11 research outputs found
Resonating Experiences of Self and Others enabled by a Tangible Somaesthetic Design
Digitalization is penetrating every aspect of everyday life including a
human's heart beating, which can easily be sensed by wearable sensors and
displayed for others to see, feel, and potentially "bodily resonate" with.
Previous work in studying human interactions and interaction designs with
physiological data, such as a heart's pulse rate, have argued that feeding it
back to the users may, for example support users' mindfulness and
self-awareness during various everyday activities and ultimately support their
wellbeing. Inspired by Somaesthetics as a discipline, which focuses on an
appreciation of the living body's role in all our experiences, we designed and
explored mobile tangible heart beat displays, which enable rich forms of bodily
experiencing oneself and others in social proximity. In this paper, we first
report on the design process of tangible heart displays and then present
results of a field study with 30 pairs of participants. Participants were asked
to use the tangible heart displays during watching movies together and report
their experience in three different heart display conditions (i.e., displaying
their own heart beat, their partner's heart beat, and watching a movie without
a heart display). We found, for example that participants reported significant
effects in experiencing sensory immersion when they felt their own heart beats
compared to the condition without any heart beat display, and that feeling
their partner's heart beats resulted in significant effects on social
experience. We refer to resonance theory to discuss the results, highlighting
the potential of how ubiquitous technology could utilize physiological data to
provide resonance in a modern society facing social acceleration.Comment: 18 page
Exploring the dynamics of the biocybernetic loop in physiological computing
Physiological computing is a highly multidisciplinary emerging field in which the spread of results across several application areas and disciplines creates a challenge of combining the lessons learned from various studies. The thesis comprises diverse publications that together create a privileged position for contributing to a common understanding of the roles and uses of physiological computing systems, generalizability of results across application areas, the theoretical grounding of the field (as with the various ways the psychophysiological states of the user can be modeled), and the emerging data analysis approaches from the domain of machine learning.
The core of physiological computing systems has been built around the concept of biocybernetic loop, aimed at providing real-time adaptation to the cognitions, motivations, and emotions of the user. However, the traditional concept of the biocybernetic loop has been both self-regulatory and immediate; that is, the system adapts to the user immediately. The thesis presents an argument that this is too narrow a view of physiological computing, and it explores scenarios wherein the physiological signals are used not only to adapt to the user but to aid system developers in designing better systems, as well as to aid other users of the system.
The thesis includes eight case studies designed to answer three research questions: 1) what are the various dynamics the biocybernetic loop can display, 2) how do the changes in loop dynamics affect the way the user is represented and modeled, and 3) how do the choices of loop dynamics and user representations affect the selection of machine learning methods and approaches? To answer these questions, an analytical model for physiological computing is presented that divides each of the physiological computing systems into five separate layers.
The thesis presents three main findings corresponding to the three research questions: Firstly, the case studies show that physiological computing extends beyond the simple real-time self-regulatory loop. Secondly, the selected user representations seem to correlate with the type of loop dynamics. Finally, the case studies show that the machine learning approaches are implemented at the level of feature generation and are used when the loop diverges from the traditional real-time and self-regulatory dynamics into systems where the adaptation happens in the future.Perinteinen ihmisen ja tietokoneen vuorovaikutus on hyvin epäsymmetristä: tietokone voi esittää ihmiselle monimutkaista audiovisuaalista informaatiota kun taas ihmisen kommunikaatio koneen suuntaan on rajattu näppäimistöön ja hiireen. Samoin, vaikka ihmisellä on mahdollisuus saada informaatiota tietokoneen sisäisestä tilasta, kuten muistin ja prosessorin käyttöasteesta, ei tietokoneella ole vastaavaa mahdollisuutta tutkia ihmisen sisäisiä tiloja kuten tunteita. Mittaamalla reaaliajassa ihmisen fysiologisia signaaleja nämä molemmat ongelmat voidaan ratkaista: näppäimistön ja hiiren lisäksi tietokone saa suuren määrän informaatiota ihmisen kognitiivisista ja affektiivisista tiloista. Esimerkiksi mittaamalla ihmisen sykettä tai ihon sähkönjohtavuutta voi tietokone päätellä onko käyttäjä juuri nyt kiihtynyt tai rentoutunut.
