Proceedings of Designing Self-care for Everyday Life. Workshop in conjunction with NordiCHI 2014, 27th October.

Managing chronic conditions can be challenging. People in such conditions, and the people around them, have to, for example: deal with symptoms, adapt to the resulting disability, manage emotions, and change habits to keep the condition under control. Self-care technologies have the potential to support self-care, however they often disregard the complexity of the settings in which they are used and fail to become integrated in everyday life.The present collection of papers forms the Proceedings of the Workshop “Designing Selfcare for Everyday Life” conducted last October 27th, 2014 in Helsinki, where 14 participants from 7 different countries spent the day discussing how to design self-care technologies that are in harmony with people’s everyday life. During the morning, discussions were driven by poster presentations focused on the participants’ work. In the afternoon, we engaged in aparticipatory design exercise focused on the self-care of Parkinson’s disease. Our discussions were driven by the experience of two people living with Parkinson’s that participated in our workshop. At the end of the exercise, each group presented the different insights, concepts and problems that each patient experiences in their everyday life with the disease. Last, we all engaged in a broader discussion with a mapping exercise of issues and challenges in relation to self-care.The contributions featured in the proceedings have been peer-reviewed by the members of the Workshop Program Committee and selected on the basis of their quality, alignment with the workshop theme, and the extent (and diversity) of their backgrounds in design. They express points of view of researchers from both Academia and Industry and provide relevant insights in the design and development use of technologies for self-care.We want to thank all the participants and co-authors for contributing to the Workshop. We are particularly grateful to the two patients, members of the Finnish Parkinson’s Association, who accepted to participate in the workshop and enabled researchers to get aperspective on the challenges of their lives. We also want to thank all the Programme Committee members for all their work during the reviewing process as well as the organisers of NordiCHI 2014 for providing useful facilities

Supporting Diabetes Self-Management with Ubiquitous Computing Technologies: A User-Centered Inquiry

Ubiquitous computing technologies offer opportunities to improve treatments for chronic health conditions. Type 1 diabetes is a compelling use-case for such approaches, given its severity, and need for individuals to make frequent care decisions, informed by complex data. However, current apps, typically based on effortful reflection on collected data, generally show poor adoption, lack vital cognitive and emotional support, and are poorly tailored to users’ actual diabetes decision making processes. This thesis investigates how diabetes apps can be improved from a user-centered perspective. An initial questionnaire-based study investigated how well existing diabetes apps meet user needs. Perceived benefits, limitations, and reasons for low adoption rates were identified. A talk-aloud study of detailed user interactions with diabetes logging apps was conducted to characterize the benefits and limitations of diverse UI elements for T1 diabetes management, and to more precisely identify wider problems with current interaction designs. This led to positing a refined version of Mamykina et al.’s model for diabetes self-management, to account for observed practices, whereby the previously accepted habitual and sensemaking cognitive states are augmented by a posited ‘fluid contextual reasoning’ (FCR) mode, which allows multiple contextual factors to be balanced for dynamic course correction when navigating complex situations, using previously learned knowledge. To investigate user perceptions of the levels and kinds of monitoring anticipated in next generation diabetes decision support systems, a 4-week technology probe, in which participants used multiple networked devices and external data aggregation, was used to frame requirements for user-centered development of such future systems. Integrating all of the above work, an iterative design process was undertaken to create DUETS, a card-based system to facilitate reflection by designers, users, and other stakeholders on diabetes support management systems. The resulting tool and method were then implemented and evaluated through structured sessions with stakeholder focus groups

