3,396 research outputs found
A Spark Of Emotion: The Impact of Electrical Facial Muscle Activation on Emotional State and Affective Processing
Facial feedback, which involves the brain receiving information about the activation of facial muscles, has the potential to influence our emotional states and judgments. The extent to which this applies is still a matter of debate, particularly considering a failed replication of a seminal study. One factor contributing to the lack of replication in facial feedback effects may be the imprecise manipulation of facial muscle activity in terms of both degree and timing. To overcome these limitations, this thesis proposes a non-invasive method for inducing precise facial muscle contractions, called facial neuromuscular electrical stimulation (fNMES). I begin by presenting a systematic literature review that lays the groundwork for standardising the use of fNMES in psychological research, by evaluating its application in existing studies. This review highlights two issues, the lack of use of fNMES in psychology research and the lack of parameter reporting. I provide practical recommendations for researchers interested in implementing fNMES. Subsequently, I conducted an online experiment to investigate participants' willingness to participate in fNMES research. This experiment revealed that concerns over potential burns and involuntary muscle movements are significant deterrents to participation. Understanding these anxieties is critical for participant management and expectation setting. Subsequently, two laboratory studies are presented that investigated the facial FFH using fNMES. The first study showed that feelings of happiness and sadness, and changes in peripheral physiology, can be induced by stimulating corresponding facial muscles with 5–seconds of fNMES. The second experiment showed that fNMES-induced smiling alters the perception of ambiguous facial emotions, creating a bias towards happiness, and alters neural correlates of face processing, as measured with event-related potentials (ERPs). In summary, the thesis presents promising results for testing the facial feedback hypothesis with fNMES and provides practical guidelines and recommendations for researchers interested in using fNMES for psychological research
The ins and outs of open-angle Glaucoma:drugs, diet, and defecation
Glaucoma is the leading cause of irreversible blindness and second leading cause of blindness. The primary aim of this thesis is to provide insight into the role of systemic effectsin the pathophysiology of OAG.<br/
Libro de Abstracts | VIII Jornadas de Investigación y Doctorado: “Ética en la Investigación Científica”
El objetivo de estas Jornadas es promover el intercambio científico entre estudiantes de doctorado,
fomentando la participación, el debate y la discusión, de aspectos científicos tan importantes como la
ética de la investigación.
Para poner en valor el papel de los doctores en la sociedad, no podemos pasar por alto las competencias transversales
que estos deben adquirir en su formación como doctores. Si bien la ética es algo fundamental en todas
las facetas de la vida, en el caso de los investigadores cobra especial relevancia, ya que son generadores de conocimiento sobre el que se asentarán futuros desarrollos y políticas de interés para toda la sociedad. Por lo tanto,
con el fin de incrementar la proyección social de las investigaciones llevadas a cabo y la proyección profesional
de los doctores, es importante incidir en su formación ética.
La base de la investigación académica está construida sobre la confianza. Los investigadores confían en que
los resultados informados por otros son veraces. La sociedad confía en que los resultados de la investigación
reflejan un intento honesto por parte de los científicos de describir el mundo de forma precisa. Pero esta confianza
sólo perdurará si la comunidad científica transmite los valores asociados a la conducta de la ética de investigación.
Por este motivo, la Universidad juega un papel muy importante en la formación de los doctores en
cuestiones éticas que son inherentes al método científico y a la generación de conocimiento. Dentro de las universidades, las Escuelas Internacionales de Doctorado, con nuestros recursos, aptitudes y espacio de influencia,
nos convertimos en actores clave para promover actitudes éticas entre los doctorandos, y estas Jornadas son
una oportunidad muy valiosa para tratar este tema.
