Using Smartphone Sensor Paradata and Personalized Machine Learning Models to Infer Participants' Well-being: Ecological Momentary Assessment

Background: Sensors embedded in smartphones allow for the passive momentary quantification of people’s states in the context of their daily lives in real time. Such data could be useful for alleviating the burden of ecological momentary assessments and increasing utility in clinical assessments. Despite existing research on using passive sensor data to assess participants’ moment-to-moment states and activity levels, only limited research has investigated temporally linking sensor assessment and self-reported assessment to further integrate the 2 methodologies. Objective: We investigated whether sparse movement-related sensor data can be used to train machine learning models that are able to infer states of individuals’ work-related rumination, fatigue, mood, arousal, life engagement, and sleep quality. Sensor data were only collected while the participants filled out the questionnaires on their smartphones. Methods: We trained personalized machine learning models on data from employees (N=158) who participated in a 3-week ecological momentary assessment study. Results: The results suggested that passive smartphone sensor data paired with personalized machine learning models can be used to infer individuals’ self-reported states at later measurement occasions. The mean R 2 was approximately 0.31 (SD 0.29), and more than half of the participants (119/158, 75.3%) had an R 2 of ≥0.18. Accuracy was only slightly attenuated compared with earlier studies and ranged from 38.41% to 51.38%. Conclusions: Personalized machine learning models and temporally linked passive sensing data have the capability to infer a sizable proportion of variance in individuals’ daily self-reported states. Further research is needed to investigate factors that affect the accuracy and reliability of the inference

New Secure IoT Architectures, Communication Protocols and User Interaction Technologies for Home Automation, Industrial and Smart Environments

