524 research outputs found

    The future of smartwatches : a case on the current status and expected category evolution on the Portuguese market

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    The introduction of new technologies and development of tools that facilitate everyday consumers’ life is part of the reality we are living. And whereas some innovations might be of slighter importance and distinctiveness, others might imply a significant change in the consumer behaviour, totally redefining the marketplace expectations. On the latter, and considering its high level of uncertainty, consumer acceptance plays a key role that companies must be aware of and consider in their strategy, in order to mitigate any barriers it might bring. The aim of this dissertation is to provide insights on how is the smartwatches category evolving in the Portuguese market and how is it possible to leverage its growth, by assessing in detail the current status of the market globally and locally, as well as retrieving insightful quantitative data on Portuguese consumer preferences towards this category. The methodology used concerns qualitative data retrieved from group interviews to 3 smartwatch owners and 4 non-owners, as well as quantitative data obtained through a survey conveyed to 258 valid respondents. All supported with an extensive literature review on both diffusion of innovation theory, as well as smartwatch definition, update on current status and foreseen evolution. The main findings suggest that, currently, smartwatches are at the chasm stage of the product lifecycle with a need of developing strategies to cross from the early adopter to the mainstream market. These same strategies are proposed in this dissertation, taking as base both literature insights as well as consumer quantitative contribution.A introdução de novas tecnologias e desenvolvimento de ferramentas facilitadoras do dia-a-dia do consumidor fazem parte da realidade atual. E enquanto algumas inovações podem ser de menor importância ou distinção, outras implicam uma mudança significativa do comportamento do consumidor, redefinindo totalmente as expectativas do mercado. No último caso, e considerando o seu alto nível de incerteza, a aceitação do consumidor desempenha um papel-chave para as empresas, devendo considerá-la na sua estratégia e mitigar potenciais barreiras que possa trazer. O objetivo desta dissertação é assim, proporcionar conhecimento na evolução da categoria de smartwatches em Portugal assim como entender de que forma alavancar o seu crescimento, ao analisar em detalhe o estado atual do mercado global e local, recolhendo dados quantitativos relevantes das preferências do consumidor Português relativas à categoria. A metodologia utilizada inclui dados qualitativos recolhidos através de entrevistas de grupo a 3 detentores de smartwatch e 4 não-detentores, assim como dados quantitativos recolhidos num inquérito distribuído a 258 inquiridos válidos. Suportado por uma extensiva revisão bibliográfica sobre teoria da difusão de inovação, assim como na definição e descrição do estado atual do mercado de smartwatches e sua expectável evolução. As principais conclusões sugerem que atualmente os smartwatches se encontram na fase de chasm do ciclo de vida do produto, com necessidade de desenvolver estratégias que os passem do mercado de pioneiros para o comercial. Estas mesmas estratégias são propostas nesta dissertação, tomando como base os conhecimentos retirados da revisão literária assim como da contribuição de dados quantitativos de consumidor

    Health Wearable Tools and Health Promotion

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    The application of wearable technology for health purposes is a multidisciplinary research topic. To summarize key contributions and simultaneously identify outstanding gaps in research, the input-mechanism-output (I-M-O) framework was applied to synthesize findings from 275 relevant papers in the period 2010–2021. Eighteen distinct cross-disciplinary themes were identified and organized under the I-M-O framework. Studies that covered input factors have largely been technocentric, exploring the design of various health wearables, with less emphasis on usability. While studies on user acceptance and engagement are increasing, there remains room for growth in user- centric aspects such as engagement. While measurement of physiological health indictors has grown more sophisticated due to sensitivity of sensors and the advancements in predictive algorithms, a rapidly growing area of research is that of measuring and tracking mental states and emotional health.Relatively few studies explore theoretically backed explanations of the role of health wearables, with technocentric theories predicting adoption favored. These mainly focused on mechanisms of adoption, while postadoption use and health behavior change were less explored. As a consequence, compared to adoption mechanisms, there is an opportunity to increase our understanding of the continued use of wearables and their effects on sustained health behavior change. While a range of incentives such as social, feedback, financial, and gamification are being tested, it is worth noting that negative attitudes, such as privacy concerns, are being paid much more attention as well. Output factors were studied in both individual and organizational settings, with the former receiving considerably more attention than the latter. The progress of research on health wearables was discussed from an interdisciplinary angle, and the role of social scientists was highlighted for the advancement of research on wearable health

