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This thesis is an invitation to rethink how simplistic approaches to biosignal processing enrich the potential of personal sensing in line with the fast development of what have come to be undeniably ubiquitous technologies. Moreover, the aim of taking signal processing, biomedical engineering techniques and machine learning, “Making biosensing available” to design with, is at the core of the thesis. Entering interaction design, pushing for mid tools that expose the affordances of the technologies used, embracing first-person and body-centred design and addressing how biosignal data touches sense-making, ownership and interpretation, a disruption that makes society reflect on how sociodigital materials affect our lives is sought. Esta tesis es una invitación a repensar cómo ciertos abordajes simplistas en procesamiento de bioseñales enriquecen el potencial de los sensores personales, en línea con el rápido desarrollo de lo que han acabado por ser tecnologías innegablemente ubicuas. Más allá de ello, el objetivo de combinar el procesamiento de señales, las técnicas de la ingeniería biomédica y aprendizaje automático, para hacer que los biosensores estén disponibles” para diseñar con ellos, constituye el núcleo central de esta tesis. Abordando el diseño de interacciones, apostando por herramientas intermedias que expongan las potencialidades de las tecnologías utilizadas, abrazando el diseño en primera-persona y centrado en el cuerpo, y analizando cómo los datos de bioseñales tienen un impacto en la construcción de significado, su posesión e interpretación, se busca una disrupción que haga a nuestra sociedad reflexionar sobre cómo los materiales sociodigitales afectan a nuestras vidas. Programa de Doctorat en Informàtica

Deep Pressure Therapy relies on exerting firm touch to help individuals with sensory sensitivity. We performed first-person explorations of deep pressure enabled by shape-changing actuation driven by breathing sensing. This revealed a novel design space with rich, evocative, aesthetically interesting interactions that can help increase breathing awareness and appreciation through: (1) applying symmetrical as well as asymmetrical pressure on the torso; (2) using pressure to direct attention to muscles or bone structure involved in different breathing patterns; (3) apply synchronous as well as asynchronous feedback following or opposing the user’s breathing rhythm through applying rhythmic pressure. Taken together these explorations led us to design (4) breathing correspondence interactions – a balance point right between leading and following users’ breathing patterns by first applying deep pressure – almost to the point of being unpleasant – and then releasing in rhythmic flow. QC 20210521

Research in the use of ubiquitous technologies, tracking systems and wearables within mental health domains is on the rise. In recent years, affective technologies have gained traction and garnered the interest of interdisciplinary fields as the research on such technologies matured. However, while the role of movement and bodily experience to affective experience is well-established, how to best address movement and engagement beyond measuring cues and signals in technology-driven interactions has been unclear. In a joint industry-academia effort, we aim to remodel how affective technologies can help address body and emotional self-awareness. We present an overview of biosignals that have become standard in low-cost physiological monitoring and show how these can be matched with methods and engagements used by interaction designers skilled in designing for bodily engagement and aesthetic experiences. Taking both strands of work together offers unprecedented design opportunities that inspire further research. Through first-person soma design, an approach that draws upon the designer&rsquo, s felt experience and puts the sentient body at the forefront, we outline a comprehensive work for the creation of novel interactions in the form of couplings that combine biosensing and body feedback modalities of relevance to affective health. These couplings lie within the creation of design toolkits that have the potential to render rich embodied interactions to the designer/user. As a result we introduce the concept of &ldquo, orchestration&rdquo, By orchestration, we refer to the design of the overall interaction: coupling sensors to actuation of relevance to the affective experience, initiating and closing the interaction, habituating, helping improve on the users&rsquo, body awareness and engagement with emotional experiences, soothing, calming, or energising, depending on the affective health condition and the intentions of the designer. Through the creation of a range of prototypes and couplings we elicited requirements on broader orchestration mechanisms. First-person soma design lets researchers look afresh at biosignals that, when experienced through the body, are called to reshape affective technologies with novel ways to interpret biodata, feel it, understand it and reflect upon our bodies.

Sepsis is a life threatening condition that can be treated if detected early. This paper presents a study of the application of a Ring Topology Echo State Network (ESN) algorithm to a sepsis prediction task based on ICU records. The implemented algorithm is compared with commonly used classifiers and a combination of both approaches. Finally, we address how different causal strategies on filling missing record values affected the final classification performances. Having a dataset with a limited number of time entries per patient, the utility score U = 0.188 obtained (team 51: PLUX) suggests that further research is needed in order for the ESN to capture the temporal dynamics of the problem at hand

Research in physiology and sports science has shown that fatigue, a complex psychophysiological phenomenon, has a relevant impact in performance and in the correct functioning of our motricity system, potentially being a cause of damage to the human organism. Fatigue can be seen as a subjective or objective phenomenon. Subjective fatigue corresponds to a mental and cognitive event, while fatigue referred as objective is a physical phenomenon. Despite the fact that subjective fatigue is often undervalued, only a physically and mentally healthy athlete is able to achieve top performance in a discipline. Therefore, we argue that physical training programs should address the preventive assessment of both subjective and objective fatigue mechanisms in order to minimize the risk of injuries. In this context, our paper presents a machine-learning system capable of extracting individual fatigue descriptors (IFDs) from electromyographic (EMG) and heart rate variability (HRV) measurements. Our novel approach, using two types of biosignals so that a global (mental and physical) fatigue assessment is taken into account, reflects the onset of fatigue by implementing a combination of a dimensionless (0-1) global fatigue descriptor (GFD) and a support vector machine (SVM) classifier. The system, based on 9 main combined features, achieves fatigue regime classification performances of0.82±0.24, ensuring a successful preventive assessment when dangerous fatigue levels are reached. Training data were acquired in a constant work rate test (executed by 14 subjects using a cycloergometry device), where the variable under study (fatigue) gradually increased until the volunteer reached an objective exhaustion state.