Las ramas de conocimiento que se incluyen para estas Jornadas son las derivadas de los programas de doctorado
de la EIDUCAM:
-Ciencias de la Salud
-Tecnologías de la Computación e Ingeniería Ambiental
-Ciencias Sociales
-Ciencias del DeporteActividad Física y DeporteAdministración y Dirección de EmpresasAgricultura y VeterinariaArte y HumanidadesCiencias AmbientalesCiencias de la AlimentaciónCiencias de la ComunicaciónCiencias ReligiosasDerechoEducaciónEnfermeríaFarmaciaIdiomasIngeniería, Industria y ConstrucciónMedicinaOdontologíaPodologíaPsicologíaTerapia y RehabilitaciónTurism
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring
Artificially intelligent perception is increasingly present in the lives of
every one of us. Vehicles are no exception, (...) In the near future, pattern
recognition will have an even stronger role in vehicles, as self-driving cars
will require automated ways to understand what is happening around (and within)
them and act accordingly. (...) This doctoral work focused on advancing
in-vehicle sensing through the research of novel computer vision and pattern
recognition methodologies for both biometrics and wellbeing monitoring. The
main focus has been on electrocardiogram (ECG) biometrics, a trait well-known
for its potential for seamless driver monitoring. Major efforts were devoted to
achieving improved performance in identification and identity verification in
off-the-person scenarios, well-known for increased noise and variability. Here,
end-to-end deep learning ECG biometric solutions were proposed and important
topics were addressed such as cross-database and long-term performance,
waveform relevance through explainability, and interlead conversion. Face
biometrics, a natural complement to the ECG in seamless unconstrained
scenarios, was also studied in this work. The open challenges of masked face
recognition and interpretability in biometrics were tackled in an effort to
evolve towards algorithms that are more transparent, trustworthy, and robust to
significant occlusions. Within the topic of wellbeing monitoring, improved
solutions to multimodal emotion recognition in groups of people and
activity/violence recognition in in-vehicle scenarios were proposed. At last,
we also proposed a novel way to learn template security within end-to-end
models, dismissing additional separate encryption processes, and a
self-supervised learning approach tailored to sequential data, in order to
ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022
to the University of Port
Blending the Material and Digital World for Hybrid Interfaces
The development of digital technologies in the 21st century is progressing continuously and new device classes such as tablets, smartphones or smartwatches are finding their way into our everyday lives. However, this development also poses problems, as these prevailing touch and gestural interfaces often lack tangibility, take little account of haptic qualities and therefore require full attention from their users. Compared to traditional tools and analog interfaces, the human skills to experience and manipulate material in its natural environment and context remain unexploited. To combine the best of both, a key question is how it is possible to blend the material world and digital world to design and realize novel hybrid interfaces in a meaningful way. Research on Tangible User Interfaces (TUIs) investigates the coupling between physical objects and virtual data. In contrast, hybrid interfaces, which specifically aim to digitally enrich analog artifacts of everyday work, have not yet been sufficiently researched and systematically discussed.
Therefore, this doctoral thesis rethinks how user interfaces can provide useful digital functionality while maintaining their physical properties and familiar patterns of use in the real world. However, the development of such hybrid interfaces raises overarching research questions about the design: Which kind of physical interfaces are worth exploring? What type of digital enhancement will improve existing interfaces? How can hybrid interfaces retain their physical properties while enabling new digital functions? What are suitable methods to explore different design? And how to support technology-enthusiast users in prototyping?
For a systematic investigation, the thesis builds on a design-oriented, exploratory and iterative development process using digital fabrication methods and novel materials. As a main contribution, four specific research projects are presented that apply and discuss different visual and interactive augmentation principles along real-world applications. The applications range from digitally-enhanced paper, interactive cords over visual watch strap extensions to novel prototyping tools for smart garments. While almost all of them integrate visual feedback and haptic input, none of them are built on rigid, rectangular pixel screens or use standard input modalities, as they all aim to reveal new design approaches. The dissertation shows how valuable it can be to rethink familiar, analog applications while thoughtfully extending them digitally. Finally, this thesis’ extensive work of engineering versatile research platforms is accompanied by overarching conceptual work, user evaluations and technical experiments, as well as literature reviews.Die Durchdringung digitaler Technologien im 21. Jahrhundert schreitet stetig voran und neue Geräteklassen wie Tablets, Smartphones oder Smartwatches erobern unseren Alltag. Diese Entwicklung birgt aber auch Probleme, denn die vorherrschenden berührungsempfindlichen Oberflächen berücksichtigen kaum haptische Qualitäten und erfordern daher die volle Aufmerksamkeit ihrer Nutzer:innen. Im Vergleich zu traditionellen Werkzeugen und analogen Schnittstellen bleiben die menschlichen Fähigkeiten ungenutzt, die Umwelt mit allen Sinnen zu begreifen und wahrzunehmen. Um das Beste aus beiden Welten zu vereinen, stellt sich daher die Frage, wie neuartige hybride Schnittstellen sinnvoll gestaltet und realisiert werden können, um die materielle und die digitale Welt zu verschmelzen. In der Forschung zu Tangible User Interfaces (TUIs) wird die Verbindung zwischen physischen Objekten und virtuellen Daten untersucht. Noch nicht ausreichend erforscht wurden hingegen hybride Schnittstellen, die speziell darauf abzielen, physische Gegenstände des Alltags digital zu erweitern und anhand geeigneter Designparameter und Entwurfsräume systematisch zu untersuchen.
