132 research outputs found

    PhysioKit: An Open-Source, Low-Cost Physiological Computing Toolkit for Single- and Multi-User Studies

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    The proliferation of physiological sensors opens new opportunities to explore interactions, conduct experiments and evaluate the user experience with continuous monitoring of bodily functions. Commercial devices, however, can be costly or limit access to raw waveform data, while low-cost sensors are efforts-intensive to setup. To address these challenges, we introduce PhysioKit, an open-source, low-cost physiological computing toolkit. PhysioKit provides a one-stop pipeline consisting of (i) a sensing and data acquisition layer that can be configured in a modular manner per research needs, and (ii) a software application layer that enables data acquisition, real-time visualization and machine learning (ML)-enabled signal quality assessment. This also supports basic visual biofeedback configurations and synchronized acquisition for co-located or remote multi-user settings. In a validation study with 16 participants, PhysioKit shows strong agreement with research-grade sensors on measuring heart rate and heart rate variability metrics data. Furthermore, we report usability survey results from 10 small-project teams (44 individual members in total) who used PhysioKit for 4–6 weeks, providing insights into its use cases and research benefits. Lastly, we discuss the extensibility and potential impact of the toolkit on the research community

    Beyond mobile apps: a survey of technologies for mental well-being

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    Mental health problems are on the rise globally and strain national health systems worldwide. Mental disorders are closely associated with fear of stigma, structural barriers such as financial burden, and lack of available services and resources which often prohibit the delivery of frequent clinical advice and monitoring. Technologies for mental well-being exhibit a range of attractive properties, which facilitate the delivery of state-of-the-art clinical monitoring. This review article provides an overview of traditional techniques followed by their technological alternatives, sensing devices, behaviour changing tools, and feedback interfaces. The challenges presented by these technologies are then discussed with data collection, privacy, and battery life being some of the key issues which need to be carefully considered for the successful deployment of mental health toolkits. Finally, the opportunities this growing research area presents are discussed including the use of portable tangible interfaces combining sensing and feedback technologies. Capitalising on the data these ubiquitous devices can record, state of the art machine learning algorithms can lead to the development of robust clinical decision support tools towards diagnosis and improvement of mental well-being delivery in real-time

    Exploring Artistic Visualization of Physiological Signals for Mindfulness and Relaxation: A Pilot Study

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    Mindfulness and relaxation techniques for mental health are increasingly being explored in the human-computer interaction community. Physiological signals and their visualization have often been exploited together in a form of biofeedback with other intervention methods. Here, we aim to contribute to the body of existing work on biofeedback interfaces for mindfulness, with a particular focus on incorporating artistic effects into physiological signal visualization. With an implemented artistic biofeedback interface, we conduct a pilot study where 10 participants attend stress-induction sessions followed by two biofeedback mindfulness sessions: classic biofeedback and artistic visualization. The result demonstrates that artistic visualization-driven biofeedback significantly improves the effectiveness of biofeedback in helping users feel relaxed in comparison with a classic graphical form of biofeedback. Also, it shows that the artistic effect makes it easy to understand what biofeedback represents. Future work includes exploring how advanced physiological computing methods can improve its efficiency and performance

    ViBreathe: Heart Rate Variability Enhanced Respiration Training for Workaday Stress Management via an Eyes-free Tangible Interface

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    Slow breathing guiding applications increasingly emerge, showing promise for helping knowledge workers to better cope with workaday stress. However, standard breathing guidance is non-interactive, with rigid paces. Despite their effects being proved, they could cause respiratory fatigue, or lack of training motivation, especially for novice users. To explore new design possibilities, we investigate using heart rate variability (HRV) data to mediate breathing guidance, which results in two HRV-enhanced guidance modes: (i) responsive breathing guidance and (ii) adaptive breathing guidance. These guidance modes are implemented on a soft haptic interface named “ViBreathe”. We conducted a user test (N\ua0=\ua024), and a one-week field deployment (N\ua0=\ua04) with knowledge workers, to understand the user experience of our design. The HRV-enhanced modes were generally experienced to reduce tiresome and improve engagement and comfort. And Vibreathe showed great potential for seamlessly weaving slow breathing practice into work routines. We thereby summarize related design insights and opportunities

