39 research outputs found
How to Relax in Stressful Situations: A Smart Stress Reduction System
Stress is an inescapable element of the modern age. Instances of untreated stress may lead to a reduction in the individual's health, well-being and socio-economic situation. Stress management application development for wearable smart devices is a growing market. The use of wearable smart devices and biofeedback for individualized real-life stress reduction interventions has received less attention. By using our unobtrusive automatic stress detection system for use with consumer-grade smart bands, we first detected stress levels. When a high stress level is detected, our system suggests the most appropriate relaxation method by analyzing the physical activity-based contextual information. In more restricted contexts, physical activity is lower and mobile relaxation methods might be more appropriate, whereas in free contexts traditional methods might be useful. We further compared traditional and mobile relaxation methods by using our stress level detection system during an eight day EU project training event involving 15 early stage researchers (mean age 28; gender 9 Male, 6 Female). Participants' daily stress levels were monitored and a range of traditional and mobile stress management techniques was applied. On day eight, participants were exposed to a 'stressful' event by being required to give an oral presentation. Insights about the success of both traditional and mobile relaxation methods by using the physiological signals and collected self-reports were provided
Quantifying Quality of Life
Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject
Quantifying Quality of Life
Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject
Detecting Flow Experiences in Cognitive Tasks - A Neurophysiological Approach
Das Flow-Erlebnis beschreibt einen Zustand vollstĂ€ndiger Aufgabenvertiefung und mĂŒhelosen Handelns, der mit Höchstleistungen, persönlichem Wachstum, sowie allgemeinem Wohlbefinden verbunden ist. FĂŒr Unternehmen stellen hĂ€ufigere Flow-Erlebnisse der ArbeitnehmerInnen daher auch eine produktivitĂ€ts- und zufriedenheitsfördernde Basis dar. Vor allem da sich aktuell globale PhĂ€nomene wie die steigende Nachfrage nach Wissensarbeit und das niedrige Arbeitsengagement zuspitzen, können Unternehmen von einer Förderung von Flow profitieren. Die UnterstĂŒtzung von Flow stellt allerdings aufgrund der Vielfalt von Arbeitnehmerfertigkeiten, -aufgaben, und -arbeitsplĂ€tzen eine komplexe Herausforderung dar. WissensarbeiterInnen stehen dynamischen Aufgaben gegenĂŒber, die diverse Kompetenzen und die Kooperation mit anderen erfordern. ArbeitsplĂ€tze werden vielseitiger, indem die Grenzen zwischen ko-prĂ€senten und virtuellen Interaktionen verschwinden. Diese Vielfalt bedeutet, dass eine solide Flow-Förderung nur durch personen-, aufgaben- und situationsunabhĂ€ngige AnsĂ€tze erfolgen kann. Aus diesem Grund werden zunehmend die neurophysiologischen Grundlagen des Flow-Erlebens untersucht. Auf deren Basis könnten adaptive Neuro-Informationssysteme entwickelt werden, die mittels tragbarer Sensorik Flow kontinuierlich erkennen und fördern können. Diese Wissensbasis ist bislang jedoch nur spĂ€rlich und in stark fragmentierter Form vorhanden. FĂŒr das Individuum existieren lediglich konkurrierende VorschlĂ€ge, die noch nicht durch situations- und sensorĂŒbergreifende Studien konsolidiert wurden. FĂŒr Gruppen existiert noch fast keine Forschung zu neurophysiologischen Flow-Korrelaten, insbesondere keine im Kontext digital-mediierter Interaktionen. In dieser Dissertation werden genau diese ForschungslĂŒcken durch die situationsĂŒbergreifende Beobachtung von Flow mit tragbaren EKG und EEG Sensoren adressiert. Dabei werden zentrale Grenzen der experimentellen Flow-Forschung berĂŒcksichtigt, vor allem die Defizite etablierter Paradigmen zum kontrollierten Hervorrufen von Flow. Indem Erlebnisse in zwei kognitiven Aufgaben und mehreren Manipulationen (von Schwierigkeit, NatĂŒrlichkeit, Autonomie und sozialer Interaktion) variiert werden, wird untersucht, wie Flow intensiver hervorgerufen und wie das Erlebnis stabiler ĂŒber Situationen hinweg beobachtet werden kann. Die Studienergebnisse deuten dabei insgesamt auf ein Flow-Muster von moderater physiologischer Aktivierung und mentaler Arbeitslast, von erhöhter, aufgabenorientierter Aufmerksamkeit und von affektiver NeutralitĂ€t hin. Vor allem die EEG Daten zeigen ein diagnostisches Potenzial, schwĂ€chere von stĂ€rkeren Flow-ZustĂ€nden unterscheiden zu können, indem optimale und nicht-optimale Aufgabenschwierigkeiten (fĂŒr Individuen und Gruppen) erkannt werden. Um das Flow-Erleben weiter zu fördern, werden geeignete Wege fĂŒr zukĂŒnftige Forschung abschlieĂend diskutiert
Stress and Health