    Ranteesta mitattavat tunteet tietokoneen ja ihmisen vuorovaikutuksessa

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    The role of emotion in human-computer interaction (HCI) has seen an increase in interest during the last decades. Technological advancements have made studying them much more viable for example because of the availability of affordable and accurate wrist-based sensors. However, this subfield of HCI still lacks theory and it has many unsolved engineering problems, especially considering naturalistic and automated emotion recognition. This thesis provides an overview of wrist- based emotion recognition in human-computer interaction by tying in the views and theoretical background of emotion from philosophy, psychology, neuroscience and economics. The thesis also includes an experimental set-up in naturalistic settings. The experiment uses an Empatica E4 device that can be worn on the wrist and which can be used to measure electrodermal activity (EDA) and heartrate variability (HRV). Both EDA and HRV are known biomarkers for various emotional reactions, such as emotional arousal or mental stress. The study explores the possibilities of EDA and HRV to measure emotional arousal and valence. Furthermore, the correlations between psychological surveys and emotional biosignal markers are explored. We used the Affect Intensity Measure (AIM) -survey, which measures the intensity of experienced and shown emotion, and Rational- Experiential Inventory (REI) -survey, which measures an individual preferred style of information processing. Five custom experiments and a data analysis method with custom analyzer code were designed for this thesis. Our findings suggest that EDA is a good marker for arousal, but that HRV is a problematic measure. Furthermore, we found evidence that there would be correlations between psychological traits and biosignals. However, there were limitations within our experiments. In conclusions, we provide suggestions for futher research and a new theoretical framework that could be used to understand emotions better in HCI.Kiinnostus tunteiden merkityksestä ihmisen ja tietokoneen vuorovaikutuksessa on kasvanut. Teknologian kehityksen myötä tunteisiin liittyviä biosignaaleja voidaan mitata hyvinkin huomaamattomasti esimerkiksi rannetietokoneilla. Alan teoria on kuitenkin vähäistä ja erityisesti naturalistiseen ja automatisoituun tunteiden tunnistamiseen liittyy monia ratkaisemattomia teknologisia ongelmia. Tämän diplomityön tarkoituksena on tarjota lukijalleen kattava teoreettinen näkemys monilta tieteen aloilta, jotka tutkivat tunteita. Työ yhdistää tunteisiin liittyvää teoriaa filosofiasta, psykologiasta, neurotieteestä sekä ihmistietokonevuorovaikutuksen tutkimuksesta rakentaakseen yhtenäisen teoreettisen viitekehyksen ongelman ymmärtämiseksi. Työhön kuuluu myös kokeellinen osuus, jossa mitataan tunteita oikeassa ympäristössä. Kokeessa käytetään Empatica E4-rannetietokonetta, jolla voidaan mitata ihon sähkönjohtavuutta (EDA) ja sydämen sykevälivaihtelua (HRV). Sekä EDA että HRV ovat molemmat tunnettuja biosignaaleja erilaisissa tunnetiloissa. Kokeen tarkoitus on tutkia EDA:n ja HRV:n kykyä mitata tunteellista virittäytyneisyyttä ja tunnearvoa. Tämän lisäksi koe tutkii erilaisten psykologisten kyselylomakkeiden korrelaatioita mitattujen biosignaalejen välillä. Kokeessa käytetään Affect Intensity Measure (AIM) -kyselykaavaketta, joka mittaa koettujen ja näytettyjen tunteiden vahvuutta, sekä Rational Experiential Inventory (REI) -kyselykaavaketta, joka mittaa yksilön suosimaa sisäisen tiedonkäsittelyn menetelmää. Koetta varten kehitettiin viisi koeasetelmaa ja metodi, jolla voitiin analysoida mitattua dataa. Tulokset vahvistavat käsityksen, että EDA on hyvä virittäytyneisyyden mittari, mutta HRV:n käytössä löydettiin vain ongelmia. Tuloksissa on myös todisteita psykologisten luonteenpiirteiden ja biosignaalien korrelaatiolle. Lopussa annamme suosituksia seuraaville tutkimuksille ja esittelemme kehittämämme uuden teoreettisen viitekehyksen, jolla tunteita voisi ymmärtää paremmin ihmisen ja tietokoneen vuorovaikutuksessa