    INNOVATING CONTROL AND EMOTIONAL EXPRESSIVE MODALITIES OF USER INTERFACES FOR PEOPLE WITH LOCKED-IN SYNDROME

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    Patients with Lock-In-Syndrome (LIS) lost their ability to control any body part beside their eyes. Current solutions mainly use eye-tracking cameras to track patients' gaze as system input. However, despite the fact that interface design greatly impacts user experience, only a few guidelines have been were proposed so far to insure an easy, quick, fluid and non-tiresome computer system for these patients. On the other hand, the emergence of dedicated computer software has been greatly increasing the patients' capabilities, but there is still a great need for improvements as existing systems still present low usability and limited capabilities. Most interfaces designed for LIS patients aim at providing internet browsing or communication abilities. State of the art augmentative and alternative communication systems mainly focus on sentences communication without considering the need for emotional expression inextricable from human communication. This thesis aims at exploring new system control and expressive modalities for people with LIS. Firstly, existing gaze-based web-browsing interfaces were investigated. Page analysis and high mental workload appeared as recurring issues with common systems. To address this issue, a novel user interface was designed and evaluated against a commercial system. The results suggested that it is easier to learn and to use, quicker, more satisfying, less frustrating, less tiring and less prone to error. Mental workload was greatly diminished with this system. Other types of system control for LIS patients were then investigated. It was found that galvanic skin response may be used as system input and that stress related bio-feedback helped lowering mental workload during stressful tasks. Improving communication was one of the main goal of this research and in particular emotional communication. A system including a gaze-controlled emotional voice synthesis and a personal emotional avatar was developed with this purpose. Assessment of the proposed system highlighted the enhanced capability to have dialogs more similar to normal ones, to express and to identify emotions. Enabling emotion communication in parallel to sentences was found to help with the conversation. Automatic emotion detection seemed to be the next step toward improving emotional communication. Several studies established that physiological signals relate to emotions. The ability to use physiological signals sensors with LIS patients and their non-invasiveness made them an ideal candidate for this study. One of the main difficulties of emotion detection is the collection of high intensity affect-related data. Studies in this field are currently mostly limited to laboratory investigations, using laboratory-induced emotions, and are rarely adapted for real-life applications. A virtual reality emotion elicitation technique based on appraisal theories was proposed here in order to study physiological signals of high intensity emotions in a real-life-like environment. While this solution successfully elicited positive and negative emotions, it did not elicit the desired emotions for all subject and was therefore, not appropriate for the goals of this research. Collecting emotions in the wild appeared as the best methodology toward emotion detection for real-life applications. The state of the art in the field was therefore reviewed and assessed using a specifically designed method for evaluating datasets collected for emotion recognition in real-life applications. The proposed evaluation method provides guidelines for future researcher in the field. Based on the research findings, a mobile application was developed for physiological and emotional data collection in the wild. Based on appraisal theory, this application provides guidance to users to provide valuable emotion labelling and help them differentiate moods from emotions. A sample dataset collected using this application was compared to one collected using a paper-based preliminary study. The dataset collected using the mobile application was found to provide a more valuable dataset with data consistent with literature. This mobile application was used to create an open-source affect-related physiological signals database. While the path toward emotion detection usable in real-life application is still long, we hope that the tools provided to the research community will represent a step toward achieving this goal in the future. Automatically detecting emotion could not only be used for LIS patients to communicate but also for total-LIS patients who have lost their ability to move their eyes. Indeed, giving the ability to family and caregiver to visualize and therefore understand the patients' emotional state could greatly improve their quality of life. This research provided tools to LIS patients and the scientific community to improve augmentative and alternative communication, technologies with better interfaces, emotion expression capabilities and real-life emotion detection. Emotion recognition methods for real-life applications could not only enhance health care but also robotics, domotics and many other fields of study. A complete system fully gaze-controlled was made available open-source with all the developed solutions for LIS patients. This is expected to enhance their daily lives by improving their communication and by facilitating the development of novel assistive systems capabilities