Acute stressful experiences or high levels of chronic stress are risk factors for mental and physical disorders. Insights into the effects of posttraumatic stress disorder and other stress-related disorders experienced by war veterans, refugees, and immigrants are presented. This volume also presents examinations of the pathological effects of stress that may disrupt the normal relationships between individuals and their families. The health of individuals and their children may be enhanced by interventions to help them manage the effects of stressful life experiences and environments. Innovative and effective interventions are examined and their applications are recommended
Novel Bidirectional Body - Machine Interface to Control Upper Limb Prosthesis
Objective. The journey of a bionic prosthetic user is characterized by the opportunities and limitations involved in adopting a device (the prosthesis) that should enable activities of daily living (ADL). Within this context, experiencing a bionic hand as a functional (and, possibly, embodied) limb constitutes the premise for mitigating the risk of its abandonment through the continuous use of the device. To achieve such a result, different aspects must be considered for making the artificial limb an effective support for carrying out ADLs. Among them, intuitive and robust control is fundamental to improving amputeesâ quality of life using upper limb prostheses. Still, as artificial proprioception is essential to perceive the prosthesis movement without constant visual attention, a good control framework may not be enough to restore practical functionality to the limb. To overcome this, bidirectional communication between the user and the prosthesis has been recently introduced and is a requirement of utmost importance in developing prosthetic hands. Indeed, closing the control loop between the user and a prosthesis by providing artificial sensory feedback is a fundamental step towards the complete restoration of the lost sensory-motor functions. Within my PhD work, I proposed the development of a more controllable and sensitive human-like hand prosthesis, i.e., the Hannes prosthetic hand, to improve its usability and effectiveness.
Approach. To achieve the objectives of this thesis work, I developed a modular and scalable software and firmware architecture to control the Hannes prosthetic multi-Degree of Freedom (DoF) system and to fit all usersâ needs (hand aperture, wrist rotation, and wrist flexion in different combinations). On top of this, I developed several Pattern Recognition (PR) algorithms to translate electromyographic (EMG) activity into complex movements. However, stability and repeatability were still unmet requirements in multi-DoF upper limb systems; hence, I started by investigating different strategies to produce a more robust control. To do this, EMG signals were collected from trans-radial amputees using an array of up to six sensors placed over the skin. Secondly, I developed a vibrotactile system to implement haptic feedback to restore proprioception and create a bidirectional connection between the user and the prosthesis. Similarly, I implemented an object stiffness detection to restore tactile sensation able to connect the user with the external word. This closed-loop control between EMG and vibration feedback is essential to implementing a Bidirectional Body - Machine Interface to impact amputeesâ daily life strongly. For each of these three activities: (i) implementation of robust pattern recognition control algorithms, (ii) restoration of proprioception, and (iii) restoration of the feeling of the grasped object's stiffness, I performed a study where data from healthy subjects and amputees was collected, in order to demonstrate the efficacy and usability of my implementations. In each study, I evaluated both the algorithms and the subjectsâ ability to use the prosthesis by means of the F1Score parameter (offline) and the Target Achievement Control test-TAC (online). With this test, I analyzed the error rate, path efficiency, and time efficiency in completing different tasks.
Main results. Among the several tested methods for Pattern Recognition, the Non-Linear Logistic Regression (NLR) resulted to be the best algorithm in terms of F1Score (99%, robustness), whereas the minimum number of electrodes needed for its functioning was determined to be 4 in the conducted offline analyses. Further, I demonstrated that its low computational burden allowed its implementation and integration on a microcontroller running at a sampling frequency of 300Hz (efficiency). Finally, the online implementation allowed the subject to simultaneously control the Hannes prosthesis DoFs, in a bioinspired and human-like way. In addition, I performed further tests with the same NLR-based control by endowing it with closed-loop proprioceptive feedback. In this scenario, the results achieved during the TAC test obtained an error rate of 15% and a path efficiency of 60% in experiments where no sources of information were available (no visual and no audio feedback). Such results demonstrated an improvement in the controllability of the system with an impact on user experience.