IoT DEVELOPMENT FOR HEALTHY INDEPENDENT LIVING

The rise of internet connected devices has enabled the home with a vast amount of enhancements to make life more convenient. These internet connected devices can be used to form a community of devices known as the internet of things (IoT). There is great value in IoT devices to promote healthy independent living for older adults. Fall-related injuries has been one of the leading causes of death in older adults. For example, every year more than a third of people over 65 in the U.S. experience a fall, of which up to 30 percent result in moderate to severe injury. Therefore, this thesis proposes an IoT-based fall detection system for smart home environments that not only to send out alerts, but also launches interaction models, such as voice assistance and camera monitoring. Such connectivity could allow older adults to interact with the system without concern of a learning curve. The proposed IoT-based fall detection system will enable family and caregivers to be immediately notified of the event and remotely monitor the individual. Integrated within a smart home environment, the proposed IoT-based fall detection system can improve the quality of life among older adults. Along with the physical concerns of health, psychological stress is also a great concern among older adults. Stress has been linked to emotional and physical conditions such as depression, anxiety, heart attacks, stroke, etc. Increased susceptibility to stress may accelerate cognitive decline resulting in conversion of cognitively normal older adults to MCI (Mild Cognitive Impairment), and MCI to dementia. Thus, if stress can be measured, there can be countermeasures put in place to reduce stress and its negative effects on the psychological and physical health of older adults. This thesis presents a framework that can be used to collect and pre-process physiological data for the purpose of validating galvanic skin response (GSR), heart rate (HR), and emotional valence (EV) measurements against the cortisol and self-reporting benchmarks for stress detection. The results of this framework can be used for feature extraction to feed into a regression model for validating each combination of physiological measurement. Also, the potential of this framework to automate stress protocols like the Trier Social Stress Test (TSST) could pave the way for an IoT-based platform for automated stress detection and management

Monipart: sistema integrado de monitorização de participantes em experiências com aquisição móvel de biossinais

The execution of experimental studies with the retrieval of fisiological data of groups of participants usually makes the researchers use proprietary equipment, with little to no possibility of leaving the laboratory or adapt the capture protocols. The use of mobile devices in coupled with a variety of selected sensors can provide a low cost, portable configuration for the collection of physiological data for investigation. Monipart is a web application that takes advantage of na alredy existing solution for the capture of bio-signals based on mobile devices and improves it by implementing a web module for the parameterization and monitoring of experiments in real-time. The application also offers a friendly environment for the researcher to manage their studies as well as further analisys of the retrieved data. In view of that, the application communicates with a pre-existing collection solution based in mobile devices that uses messages (MQTT) to control and monitor the experiments. The exploration of the collected information is supported by a database designed to handle time-series for added flexibility in the data processing. The application can be easily extended via plugins that can include new functionalities (i.e. specialized viewers for certain types of data) that may be developed by external entities to the project. With this application, the researchers now can plan and follow experiments via a web interface as well as have immediate access to the data.A realização de estudos experimentais com recolha de dados fisiológicos de grupos de participantes obriga frequentemente os investigadores a usar equipamentos proprietários, com poucas possibilidades de sair do laboratório ou adaptar os protocolos de recolha. A utilização de dispositivos móveis, em conjunto com sensores selecionados, pode proporcionar uma configuração de baixo custo e portátil para a recolha de dados fisiológicos, para investigação. O Monipart é uma aplicação web que tira partido de uma solução já existente para a captura de biossinais baseada em dispositivos móveis e que a evolui no sentido de implementar um módulo web para a configuração e monitorização de experiências em tempo real. A aplicação também oferece um ambiente amigável para os investigadores fazerem a gestão dos seus estudos bem como a posterior análise da informação recolhida. Para isso, a aplicação comunica com a solução de recolha preexistente, baseada em dispositivos móveis, recorrendo a mensagens (MQTT), para controlo e monitorização das experiências. A exploração dos dados obtidos é suportada pelo uso de uma base de dados orientada a séries temporais, para flexibilidade adicional no processamento de dados. A aplicação desenvolvida permite uma fácil extensão via plugins para incluir novas funcionalidades (e.g.: visualizadores especializados para certos tipos de dados) que podem ser desenvolvidas por entidades externas ao projeto. Com este trabalho, os investigadores podem agora planear e acompanhar experiências através de uma interface web, bem como ter um acesso imediato aos dados.Mestrado em Engenharia Informátic