    BeMonitored: monitorização psicofisiológica usando dispositivos móveis

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    Mestrado em Engenharia de Computadores e TelemáticaThe daily life in modern societies has a high impact in individuals. Long-term stress, changes, traumas and life experiences are some of environmental factors that lead to the development of anxiety disorders. Anxiety disorders affects many people in their daily lives, since they may lead to social isolation, clinical depression, and can impair a person’s ability to work, study and routine activities. Nevertheless, there are many effective therapies available for such disease, sufferers do not seek for treatment, because they underestimate the problem, the treatments duration, cost or difficult in access. In result, it is of the utmost importance that researchers can recreate, as accurately as possible, real life conditions in psychological studies. However, that is not always possible. Recent improvements in sensors technology make then a straightforward solution to gather physiological data. However, their standalone use is quite limited. Nevertheless, combining those sensors with a Smartphone creates an independent solution that without any more requirements has an enormous potential, due to the advanced computing power and connectivity features available. In this dissertation it is proposed the BeMonitored, a Smartphone based solution to support more ecological valid monitoring of psychological experiments. BeMonitored delivers customizable specific context dependent audio-visual stimuli and using external resources connected via Bluetooth or Smartphone own resources (camera, gps), is able to capture the subject’s behavior, physiology and environment. As a proof of concept, BeMonitored was tested in a spider phobia population, where it was found that spider phobic was separated from control subjects using solely the face motion captured with the Smartphone camera. Also, heart rate differences were found between spider and neutral stimuli. Although current study focused only on spider phobia, the results support the validity and the potential of using BeMonitored in other phobias related, especially in cognitive behavioral therapy (CBT) scenarios, either for assessment of the phobia “stage” or to deliver a stepwise sequence of video stimuli according to accepted psychology guidelines.O dia a dia nas sociedades modernas, tem um grande impacto nos indivíduos. O stress continuado, mudanças, traumas e as experiências de vida, são alguns dos fatores ambientais que potenciam o desenvolvimento de doenças de ansiedade. Este tipo de doenças podem conduzir ao isolamento social, a depressões, à diminuição da capacidade de trabalhar, estudar ou executar tarefas do quotidiano. Apesar de existirem inúmeras terapias eficazes no tratamento deste tipo de doenças, os sofredores, não procuram tratamento, ou por desvalorizarem o problema, ou devido à duração e custo associado ou pelo difícil acesso. Deste modo, é da extrema importância que os investigadores consigam recriar as condições da vida real no estudo de doenças do foro psicológico.Contudo, tal nem sempre é possível. As recentes evoluções ao nível dos sensores biomédicos fazem deles uma solução simples para adquirir sinais biológicos. Contudo, o seu uso isolado é de certa forma limitado. Por outro lado, combinando estes sensores com um Smartphone, criamos uma solução independente, com enorme potencial, devido ao avançado poder computacional e conectividade destes dispositivos. Nesta dissertação propomos o sistema BeMonitored: uma solução baseada em Smartphone para suportar um estudo ecologicamente válido a nível da monitorização de doenças do foro psicológico. BeMonitored é uma solução que permite expor os sujeitos a um estímulo audiovisual configurável, que usando sensores biomédicos ligados por Bluetooth ao Smartphone, juntamente com os seus recursos de hardware (ex: câmera, GPS), é capaz de adquirir o comportamento e a fisiologia dos sujeitos, bem como o contexto envolvente. Como prova de conceito, o BeMonitored foi testado num estudo de fobia a aranhas, onde foi possível obter resultados que nos permitem separar os sujeitos fóbicos dos sujeitos de controlo usando apenas o movimento facial capturado com a camara do smartphone. Encontraram-se também diferenças na frequência cardiaca entre os segmentos de vídeo com aranhas e neutros. Apesar do estudo ser focado nas fobias a aranhas, os resultados obtidos confirmam a validade e o potencial de utilização do BeMonitored em outras fobias, bem como em cenários de terapia cognitivo-comportamental(CBT), quer para a avaliação do nível de fobia quer na exposição gradual de estímulos de video de acordo com as directizes aceites na área da psicologia

    Toward Emotion Recognition From Physiological Signals in the Wild: Approaching the Methodological Issues in Real-Life Data Collection

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    Emotion, mood, and stress recognition (EMSR) has been studied in laboratory settings for decades. In particular, physiological signals are widely used to detect and classify affective states in lab conditions. However, physiological reactions to emotional stimuli have been found to differ in laboratory and natural settings. Thanks to recent technological progress (e.g., in wearables) the creation of EMSR systems for a large number of consumers during their everyday activities is increasingly possible. Therefore, datasets created in the wild are needed to insure the validity and the exploitability of EMSR models for real-life applications. In this paper, we initially present common techniques used in laboratory settings to induce emotions for the purpose of physiological dataset creation. Next, advantages and challenges of data collection in the wild are discussed. To assess the applicability of existing datasets to real-life applications, we propose a set of categories to guide and compare at a glance different methodologies used by researchers to collect such data. For this purpose, we also introduce a visual tool called Graphical Assessment of Real-life Application-Focused Emotional Dataset (GARAFED). In the last part of the paper, we apply the proposed tool to compare existing physiological datasets for EMSR in the wild and to show possible improvements and future directions of research. We wish for this paper and GARAFED to be used as guidelines for researchers and developers who aim at collecting affect-related data for real-life EMSR-based applications

    Wearable in-ear pulse oximetry: theory and applications

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    Wearable health technology, most commonly in the form of the smart watch, is employed by millions of users worldwide. These devices generally exploit photoplethysmography (PPG), the non-invasive use of light to measure blood volume, in order to track physiological metrics such as pulse and respiration. Moreover, PPG is commonly used in hospitals in the form of pulse oximetry, which measures light absorbance by the blood at different wavelengths of light to estimate blood oxygen levels (SpO2). This thesis aims to demonstrate that despite its widespread usage over many decades, this sensor still possesses a wealth of untapped value. Through a combination of advanced signal processing and harnessing the ear as a location for wearable sensing, this thesis introduces several novel high impact applications of in-ear pulse oximetry and photoplethysmography. The aims of this thesis are accomplished through a three pronged approach: rapid detection of hypoxia, tracking of cognitive workload and fatigue, and detection of respiratory disease. By means of the simultaneous recording of in-ear and finger pulse oximetry at rest and during breath hold tests, it was found that in-ear SpO2 responds on average 12.4 seconds faster than the finger SpO2. This is likely due in part to the ear being in close proximity to the brain, making it a priority for oxygenation and thus making wearable in-ear SpO2 a good proxy for core blood oxygen. Next, the low latency of in-ear SpO2 was further exploited in the novel application of classifying cognitive workload. It was found that in-ear pulse oximetry was able to robustly detect tiny decreases in blood oxygen during increased cognitive workload, likely caused by increased brain metabolism. This thesis demonstrates that in-ear SpO2 can be used to accurately distinguish between different levels of an N-back memory task, representing different levels of mental effort. This concept was further validated through its application to gaming and then extended to the detection of driver related fatigue. It was found that features derived from SpO2 and PPG were predictive of absolute steering wheel angle, which acts as a proxy for fatigue. The strength of in-ear PPG for the monitoring of respiration was investigated with respect to the finger, with the conclusion that in-ear PPG exhibits far stronger respiration induced intensity variations and pulse amplitude variations than the finger. All three respiratory modes were harnessed through multivariate empirical mode decomposition (MEMD) to produce spirometry-like respiratory waveforms from PPG. It was discovered that these PPG derived respiratory waveforms can be used to detect obstruction to breathing, both through a novel apparatus for the simulation of breathing disorders and through the classification of chronic obstructive pulmonary disease (COPD) in the real world. This thesis establishes in-ear pulse oximetry as a wearable technology with the potential for immense societal impact, with applications from the classification of cognitive workload and the prediction of driver fatigue, through to the detection of chronic obstructive pulmonary disease. The experiments and analysis in this thesis conclusively demonstrate that widely used pulse oximetry and photoplethysmography possess a wealth of untapped value, in essence teaching the old PPG sensor new tricks.Open Acces

    Investigating the Effects of Physiology-driven Vibro-tactile Biofeedback for Mitigating State Anxiety during Public Speaking

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    For some, public speaking can cause heightened moments of stress while giving a speech or presentation. These moments are quantifiable through one’s physiology and vocal characteristics, measurable through sensor-enabled smart technology. Through these measurements, we can assess the current state of the individual to determine opportune moments to deliver interventions that alleviate symptoms of stressful moments. Recent work in wrist-worn vibrotactile biofeedback suggests that it is a promising intervention towards reducing state-based anxiety for public speaking. However, since the vibrotactile stimulus is delivered constantly, adaptation could risk diminishing relieving effects. Therefore, we administer vibrotactile biofeedback as a just-in-time adaptive intervention during in-the-moment heightened levels of stress. We evaluate two types of vibrotactile feedback delivery mechanisms in a between-subjects design – one that delivers stimulus randomly and one that delivers stimulus during moments of heightened physiological reactivity, as determined by changes in electrodermal activity. The results from these interventions indicate that vibrotactile biofeedback administered during high physiological arousal appears to improve stress-related measures early on, but these effects diminish over time. However, we also observe no significant differences in self-reported state anxiety scores between experiment groups. In the latter half of this thesis, we will explore methods for personalizing machine learning models that detect the onset of heightened moments of stress in real-time. Results indicate that baseline-norming, fine-tuning on participant-specific data, and providing individual-specific trait information are all helpful techniques for improving stress detection performance
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