Significance. The obtained results confirmed the hypothesis of improving robustness and efficiency of a prosthetic control thanks to of the implemented closed-loop approach. The bidirectional communication between the user and the prosthesis is capable to restore the loss of sensory functionality, with promising implications on direct translation in the clinical practice
Internet and Biometric Web Based Business Management Decision Support
Internet and Biometric Web Based Business Management Decision Support
MICROBE
MOOC material prepared under
IO1/A5 Development of the MICROBE personalized MOOCs content and teaching materials
Prepared by:
A. Kaklauskas, A. Banaitis, I. Ubarte
Vilnius Gediminas Technical University, Lithuania
Project No: 2020-1-LT01-KA203-07810
AmĂ©liorer les interactions homme-machine et la prĂ©sence sociale avec lâinformatique physiologique
This thesis explores how physiological computing can contribute to human-computer interaction (HCI) and foster new communication channels among the general public. We investigated how physiological sensors, such as electroencephalography (EEG), could be employed to assess the mental state of the users and how they relate to other evaluation methods. We created the first brain-computer interface that could sense visual comfort during the viewing of stereoscopic images and shaped a framework that could help to assess the over all user experience by monitoring workload, attention and error recognition.To lower the barrier between end users and physiological sensors,we participated in the software integration of a low-cost and open hardware EEG device; used off-the shelf webcams to measure heart rate remotely, crafted we arables that can quickly equip users so that electrocardiography, electrodermal activity or EEG may be measured during public exhibitions. We envisioned new usages for our sensors, that would increase social presence. In a study about human-agent interaction, participants tended to prefer virtual avatars that were mirroring their own internal state. A follow-up study focused on interactions between users to describe how physiological monitoringcould alter our relationships. Advances in HCI enabled us to seam lesslyintegrate biofeedback to the physical world. We developped Teegi, apuppet that lets novices discover by themselves about their brain activity. Finally, with Tobe, a toolkit that encompasses more sensors and give more freedom about their visualizations, we explored how such proxy shifts our representations, about our selves as well as about the others.Cette thĂšse explore comment lâinformatique physiologique peut contribuer aux interactions homme-machine (IHM) et encourager lâapparition de nouveaux canaux de communication parmi le grand public. Nous avons examinĂ© comment des capteurs physiologiques,tels que lâĂ©lectroencĂ©phalographie (EEG), pourraient ĂȘtre utilisĂ©s afin dâestimer lâĂ©tat mental des utilisateurs et comment ils se positionnent par rapport Ă dâautres mĂ©thodes dâĂ©valuation. Nous avons crĂ©Ă© la premiĂšre interface cerveau-ordinateur capable de discriminer le confort visuel pendant le visionnage dâimages stĂ©rĂ©oscopiques et nous avons esquissĂ© un systĂšme qui peux aider Ă estimer lâexpĂ©rience utilisateur dans son ensemble, en mesurant charge mentale, attention et reconnaissance dâerreur. Pour abaisser la barriĂšre entre utilisateurs finaux et capteurs physiologiques, nous avons participĂ© Ă lâintĂ©gration logicielle dâun appareil EEG bon marchĂ© et libre, nous avons utilisĂ© des webcams du commerce pour mesurer le rythme cardiaque Ă distance, nous avons confectionnĂ© des wearables dont les utilisateurs peuvent rapidement sâĂ©quiper afin quâĂ©lectrocardiographie, activitĂ© Ă©lectrodermale et EEG puissent ĂȘtre mesurĂ©es lors de manifestations publiques. Nous avons imaginĂ© de nouveaux usages pour nos capteurs, qui augmenteraient la prĂ©sence sociale. Dans une Ă©tude autour de lâinteraction humain agent,les participants avaient tendance Ă prĂ©fĂ©rer les avatars virtuels rĂ©pliquant leurs propres Ă©tats internes. Une Ă©tude ultĂ©rieure sâest concentrĂ©e sur lâinteraction entre utilisateurs, profitant dâun jeu de plateau pour dĂ©crire comment lâexamen de la physiologie pourrait changer nos rapports. Des avancĂ©es en IHM ont permis dâintĂ©grer de maniĂšre transparente du biofeedback au monde physique. Nous avons dĂ©veloppĂ© Teegi, une poupĂ©e qui permet aux novices dâen dĂ©couvrir plus sur leur activitĂ© cĂ©rĂ©brale, par eux-mĂȘmes. Enfin avec Tobe, un toolkit qui comprend plus de capteurs et donne plus de libertĂ© quant Ă leurs visualisations, nous avons explorĂ© comment un tel proxy dĂ©calenos reprĂ©sentations, tant de nous-mĂȘmes que des autres
Enriching mobile interaction with garment-based wearable computing devices
Wearable computing is on the brink of moving from research to mainstream. The first simple products, such as fitness wristbands and smart watches, hit the mass market and achieved considerable market penetration. However, the number and versatility of research prototypes in the field of wearable computing is far beyond the available devices on the market. Particularly, smart garments as a specific type of wearable computer, have high potential to change the way we interact with computing systems. Due to the proximity to the user`s body, smart garments allow to unobtrusively sense implicit and explicit user input. Smart garments are capable of sensing physiological information, detecting touch input, and recognizing the movement of the user.