    The Spread Fashion: an Explorative Research of Italian Fashion Blog

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    In the collaborative context of the web 2.0 the blogging phenomenon has become one of the most common ways to communicate and share information through its user-generated content. In the blogosphere fashion blogs represent probably one of the liveliest segments. Focusing on fashion brands, fashion products, street style, and personal style, fashion blogs can be written by both fashion professionals and normal people with an interest in the fashion system. This phenomenon has become even more relevant since the fashion brands have recognized the role of fashion bloggers in influencing the final consumers as well as the role of peer – to – peer recommendations in shaping desires and attires of fashion blog users. The paper presents the preliminary results of a netnographic analysis conducted on some of the most popular Italian ‘non-professional’ fashion blogs in order to map the different approach to this grassrooted blog phenomenon, and to categorize and interpret the blog postings and the audience comments concerning fashion product-related information

    Continuous assessment of epileptic seizures with wrist-worn biosensors

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    Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 145-159).Epilepsy is a neurological disorder characterized predominantly by an enduring predisposition to generate epileptic seizures. The apprehension about injury, or even death, resulting from a seizure often overshadows the lives of those unable to achieve complete seizure control. Moreover, the risk of sudden death in people with epilepsy is 24 times higher compared to the general population and the pathophysiology of sudden unexpected death in epilepsy (SUDEP) remains unclear. This thesis describes the development of a wearable electrodermal activity (EDA) and accelerometry (ACM) biosensor, and demonstrates its clinical utility in the assessment of epileptic seizures. The first section presents the development of a wrist-worn sensor that can provide comfortable and continuous measurements of EDA, a sensitive index of sympathetic activity, and ACM over extensive periods of time. The wearable biosensor achieved high correlations with a Food and Drug Administration (FDA) approved system for the measurement of EDA during various classic arousal experiments. This device offers the unprecedented ability to perform comfortable, long-term, and in situ assessment of EDA and ACM. The second section describes the autonomic alterations that accompany epileptic seizures uncovered using the wearable EDA biosensor and time-frequency mapping of heart rate variability. We observed that the post-ictal period was characterized by a surge in sympathetic sudomotor and cardiac activity coinciding with vagal withdrawal and impaired reactivation. The impact of autonomic dysregulation was more pronounced after generalized tonic-clonic seizures compared to complex partial seizures. Importantly, we found that the intensity of both sympathetic activation and parasympathetic suppression increased approximately linearly with duration of post-ictal EEG suppression, a possible marker for the risk of SUDEP. These results highlight a critical window of post-ictal autonomic dysregulation that may be relevant in the pathogenesis of SUDEP and hint at the possibility for assessment of SUDEP risk by autonomic biomarkers. Lastly, this thesis presents a novel algorithm for generalized tonic-clonic seizure detection with the use of EDA and ACM. The algorithm was tested on 4213 hours (176 days) of recordings from 80 patients containing a wide range of ordinary daily activities and detected 15/16 (94%) tonic-clonic seizures with a low rate of false alarms (<; 1 per 24 h). It is anticipated that the proposed wearable biosensor and seizure detection algorithm will provide an ambulatory seizure alarm and improve the quality of life of patients with uncontrolled tonic-clonic seizures.by Ming-Zher Poh.Ph.D

    Modeling the dynamics of nonverbal behavior on interpersonal trust for human-robot interactions

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    Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 105-108).We describe the design, implementation, and validation of a computational model for recognizing interpersonal trust in social interactions. We begin by leverage pre-existing datasets to understand the relationship between synchronous movement, mimicry, and gestural cues with trust. We found that although synchronous movement was not predictive of trust, synchronous movement is positively correlated with mimicry. That is, people who mimicked each other more frequently also move more synchronously in time together. And revealing the versatile nature of unconscious mimicry, we found mimicry to be predictive of liking between participants instead of trust. We reconfirmed that the following four negative gestural cues, leaning-backward, face-touching, hand-touching, and crossing-arms, when taken together are predictive of lower levels of trust, while the following three positive gestural cues, leaning-forward, having arms-in-lap, and open-arms, were predictive of higher levels of trust. We train and validate a probabilistic graphical model using natural social interaction data from 74 participants. And by observing how these seven important gestures unfold throughout the social interaction, our Trust Hidden Markov Model is able to predict with 94% accuracy whether an individual is willing to behave cooperatively or uncooperatively with their novel partner. And by simulating the resulting model, we found that not only does the frequency in the emission of the predictive gestures matter as well, but also the sequence in which we emit negative to positive cues matter. We attempt to automate this recognition process by detecting those trust-related behaviors through 3D motion capture technology and gesture recognition algorithms. And finally, we test how accurately our entire system, with low-level gesture recognition for high-level trust recognition, can predict whether an individual finds another to be trustworthy or untrustworthy.by Jin Joo Lee.S.M