Programa Oficial de Doutoramento en Tecnoloxías da Información e das Comunicacións en Redes Móbiles. 5029V01Tese por compendio de publicacións[Abstract] The Internet of Things (IoT) presents a communication network where heterogeneous physical devices such as vehicles, homes, urban infrastructures or industrial machinery are interconnected and share data. For these communications to be successful, it is necessary to integrate and embed electronic devices that allow for obtaining environmental information (sensors), for performing physical actuations (actuators) as well as for sending and receiving data (network interfaces). This integration of embedded systems poses several challenges. It is needed for these devices to present very low power consumption. In many cases IoT nodes are powered by batteries or constrained power supplies. Moreover, the great amount of devices needed in an IoT network makes power e ciency one of the major concerns of these deployments, due to the cost and environmental impact of the energy consumption. This need for low energy consumption is demanded by resource constrained devices, con icting with the second major concern of IoT: security and data privacy. There are critical urban and industrial systems, such as tra c management, water supply, maritime control, railway control or high risk industrial manufacturing systems such as oil re neries that will obtain great bene ts from IoT deployments, for which non-authorized access can posse severe risks for public safety. On the other hand, both these public systems and the ones deployed on private environments (homes, working places, malls) present a risk for the privacy and security of their users. These IoT deployments need advanced security mechanisms, both to prevent access to the devices and to protect the data exchanged by them. As a consequence, it is needed to improve two main aspects: energy e ciency of IoT devices and the use of lightweight security mechanisms that can be implemented by these resource constrained devices but at the same time guarantee a fair degree of security. The huge amount of data transmitted by this type of networks also presents another challenge. There are big data systems capable of processing large amounts of data, but with IoT the granularity and dispersion of the generated information presents a new scenario very di erent from the one existing nowadays. Forecasts anticipate that there will be a growth from the 15 billion installed devices in 2015 to more than 75 billion devices in 2025. Moreover, there will be much more services exploiting the data produced by these networks, meaning the resulting tra c will be even higher. The information must not only be processed in real time, but data mining processes will have to be performed to historical data. The main goal of this Ph.D. thesis is to analyze each one of the previously described challenges and to provide solutions that allow for an adequate adoption of IoT in Industrial, domestic and, in general, any scenario that can obtain any bene t from the interconnection and exibility that IoT brings.[Resumen] La internet de las cosas (IoT o Internet of Things) representa una red de intercomunicaciones en la que participan dispositivos físicos de toda índole, como vehículos, viviendas, electrodomésticos, infraestructuras urbanas o maquinaria y dispositivos industriales. Para que esta comunicación se pueda llevar a cabo es necesario integrar elementos electr onicos que permitan obtener informaci on del entorno (sensores), realizar acciones f sicas (actuadores) y enviar y recibir la informaci on necesaria (interfaces de comunicaciones de red). La integración y uso de estos sistemas electrónicos embebidos supone varios retos. Es necesario que dichos dispositivos presenten un consumo reducido. En muchos casos deberían ser alimentados por baterías o fuentes de alimentación limitadas. Además, la gran cantidad de dispositivos que involucra la IoT hace necesario que la e ciencia energética de los mismos sea una de las principales preocupaciones, por el coste e implicaciones medioambientales que supone el consumo de electricidad de los mismos. Esta necesidad de limitar el consumo provoca que dichos dispositivos tengan unas prestaciones muy limitadas, lo que entra en conflicto con la segunda mayor preocupación de la IoT: la seguridad y privacidad de los datos. Por un lado existen sistemas críticos urbanos e industriales, como puede ser la regulación del tráfi co, el control del suministro de agua, el control marítimo, el control ferroviario o los sistemas de producción industrial de alto riesgo, como refi nerías, que son claros candidatos a benefi ciarse de la IoT, pero cuyo acceso no autorizado supone graves problemas de seguridad ciudadana. Por otro lado, tanto estos sistemas de naturaleza publica, como los que se desplieguen en entornos privados (viviendas, entornos de trabajo o centros comerciales, entre otros) suponen un riesgo para la privacidad y también para la seguridad de los usuarios. Todo esto hace que sean necesarios mecanismos de seguridad avanzados, tanto de acceso a los dispositivos como de protección de los datos que estos intercambian. En consecuencia, es necesario avanzar en dos aspectos principales: la e ciencia energética de los dispositivos y el uso de mecanismos de seguridad e ficientes, tanto computacional como energéticamente, que permitan la implantación de la IoT sin comprometer la seguridad y la privacidad de los usuarios. Por otro lado, la ingente cantidad de información que estos sistemas puede llegar a producir presenta otros dos retos que deben ser afrontados. En primer lugar, el tratamiento y análisis de datos toma una nueva dimensión. Existen sistemas de big data capaces de procesar cantidades enormes de información, pero con la internet de las cosas la granularidad y dispersión de los datos plantean un escenario muy distinto al actual. La previsión es pasar de 15.000.000.000 de dispositivos instalados en 2015 a más de 75.000.000.000 en 2025. Además existirán multitud de servicios que harán un uso intensivo de estos dispositivos y de los datos que estos intercambian, por lo que el volumen de tráfico será todavía mayor. Asimismo, la información debe ser procesada tanto en tiempo real como a posteriori sobre históricos, lo que permite obtener información estadística muy relevante en diferentes entornos. El principal objetivo de la presente tesis doctoral es analizar cada uno de estos retos (e ciencia energética, seguridad, procesamiento de datos e interacción con el usuario) y plantear soluciones que permitan una correcta adopción de la internet de las cosas en ámbitos industriales, domésticos y en general en cualquier escenario que se pueda bene ciar de la interconexión y flexibilidad de acceso que proporciona el IoT.[Resumo] O internet das cousas (IoT ou Internet of Things) representa unha rede de intercomunicaci óns na que participan dispositivos físicos moi diversos, coma vehículos, vivendas, electrodomésticos, infraestruturas urbanas ou maquinaria e dispositivos industriais. Para que estas comunicacións se poidan levar a cabo é necesario integrar elementos electrónicos que permitan obter información da contorna (sensores), realizar accións físicas (actuadores) e enviar e recibir a información necesaria (interfaces de comunicacións de rede). A integración e uso destes sistemas electrónicos integrados supón varios retos. En primeiro lugar, é necesario que estes dispositivos teñan un consumo reducido. En moitos casos deberían ser alimentados por baterías ou fontes de alimentación limitadas. Ademais, a gran cantidade de dispositivos que se empregan na IoT fai necesario que a e ciencia enerxética dos mesmos sexa unha das principais preocupacións, polo custo e implicacións medioambientais que supón o consumo de electricidade dos mesmos. Esta necesidade de limitar o consumo provoca que estes dispositivos teñan unhas prestacións moi limitadas, o que entra en con ito coa segunda maior preocupación da IoT: a seguridade e privacidade dos datos. Por un lado existen sistemas críticos urbanos e industriais, como pode ser a regulación do tráfi co, o control de augas, o control marítimo, o control ferroviario ou os sistemas de produción industrial de alto risco, como refinerías, que son claros candidatos a obter benefi cios da IoT, pero cuxo acceso non autorizado supón graves problemas de seguridade cidadá. Por outra parte tanto estes sistemas de natureza pública como os que se despreguen en contornas privadas (vivendas, contornas de traballo ou centros comerciais entre outros) supoñen un risco para a privacidade e tamén para a seguridade dos usuarios. Todo isto fai que sexan necesarios mecanismos de seguridade avanzados, tanto de acceso aos dispositivos como de protección dos datos que estes intercambian. En consecuencia, é necesario avanzar en dous aspectos principais: a e ciencia enerxética dos dispositivos e o uso de mecanismos de seguridade re cientes, tanto computacional como enerxéticamente, que permitan o despregue da IoT sen comprometer a seguridade e a privacidade dos usuarios. Por outro lado, a inxente cantidade de información que estes sistemas poden chegar a xerar presenta outros retos que deben ser tratados. O tratamento e a análise de datos toma unha nova dimensión. Existen sistemas de big data capaces de procesar cantidades enormes de información, pero coa internet das cousas a granularidade e dispersión dos datos supón un escenario moi distinto ao actual. A previsión e pasar de 15.000.000.000 de dispositivos instalados no ano 2015 a m ais de 75.000.000.000 de dispositivos no ano 2025. Ademais existirían multitude de servizos que farían un uso intensivo destes dispositivos e dos datos que intercambian, polo que o volume de tráfico sería aínda maior. Do mesmo xeito a información debe ser procesada tanto en tempo real como posteriormente sobre históricos, o que permite obter información estatística moi relevante en diferentes contornas. O principal obxectivo da presente tese doutoral é analizar cada un destes retos (e ciencia enerxética, seguridade, procesamento de datos e interacción co usuario) e propor solucións que permitan unha correcta adopción da internet das cousas en ámbitos industriais, domésticos e en xeral en todo aquel escenario que se poda bene ciar da interconexión e flexibilidade de acceso que proporciona a IoT

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

Artificial Intelligence and Cognitive Computing

Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that

Selected Papers from the 5th International Electronic Conference on Sensors and Applications

This Special Issue comprises selected papers from the proceedings of the 5th International Electronic Conference on Sensors and Applications, held on 15–30 November 2018, on sciforum.net, an online platform for hosting scholarly e-conferences and discussion groups. In this 5th edition of the electronic conference, contributors were invited to provide papers and presentations from the field of sensors and applications at large, resulting in a wide variety of excellent submissions and topic areas. Papers which attracted the most interest on the web or that provided a particularly innovative contribution were selected for publication in this collection. These peer-reviewed papers are published with the aim of rapid and wide dissemination of research results, developments, and applications. We hope this conference series will grow rapidly in the future and become recognized as a new way and venue by which to (electronically) present new developments related to the field of sensors and their applications

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise