowsy, the study tests three ensemble algorithms and finds that the Boosting algorithm is the most effective in detecting drowsiness with an accuracy of 89.4%. The results of this study show that it is possible to identify when a driver is drowsy using only signals from the skin on the wrist, and this encourages further research to develop a real-time warning system for early detection of drowsiness

Approaches, applications, and challenges in physiological emotion recognition — a tutorial overview

An automatic emotion recognition system can serve as a fundamental framework for various applications in daily life from monitoring emotional well-being to improving the quality of life through better emotion regulation. Understanding the process of emotion manifestation becomes crucial for building emotion recognition systems. An emotional experience results in changes not only in interpersonal behavior but also in physiological responses. Physiological signals are one of the most reliable means for recognizing emotions since individuals cannot consciously manipulate them for a long duration. These signals can be captured by medical-grade wearable devices, as well as commercial smart watches and smart bands. With the shift in research direction from laboratory to unrestricted daily life, commercial devices have been employed ubiquitously. However, this shift has introduced several challenges, such as low data quality, dependency on subjective self-reports, unlimited movement-related changes, and artifacts in physiological signals. This tutorial provides an overview of practical aspects of emotion recognition, such as experiment design, properties of different physiological modalities, existing datasets, suitable machine learning algorithms for physiological data, and several applications. It aims to provide the necessary psychological and physiological backgrounds through various emotion theories and the physiological manifestation of emotions, thereby laying a foundation for emotion recognition. Finally, the tutorial discusses open research directions and possible solutions

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