    Usable Security for Wireless Body-Area Networks

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    We expect wireless body-area networks of pervasive wearable devices will enable in situ health monitoring, personal assistance, entertainment personalization, and home automation. As these devices become ubiquitous, we also expect them to interoperate. That is, instead of closed, end-to-end body-worn sensing systems, we envision standardized sensors that wirelessly communicate their data to a device many people already carry today, the smart phone. However, this ubiquity of wireless sensors combined with the characteristics they sense present many security and privacy problems. In this thesis we describe solutions to two of these problems. First, we evaluate the use of bioimpedance for recognizing who is wearing these wireless sensors and show that bioimpedance is a feasible biometric. Second, we investigate the use of accelerometers for verifying whether two of these wireless sensors are on the same person and show that our method is successful as distinguishing between sensors on the same body and on different bodies. We stress that any solution to these problems must be usable, meaning the user should not have to do anything but attach the sensor to their body and have them just work. These methods solve interesting problems in their own right, but it is the combination of these methods that shows their true power. Combined together they allow a network of wireless sensors to cooperate and determine whom they are sensing even though only one of the wireless sensors might be able to determine this fact. If all the wireless sensors know they are on the same body as each other and one of them knows which person it is on, then they can each exploit the transitive relationship to know that they must all be on that person’s body. We show how these methods can work together in a prototype system. This ability to operate unobtrusively, collecting in situ data and labeling it properly without interrupting the wearer’s activities of daily life, will be vital to the success of these wireless sensors

    Psychophysiology in the digital age

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    The research I performed for my thesis revolved around the question how affect-physiology dynamics can be best measured in daily life. In my thesis I focused on three aspects of this question: 1) Do wearable wristband devices have sufficient validity to capture ANS activity? 2) To what extent is the laboratory design suitable to measure affect-ANS dynamics? 3) Are the affect-ANS dynamics subject to individual differences, both in the laboratory and in daily life? In chapter 2, I validated a shortened version of the Sing-a-Song Stress (SSST) test, the SSSTshort. The purpose of this test is to create social-evaluative stress in participants through a simple and brief design that does not require the involvement of multiple confederates. The results indicated that the SSSTshort was effective in inducing ANS and affective reactivity. This makes the SSSTshort a cost-effective alternative to the well-known Trier-Social-Stress task (TSST), which can be easily incorporated into large-scale studies to expand the range of stress types that can be studied in laboratory designs. In chapter 3, I validated a new wrist worn technology for measuring electrodermal activity (EDA). As expected, the overall EDA levels measured on the wrist were lower than those measured on the palm, likely due to the lower density of sweat glands on the wrist. The analysis demonstrated that the frequency measure of non-specific skin conductance response (ns.SCR) was superior to the commonly used measure of skin conductance level (SCL) for both the palm and wrist. The wrist-based ns.SCR measure was sensitive to the experimental manipulations and showed similar correspondence to the pre-ejection period (PEP) as palm-based ns.SCR. Moreover, wrist-based ns.SCR demonstrated similar predictive validity for affective state as PEP. However, the predictive validity of both wrist-based ns.SCR and PEP was lower compared to palm-based ns.SCR. These findings suggest that wrist-based ns.SCR EDA parameter has a promising future for use in psychophysiological research. In Chapter 4 of my thesis, I conducted the first study to directly compare the relationship between affect and ANS activity in a laboratory setting to that in daily life. To elicit stress in the laboratory, four different stress paradigms were employed, while stressful events in daily life were left to chance. In both settings, a valence and arousal scale was constructed from a nine-item affect questionnaire, and ANS activity was collected using the same devices. Data was collected from a single population, and the affect-ANS dynamics were analyzed using the same methodology for both laboratory and daily life settings. The results showed a remarkable similarity between the laboratory and daily life affect-ANS relationships. In Chapter 5 of my thesis, I investigated the influence of individual differences in physical activity and aerobic fitness on ANS and affective stress reactivity. Previous research has yielded inconsistent results due to heterogeneity issues in the population studied, stressor type, and the way fitness was measured. My study made a unique contribution to this field by measuring physical activity in three ways: 1) as objective aerobic fitness, 2) leisure time exercise behavior, and 3) total moderate-to-vigorous exercise (including both exercise and all other regular physical activity behaviors). In addition, we measured the physiological and affective stress response in both a laboratory and daily life setting. The total amount of physical activity showed more relationships with stress reactivity compared to exercise behavior alone, suggesting that future research should include a total physical activity variable. Our results did not support the cross-stressor adaptation hypotheses, suggesting that if exercise has a stress-reducing effect, it is unlikely to be mediated by altered ANS regulation due to repeated exposure to physical stress