Tällaista fysiologisten signaalien reaaliaikaista hyödyntämistä ihmisen ja koneen vuorovaikutuksessa on tutkittu onnistuneesti monessa eri yhteyksissä: autonkuljettajien väsymystä voidaan mitata ja tarvittaessa varoittaa ajajaa, tietokonepelaajia mittaamalla on mahdollista säätää pelin vaikeustasoa sopivaksi ja älykello voi reagoida käyttäjän stressiin ehdottamalla rentoutumisharjoitusta. Näitä tapauksia yhdistää se, että käyttäjän fysiologisia signaaleja käytetään reaaliajassa sopeuttamaan järjestelmä käyttäjän itsensä tarpeisiin. Tällaista järjestelmän sopeuttamista reaaliajassa käyttäjän fysiologisten signaalien perusteella kutsutaan “biokyberneettiseksi silmukaksi” (biocybernetic loop).
Biokyberneettisen silmukka on perinteisesti määritelty systeemin sopeuttamiseen yksittäisen käyttäjän sen hetkisen fysiologisen vasteen mukaan. Väitöskirjan tarkoitus on tutkia kuinka biokyberneettisen silmukan dynamiikkaa voidaan laajentaa sekä tilassa (voiko silmukka käsittää useita käyttäjiä) ja ajassa (voiko silmukan idea toimia myös ei-reaaliajassa). Erityisesti keskitytään tutkimaan kuinka muutokset silmukan dynamiikassa vaikuttavat silmukan toteutuksen yksityiskohtiin: kannattaako käyttäjää mallintaa eri tavoin ja ovatko tietyn tyyppiset silmukat soveltuvampia koneoppimiseen verrattuna ns. käsintehtyyn ratkaisuun.
Väitöskirja sisältää kahdeksan käyttäjätutkimusta, jotka peilaavat biokyberneettisen silmukan käyttäytymistä erilaisissa konteksteissa. Tutkimukset osoittavat, että biokyberneettistä silmukkaa voidaan käyttää myös osana järjestelmän suunnittelua kun fysiologisten mittausten tulokset ohjataan järjestelmän kehittelijöille, ja järjestelmän muiden käyttäjien auttamiseen suosittelujärjestelmissä, joissa käyttäjän antamaa implisiittistä palautetta käytetään hyväksi suositeltaessa tuotteita toisille käyttäjille
Connecting Brains and Bodies: Applying Physiological Computing to Support Social Interaction
Physiological and affective computing propose methods to improve human-machine interactions by adapting machines to the users' states. Recently, social signal processing (SSP) has proposed to apply similar methods to human-human interactions with the hope of better understanding and modeling social interactions. Most of the social signals employed are facial expressions, body movements and speech, but studies using physiological signals remain scarce. In this paper, we motivate the use of physiological signals in the context of social interactions. Specifically, we review studies which have investigated the relationship between various physiological indices and social interactions. We then propose two main directions to apply physiological SSP: using physiological signals of individual users as new social cues displayed in the group and using inter-user physiology to measure properties of the interactions such as conflict and social presence. We conclude that physiological measures have the potential to enhance social interactions and to connect peopl
Physio-Stacks: Supporting Communication with Ourselves and Others via Tangible, Modular Physiological Devices
International audienceOur physiological activity reflects our inner workings. However, we are not always aware of it in full detail. Physiological devices allow us to monitor and create adaptive systems and support introspection. Given that these devices have access to sensitive data, it is vital that users have a clear understanding of the internal mechanisms (extrospection), yet the underlying processes are hard to understand and control, resulting in a loss of agency. In this work, we focus on bringing the agency back to the user, by using design guidelines based on principles of honest communication and driven by positive activities. To this end, we conceived a tangible, modular approach for the construction of physiological interfaces that can be used as a prototyping toolkit by designers and researchers, or as didactic tools by educators and pupils. We show the potential of such an approach with a set of examples, supporting introspection, dialog, music creation, and play
Neurofeedback Learning is Skill Acquisition but does not Guarantee Treatment Benefit : Continuous-Time Analysis of Learning-Curves from a Clinical Trial for ADHD
Neurofeedback for attention deficit/hyperactivity disorder (ADHD) has long been studied as an alternative to medication, promising non-invasive treatment with minimal side-effects and sustained outcome. However, debate continues over the efficacy of neurofeedback, partly because existing evidence for efficacy is mixed and often non-specific, with unclear relationships between prognostic variables, patient performance when learning to self-regulate, and treatment outcomes. We report an extensive analysis on the understudied area of neurofeedback learning. Our data comes from a randomised controlled clinical trial in adults with ADHD (registered trial ISRCTN13915109; N=23; 13:10 female:male; age 25-57). Patients were treated with either theta-beta ratio or sensorimotor-rhythm regimes for 40 one-hour sessions. We classify 11 learners