In dieser Dissertation wird daher untersucht, wie Materialität und Digitalität nahtlos ineinander übergehen können. Es soll erforscht werden, wie künftige Benutzungsschnittstellen nützliche digitale Funktionen bereitstellen können, ohne ihre physischen Eigenschaften und vertrauten Nutzungsmuster in der realen Welt zu verlieren. Die Entwicklung solcher hybriden Ansätze wirft jedoch übergreifende Forschungsfragen zum Design auf: Welche Arten von physischen Schnittstellen sind es wert, betrachtet zu werden? Welche Art von digitaler Erweiterung verbessert das Bestehende? Wie können hybride Konzepte ihre physischen Eigenschaften beibehalten und gleichzeitig neue digitale Funktionen ermöglichen? Was sind geeignete Methoden, um verschiedene Designs zu erforschen? Wie kann man Technologiebegeisterte bei der Erstellung von Prototypen unterstützen?
Für eine systematische Untersuchung stützt sich die Arbeit auf einen designorientierten, explorativen und iterativen Entwicklungsprozess unter Verwendung digitaler Fabrikationsmethoden und neuartiger Materialien. Im Hauptteil werden vier Forschungsprojekte vorgestellt, die verschiedene visuelle und interaktive Prinzipien entlang realer Anwendungen diskutieren. Die Szenarien reichen von digital angereichertem Papier, interaktiven Kordeln über visuelle Erweiterungen von Uhrarmbändern bis hin zu neuartigen Prototyping-Tools für intelligente Kleidungsstücke. Um neue Designansätze aufzuzeigen, integrieren nahezu alle visuelles Feedback und haptische Eingaben, um Alternativen zu Standard-Eingabemodalitäten auf starren Pixelbildschirmen zu schaffen. Die Dissertation hat gezeigt, wie wertvoll es sein kann, bekannte, analoge Anwendungen zu überdenken und sie dabei gleichzeitig mit Bedacht digital zu erweitern. Dabei umfasst die vorliegende Arbeit sowohl realisierte technische Forschungsplattformen als auch übergreifende konzeptionelle Arbeiten, Nutzerstudien und technische Experimente sowie die Analyse existierender Forschungsarbeiten
Autonomous Radar-based Gait Monitoring System
Features related to gait are fundamental metrics of human motion [1]. Human gait has been shown to be a valuable and feasible clinical marker to determine the risk of physical and mental functional decline [2], [3]. Technologies that detect changes in people’s gait patterns, especially older adults, could support the detection, evaluation, and monitoring of parameters related to changes in mobility, cognition, and frailty. Gait assessment has the potential to be leveraged as a clinical measurement as it is not limited to a specific health care discipline and is a consistent and sensitive test [4].
A wireless technology that uses electromagnetic waves (i.e., radar) to continually measure gait parameters at home or in a hospital without a clinician’s participation has been proposed as a suitable solution [3], [5]. This approach is based on the interaction between electromagnetic waves with humans and how their bodies impact the surrounding and scattered wireless signals. Since this approach uses wireless waves, people do not need to wear or carry a device on their bodies. Additionally, an electromagnetic wave wireless sensor has no privacy issues because there is no video-based camera.