Context-sensitive memory augmentation using recorded everyday life data

The recent rise of life-logging technologies and wearable computing gadgets allows the recording of data from our daily lives. Experiences make people what they are. The omnipresent tracking devices and their sensors experience the same things as their owners, thus creating e-memories and surrogate brains. Such life-logs or e-memories contain everything we can sense or our environment senses, like images, heart rates or locations. With this increase of digital personal data we explore challenges and solutions how to use this vast amount of data with the goal to support human memory. To do this, we used a user-centered approach. In the first step we conducted a series of focus groups and an online survey with the goal of understanding the requirements of life-logging tools. The results of the requirement analysis led to the development of a holistic concept of a digital life assistant. Our initial prototype leverages life-log data in form of a smart alarm clock, which provides an automatic morning briefing about the past and the upcoming day via audio and bedside projection. The prototype was finally evaluated in the field in a small-scale pilot study with the focus on the different presentation modes.Die aktuelle Entwicklung von Life-Logging-Technologien und tragbaren Computern ermöglicht die Aufzeichnung von Daten aus dem täglichen Leben. Erfahrungen machen Menschen zu dem was sie sind. Die allgegenwärtigen Aufnahmegeräte erleben dasselbe, wie ihre Besitzer und schaffen damit elektronische Erinnerungen und einen stellvertretenden Verstand. Diese Life-Logs oder elektronischen Erinnerungen beinhalten alles was deren Besitzer oder deren Umgebungen wahrnehmen, wie z. B. Bilder, Herzfrequenzen oder Standorte. Mit diesem Anstieg von digitalen persönlichen Daten erforschen wir Herausforderungen und Lösungen, wie diese gewaltige Datenmenge nutzbar gemacht und das menschliche Gedächtnis unterstützt werden kann. Daher haben wir einen nutzerorientierten Ansatz gewählt. Im ersten Schritt haben wir eine Serie von Fokusgruppen und eine Online-Umfrage durchgeführt, um die Anforderungen von Life-Logging Werkzeugen zu verstehen. Das Ergebnis der Anforderungsanalyse führte zu der Entwicklung eines ganzheitlichen Konzepts eines digitalen persönlichen Assistentens. Unser initialer Prototyp macht sich Life-Logging-Daten in Form eines intelligenten Weckers zu Nutze. Der Assistent bereitet automatisiert ein morgendliches Briefing über die Vergangenheit und den bevorstehenden Tag vor und präsentiert dieses mittels Sprache und einer bettseitigen Projektion. Schließlich wurde der Prototyp im praktischen Einsatz in einer kleinen Pilotstudie mit dem Fokus auf die verschiedenen Präsentationsmodi untersucht

A DATA-DRIVEN APPROACH TO SUPPORTING USERS’ ADAPTATION TO SMART IN-VEHICLE SYSTEMS

The utilization of data to understand user behavior and support user needs began to develop in areas such as internet services, smartphone apps development, and the gaming industry. This bloom of data-driven services and applications forced OEMs to consider possible solutions for better in-vehicle connectivity. However, digital transformation in the automotive sector presents numerous challenges. One of those challenges is identifying and establishing the relevant user-related data that will cover current and future needs to help the automotive industry cope with the digital transformation pace. At the same time, this development should not be sporadic, without a clear purpose or vision of how newly-generated data can support engineers to create better systems for drivers. The important issue is to learn how to extract the knowledge from the immense data we possess, and to understand the extent to which this data can be used.Another challenge is the lack of established approaches towards vehicle data utilization for user-related studies. This area is relatively new to the automotive industry. Despite the positive examples from other fields that demonstrate the potential for data-driven context-aware applications, automotive practices still have gaps in capturing the driving context and driver behavior. This lack of user-related data can partially be explained by the multitasking activities that the driver performs while driving the car and the higher complexity of the automotive context compared to other domains. Thus, more research is needed to explore the capacity of vehicle data to support users in different tasks.Considering all the interrelations between the driver and in-vehicle system in the defined context of use helps to obtain more comprehensive information and better understand how the system under evaluation can be improved to meet driver needs. Tracking driver behavior with the help of vehicle data may provide developers with quick and reliable user feedback on how drivers are using the system. Compared to vehicle data, the driver’s feedback is often incomplete and perception-based since the driver cannot always correlate his behavior to complex processes of vehicle performance or clearly remember the context conditions. Thus, this research aims to demonstrate the ability of vehicle data to support product design and evaluation processes with data-driven automated user insights. This research does not disregard the driver’s qualitative input as unimportant but provides insights into how to better combine quantitative and qualitative methods for more effective results.According to the aim, the research focuses on three main aspects:•\ua0\ua0\ua0\ua0\ua0 Identifying the extent to which vehicle data can contribute to driver behavior understanding.\ua0 •\ua0\ua0\ua0\ua0\ua0 Expanding the concepts for vehicle data utilization to support drivers.•\ua0\ua0\ua0\ua0\ua0 Developing the methodology for a more effective combination of quantitative (vehicle data-based) and qualitative (based on users’ feedback) studies. Additionally, special consideration is given to describing the drawbacks and limitations, to enhance future data-driven applications