    91st Annual Meeting of the Virginia Academy of Science: Proceedings

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    Proceedings of the 91st Annual Meeting of the Virginia Academy of Science, held at Virginia Polytechnic Institute and State University, May 22-24, 2013

    Psychophysiological analysis of a pedagogical agent and robotic peer for individuals with autism spectrum disorders.

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    Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by ongoing problems in social interaction and communication, and engagement in repetitive behaviors. According to Centers for Disease Control and Prevention, an estimated 1 in 68 children in the United States has ASD. Mounting evidence shows that many of these individuals display an interest in social interaction with computers and robots and, in general, feel comfortable spending time in such environments. It is known that the subtlety and unpredictability of people’s social behavior are intimidating and confusing for many individuals with ASD. Computerized learning environments and robots, however, prepare a predictable, dependable, and less complicated environment, where the interaction complexity can be adjusted so as to account for these individuals’ needs. The first phase of this dissertation presents an artificial-intelligence-based tutoring system which uses an interactive computer character as a pedagogical agent (PA) that simulates a human tutor teaching sight word reading to individuals with ASD. This phase examines the efficacy of an instructional package comprised of an autonomous pedagogical agent, automatic speech recognition, and an evidence-based instructional procedure referred to as constant time delay (CTD). A concurrent multiple-baseline across-participants design is used to evaluate the efficacy of intervention. Additionally, post-treatment probes are conducted to assess maintenance and generalization. The results suggest that all three participants acquired and maintained new sight words and demonstrated generalized responding. The second phase of this dissertation describes the augmentation of the tutoring system developed in the first phase with an autonomous humanoid robot which serves the instructional role of a peer for the student. In this tutoring paradigm, the robot adopts a peer metaphor, where its function is to act as a peer. With the introduction of the robotic peer (RP), the traditional dyadic interaction in tutoring systems is augmented to a novel triadic interaction in order to enhance the social richness of the tutoring system, and to facilitate learning through peer observation. This phase evaluates the feasibility and effects of using PA-delivered sight word instruction, based on a CTD procedure, within a small-group arrangement including a student with ASD and the robotic peer. A multiple-probe design across word sets, replicated across three participants, is used to evaluate the efficacy of intervention. The findings illustrate that all three participants acquired, maintained, and generalized all the words targeted for instruction. Furthermore, they learned a high percentage (94.44% on average) of the non-target words exclusively instructed to the RP. The data show that not only did the participants learn nontargeted words by observing the instruction to the RP but they also acquired their target words more efficiently and with less errors by the addition of an observational component to the direct instruction. The third and fourth phases of this dissertation focus on physiology-based modeling of the participants’ affective experiences during naturalistic interaction with the developed tutoring system. While computers and robots have begun to co-exist with humans and cooperatively share various tasks; they are still deficient in interpreting and responding to humans as emotional beings. Wearable biosensors that can be used for computerized emotion recognition offer great potential for addressing this issue. The third phase presents a Bluetooth-enabled eyewear – EmotiGO – for unobtrusive acquisition of a set of physiological signals, i.e., skin conductivity, photoplethysmography, and skin temperature, which can be used as autonomic readouts of emotions. EmotiGO is unobtrusive and sufficiently lightweight to be worn comfortably without interfering with the users’ usual activities. This phase presents the architecture of the device and results from testing that verify its effectiveness against an FDA-approved system for physiological measurement. The fourth and final phase attempts to model the students’ engagement levels using their physiological signals collected with EmotiGO during naturalistic interaction with the tutoring system developed in the second phase. Several physiological indices are extracted from each of the signals. The students’ engagement levels during the interaction with the tutoring system are rated by two trained coders using the video recordings of the instructional sessions. Supervised pattern recognition algorithms are subsequently used to map the physiological indices to the engagement scores. The results indicate that the trained models are successful at classifying participants’ engagement levels with the mean classification accuracy of 86.50%. These models are an important step toward an intelligent tutoring system that can dynamically adapt its pedagogical strategies to the affective needs of learners with ASD
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