This thesis presents the design and testing of a radar-based contactless system that can monitor people’s gait patterns and recognize their activities in a range of indoor environments frequently and accurately. In this thesis, the use of commercially available radars for gait monitoring is investigated, which offers opportunities to implement unobtrusive and contactless gait monitoring and activity recognition. A novel fast and easy-to-implement gait extraction algorithm that enables an individual’s spatiotemporal gait parameter extraction at each gait cycle using a single FMCW (Frequency Modulated Continuous Wave) radar is proposed. The proposed system detects changes in gait that may be the signs of changes in mobility, cognition, and frailty, particularly for older adults in individual’s homes, retirement homes and long-term care facilities retirement homes. One of the straightforward applications for gait monitoring using radars is in corridors and hallways, which are commonly available in most residential homes, retirement, and long-term care homes. However, walls in the hallway have a strong “clutter” impact, creating multipath due to the wide beam of commercially available radar antennas. The multipath reflections could result in an inaccurate gait measurement because gait extraction algorithms employ the assumption that the maximum reflected signals come from the torso of the walking person (rather than indirect reflections or multipath) [6].
To address the challenges of hallway gait monitoring, two approaches were used: (1) a novel signal processing method and (2) modifying the radar antenna using a hyperbolic lens. For the first approach, a novel algorithm based on radar signal processing, unsupervised learning, and a subject detection, association and tracking method is proposed. This proposed algorithm could be paired with any type of multiple-input multiple-output (MIMO) or single-input multiple-output (SIMO) FMCW radar to capture human gait in a highly cluttered environment without needing radar antenna alteration. The algorithm functionality was validated by capturing spatiotemporal gait values (e.g., speed, step points, step time, step length, and step count) of people walking in a hallway. The preliminary results demonstrate the promising potential of the algorithm to accurately monitor gait in hallways, which increases opportunities for its applications in institutional and home environments. For the second approach, an in-package hyperbola-based lens antenna was designed that can be integrated with a radar module package empowered by the fast and easy-to-implement gait extraction method. The system functionality was successfully validated by capturing the spatiotemporal gait values of people walking in a hallway filled with metallic cabinets. The results achieved in this work pave the way to explore the use of stand-alone radar-based sensors in long hallways for day-to-day long-term monitoring of gait parameters of older adults or other populations.
The possibility of the coexistence of multiple walking subjects is high, especially in long-term care facilities where other people, including older adults, might need assistance during walking. GaitRite and wearables are not able to assess multiple people’s gait at the same time using only one device [7], [8]. In this thesis, a novel radar-based algorithm is proposed that is capable of tracking multiple people or extracting walking speed of a participant with the coexistence of other people. To address the problem of tracking and monitoring multiple walking people in a cluttered environment, a novel iterative framework based on unsupervised learning and advanced signal processing was developed and tested to analyze the reflected radio signals and extract walking movements and trajectories in a hallway environment. Advanced algorithms were developed to remove multipath effects or ghosts created due to the interaction between walking subjects and stationary objects, to identify and separate reflected signals of two participants walking at a close distance, and to track multiple subjects over time. This method allows the extraction of walking speed in multiple closely-spaced subjects simultaneously, which is distinct from previous approaches where the speed of only one subject was obtained. The proposed multiple-people gait monitoring was assessed with 22 participants who participated in a bedrest (BR) study conducted at McGill University Health Centre (MUHC).