In this thesis, we explore how smart garments can enrich mobile interaction. Employing a user-centered design process, we demonstrate how different input and output modalities can enrich interaction capabilities of mobile devices such as mobile phones or smart watches. To understand the context of use, we chart the design space for mobile interaction through wearable devices. We focus on the device placement on the body as well as interaction modality.
We use a probe-based research approach to systematically investigate the possible inputs and outputs for garment based wearable computing devices. We develop six different research probes showing how mobile interaction benefits from wearable computing devices and what requirements these devices pose for mobile operating systems. On the input side, we look at explicit input using touch and mid-air gestures as well as implicit input using physiological signals. Although touch input is well known from mobile devices, the limited screen real estate as well as the occlusion of the display by the input finger are challenges that can be overcome with touch-enabled garments. Additionally, mid-air gestures provide a more sophisticated and abstract form of input. We present a gesture elicitation study to address the special requirements of mobile interaction and present the resulting gesture set. As garments are worn, they allow different physiological signals to be sensed. We explore how we can leverage these physiological signals for implicit input. We conduct a study assessing physiological information by focusing on the workload of drivers in an automotive setting. We show that we can infer the driverÂŽs workload using these physiological signals.
Beside the input capabilities of garments, we explore how garments can be used as output. We present research probes covering the most important output modalities, namely visual, auditory, and haptic. We explore how low resolution displays can serve as a context display and how and where content should be placed on such a display. For auditory output, we investigate a novel authentication mechanism utilizing the closeness of wearable devices to the body. We show that by probing audio cues through the head of the user and re-recording them, user authentication is feasible. Last, we investigate EMS as a haptic feedback method. We show that by actuating the user`s body, an embodied form of haptic feedback can be achieved.
From the aforementioned research probes, we distilled a set of design recommendations. These recommendations are grouped into interaction-based and technology-based recommendations and serve as a basis for designing novel ways of mobile interaction. We implement a system based on these recommendations. The system supports developers in integrating wearable sensors and actuators by providing an easy to use API for accessing these devices.
In conclusion, this thesis broadens the understanding of how garment-based wearable computing devices can enrich mobile interaction. It outlines challenges and opportunities on an interaction and technological level. The unique characteristics of smart garments make them a promising technology for making the next step in mobile interaction
Health State Estimation
Life's most valuable asset is health. Continuously understanding the state of
our health and modeling how it evolves is essential if we wish to improve it.
Given the opportunity that people live with more data about their life today
than any other time in history, the challenge rests in interweaving this data
with the growing body of knowledge to compute and model the health state of an
individual continually. This dissertation presents an approach to build a
personal model and dynamically estimate the health state of an individual by
fusing multi-modal data and domain knowledge. The system is stitched together
from four essential abstraction elements: 1. the events in our life, 2. the
layers of our biological systems (from molecular to an organism), 3. the
functional utilities that arise from biological underpinnings, and 4. how we
interact with these utilities in the reality of daily life. Connecting these
four elements via graph network blocks forms the backbone by which we
instantiate a digital twin of an individual. Edges and nodes in this graph
structure are then regularly updated with learning techniques as data is
continuously digested. Experiments demonstrate the use of dense and
heterogeneous real-world data from a variety of personal and environmental
sensors to monitor individual cardiovascular health state. State estimation and
individual modeling is the fundamental basis to depart from disease-oriented
approaches to a total health continuum paradigm. Precision in predicting health
requires understanding state trajectory. By encasing this estimation within a
navigational approach, a systematic guidance framework can plan actions to
transition a current state towards a desired one. This work concludes by
presenting this framework of combining the health state and personal graph
model to perpetually plan and assist us in living life towards our goals.Comment: Ph.D. Dissertation @ University of California, Irvin