vs 12 non-learners by the significance of random slopes in a linear mixed growth-curve model. We then analyse the predictors, outcomes, and processes of learners vs non-learners, using these groups as mutual controls. Significant predictive relationships were found in anxiety disorder (GAD), dissociative experience (DES), and behavioural inhibition (BIS) scores obtained during screening. Low DES, but high GAD and BIS, predicted positive learning. Patterns of behavioural outcomes from Test Of Variables of Attention, and symptoms from adult ADHD Self-Report Scale, suggested that learning itself is not required for positive outcomes. Finally, the learning process was analysed using structural-equations modelling with continuous-time data, estimating the short-term and sustained impact of each session on learning. A key finding is that our results support the conceptualisation of neurofeedback learning as skill acquisition, and not merely operant conditioning as originally proposed in the literature.Peer reviewe
Suomalainen rahapelitutkimuksen bibliografia 2016–2020
publishedVersionNon peer reviewe
NON-VERBAL COMMUNICATION WITH PHYSIOLOGICAL SENSORS. THE AESTHETIC DOMAIN OF WEARABLES AND NEURAL NETWORKS
Historically, communication implies the transfer of information between bodies, yet this
phenomenon is constantly adapting to new technological and cultural standards. In a
digital context, it’s commonplace to envision systems that revolve around verbal modalities.
However, behavioural analysis grounded in psychology research calls attention to
the emotional information disclosed by non-verbal social cues, in particular, actions that
are involuntary. This notion has circulated heavily into various interdisciplinary computing
research fields, from which multiple studies have arisen, correlating non-verbal
activity to socio-affective inferences. These are often derived from some form of motion
capture and other wearable sensors, measuring the ‘invisible’ bioelectrical changes that
occur from inside the body.
This thesis proposes a motivation and methodology for using physiological sensory
data as an expressive resource for technology-mediated interactions. Initialised from a
thorough discussion on state-of-the-art technologies and established design principles
regarding this topic, then applied to a novel approach alongside a selection of practice
works to compliment this. We advocate for aesthetic experience, experimenting with
abstract representations. Atypically from prevailing Affective Computing systems, the
intention is not to infer or classify emotion but rather to create new opportunities for rich
gestural exchange, unconfined to the verbal domain.
Given the preliminary proposition of non-representation, we justify a correspondence
with modern Machine Learning and multimedia interaction strategies, applying an iterative,
human-centred approach to improve personalisation without the compromising
emotional potential of bodily gesture. Where related studies in the past have successfully
provoked strong design concepts through innovative fabrications, these are typically limited
to simple linear, one-to-one mappings and often neglect multi-user environments;
we foresee a vast potential. In our use cases, we adopt neural network architectures to
generate highly granular biofeedback from low-dimensional input data.
We present the following proof-of-concepts: Breathing Correspondence, a wearable
biofeedback system inspired by Somaesthetic design principles; Latent Steps, a real-time auto-encoder to represent bodily experiences from sensor data, designed for dance performance;
and Anti-Social Distancing Ensemble, an installation for public space interventions,
analysing physical distance to generate a collective soundscape. Key findings are
extracted from the individual reports to formulate an extensive technical and theoretical
framework around this topic. The projects first aim to embrace some alternative perspectives
already established within Affective Computing research. From here, these concepts
evolve deeper, bridging theories from contemporary creative and technical practices with
the advancement of biomedical technologies.Historicamente, os processos de comunicação implicam a transferência de informação
entre organismos, mas este fenómeno está constantemente a adaptar-se a novos padrões
tecnológicos e culturais. Num contexto digital, é comum encontrar sistemas que giram
em torno de modalidades verbais. Contudo, a análise comportamental fundamentada
na investigação psicológica chama a atenção para a informação emocional revelada por
sinais sociais não verbais, em particular, acções que são involuntárias. Esta noção circulou
fortemente em vários campos interdisciplinares de investigação na área das ciências da
computação, dos quais surgiram múltiplos estudos, correlacionando a actividade nãoverbal
com inferências sócio-afectivas. Estes são frequentemente derivados de alguma
forma de captura de movimento e sensores “wearable”, medindo as alterações bioeléctricas
“invisíveis” que ocorrem no interior do corpo.