The system functionality also was assessed for in-home applications. In this regard, a cloud-based system is proposed for non-contact, real-time recognition and monitoring of physical activities and walking periods within a domestic environment. The proposed system employs standalone Internet of Things (IoT)-based millimeter wave radar devices and deep learning models to enable autonomous, free-living activity recognition and gait analysis. Range-Doppler maps generated from a dataset of real-life in-home activities are used to train deep learning models. The performance of several deep learning models was evaluated based on accuracy and prediction time, with the gated recurrent network (GRU) model selected for real-time deployment due to its balance of speed and accuracy compared to 2D Convolutional Neural Network Long Short-Term Memory (2D-CNNLSTM) and Long Short-Term Memory (LSTM) models. In addition to recognizing and differentiating various activities and walking periods, the system also records the subject’s activity level over time, washroom use frequency, sleep/sedentary/active/out-of-home durations, current state, and gait parameters. Importantly, the system maintains privacy by not requiring the subject to wear or carry any additional devices
Tradition and Innovation in Construction Project Management
This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings
A Taxonomy of Freehand Grasping Patterns in Virtual Reality
Grasping is the most natural and primary interaction paradigm people perform every day, which allows us to pick up and manipulate objects around us such as drinking a cup of coffee or writing with a pen. Grasping has been highly explored in real environments, to understand and structure the way people grasp and interact with objects by presenting categories, models and theories for grasping approach. Due to the complexity of the human hand, classifying grasping knowledge to provide meaningful insights is a challenging task, which led to researchers developing grasp taxonomies to provide guidelines for emerging grasping work (such as in anthropology, robotics and hand surgery) in a systematic way.
While this body of work exists for real grasping, the nuances of grasping transfer in virtual environments is unexplored. The emerging development of robust hand tracking sensors for virtual devices now allow the development of grasp models that enable VR to simulate real grasping interactions. However, present work has not yet explored the differences and nuances that are present in virtual grasping compared to real object grasping, which means that virtual systems that create grasping models based on real grasping knowledge, might make assumptions which are yet to be proven true or untrue around the way users intuitively grasp and interact with virtual objects.
To address this, this thesis presents the first user elicitation studies to explore grasping patterns directly in VR. The first study presents main similarities and differences between real and virtual object grasping, the second study furthers this by exploring how virtual object shape influences grasping patterns, the third study focuses on visual thermal cues and how this influences grasp metrics, and the fourth study focuses on understanding other object characteristics such as stability and complexity and how they influence grasps in VR. To provide structured insights on grasping interactions in VR, the results are synthesized in the first VR Taxonomy of Grasp Types, developed following current methods for developing grasping and HCI taxonomies and re-iterated to
present an updated and more complete taxonomy.
Results show that users appear to mimic real grasping behaviour in VR, however they also illustrate that users present issues around object size estimation and generally a lower variability in grasp types is used. The taxonomy shows that only five grasps account for the majority of grasp data in VR, which can be used for computer systems aiming to achieve natural and intuitive interactions at lower computational cost. Further, findings show that virtual object characteristics such as shape, stability and complexity as well as visual cues for temperature influence grasp metrics such as aperture, category, type, location and dimension. These changes in grasping patterns together with virtual object categorisation methods can be used to inform design decisions when developing intuitive interactions and virtual objects and environments and therefore taking a step forward in achieving natural grasping interaction in VR
Analysing the Movement and Behaviour of Housed Dairy Cows
Cows in modern dairy systems are at risk of comprised health and welfare, and monitoring changes in behaviour can help identify early-warning signs. This thesis uses a local positioning system to detect changes in group-level behaviour. The proximity interaction network structure and consistency of a herd housed in a closed barn on a commercial farm in Essex is explored. Next, the network structure, alongside group-level space-use patterns, on the commercial farm in Essex are compared to those of a second dairy cow herd housed in an open barn (RVC Research farm). In the subsequent chapters, the relationship between barn temperature and bunching behaviour, a potentially maladaptive response to warmer than average temperatures, was investigated in both herds, through various bunching metrics: range size, inter-cow distance and nearest neighbour distance. The herd on the commercial farm in Essex was highly connected and temporally unstable, with inter-individual variation in interactions in the non-feeding zone, and social differentiation across functional zones. No social assortment by parity, days in milk or lameness state was detected. The herd on the RVC Research farm were less connected than the herd on the commercial farm in Essex. Inter-individual variation in proximity interactions was found in the feeding zoneof the RVC Research farm, alongside social differentiation across functional zones. Cows showed preferences for specific areas of the non-feeding zones, more so on the commercial farm in Essex than on the RVC Research farm. Cows increased their bunching behaviour ≥ 20°C in terms of all bunching metrics on the commercial farm in Essex. This pattern was observed for nearest neighbour distance on the RVC Research farm ≥ 15.91°C. This thesis demonstrates the use of precision livestock farming to monitor changes in group-level behaviour to improve the health and welfare of livestock
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