Nesta tese, propomos uma motivação e metodologia para a utilização de dados sensoriais
fisiológicos como um recurso expressivo para interacções mediadas pela tecnologia.
Iniciada a partir de uma discussão aprofundada sobre tecnologias de ponta e princípios
de concepção estabelecidos relativamente a este tópico, depois aplicada a uma nova abordagem,
juntamente com uma selecção de trabalhos práticos, para complementar esta.
Defendemos a experiência estética, experimentando com representações abstractas. Contrariamente
aos sistemas de Computação Afectiva predominantes, a intenção não é inferir
ou classificar a emoção, mas sim criar novas oportunidades para uma rica troca gestual,
não confinada ao domínio verbal.
Dada a proposta preliminar de não representação, justificamos uma correspondência
com estratégias modernas de Machine Learning e interacção multimédia, aplicando uma
abordagem iterativa e centrada no ser humano para melhorar a personalização sem o
potencial emocional comprometedor do gesto corporal. Nos casos em que estudos anteriores
demonstraram com sucesso conceitos de design fortes através de fabricações
inovadoras, estes limitam-se tipicamente a simples mapeamentos lineares, um-para-um,
e muitas vezes negligenciam ambientes multi-utilizadores; com este trabalho, prevemos
um potencial alargado. Nos nossos casos de utilização, adoptamos arquitecturas de redes
neurais para gerar biofeedback altamente granular a partir de dados de entrada de baixa dimensão.
Apresentamos as seguintes provas de conceitos: Breathing Correspondence, um sistema
de biofeedback wearable inspirado nos princípios de design somaestético; Latent
Steps, um modelo autoencoder em tempo real para representar experiências corporais
a partir de dados de sensores, concebido para desempenho de dança; e Anti-Social Distancing
Ensemble, uma instalação para intervenções no espaço público, analisando a
distância física para gerar uma paisagem sonora colectiva. Os principais resultados são
extraídos dos relatórios individuais, para formular um quadro técnico e teórico alargado
para expandir sobre este tópico. Os projectos têm como primeiro objectivo abraçar algumas
perspectivas alternativas às que já estão estabelecidas no âmbito da investigação
da Computação Afectiva. A partir daqui, estes conceitos evoluem mais profundamente,
fazendo a ponte entre as teorias das práticas criativas e técnicas contemporâneas com o
avanço das tecnologias biomédicas
The Link Between Flow and Performance is Moderated by Task Experience
Flow is an intrinsically motivating (i.e. 'autotelic') psychological state of complete absorption in moment-tomoment activity that can occur when one performs a task whose demands match one's skill-level. Flow theory proposes that Flow causally leads to better performance, but empirical evidence for this assumption is mixed. Recent evidence suggests that self-reported Flow may not be linked to performance-levels per se, but instead to deviations from anticipated performance (the so-called flow deviation, or F-d effect). We aimed to replicate and extend these results by employing a high-speed steering game (CogCarSim) to elicit Flow, and specifically focused on the moderating effects of learning and task experience on the F-d effect. In a longitudinal design, 18 participants each played CogCarSim for 40 trials across eight sessions, totaling 720 measurements across participants. CogCarSim reliably elicited Flow, and learning to play the game fit well to a power-law model. We successfully replicated the F-d effect: self-reported Flow was much more strongly associated with deviationfrom-expected performance than with objective performance levels. We also found that the F-d effect grew stronger with increasing task experience, thus demonstrating an effect of learning on Flow. We discuss the implications of our findings for contemporary theories of Flow.Peer reviewe