Moving on from Weiser's Vision of Calm Computing: engaging UbiComp experiences

A motivation behind much UbiComp research has been to make our lives convenient, comfortable and informed, following in the footsteps of Weiser's calm computing vision. Three themes that have dominated are context awareness, ambient intelligence and monitoring/tracking. While these avenues of research have been fruitful their accomplishments do not match up to anything like Weiser's world. This paper discusses why this is so and argues that is time for a change of direction in the field. An alternative agenda is outlined that focuses on engaging rather than calming people. Humans are very resourceful at exploiting their environments and extending their capabilities using existing strategies and tools. I describe how pervasive technologies can be added to the mix, outlining three areas of practice where there is much potential for professionals and laypeople alike to combine, adapt and use them in creative and constructive ways

Technologies to support community-dwelling persons with dementia: a position paper on issues regarding development, usability, effectiveness and cost-effectiveness, deployment, and ethics

Background: With the expected increase in the numbers of persons with dementia, providing timely, adequate, and affordable care and support is challenging. Assistive and health technologies may be a valuable contribution in dementia care, but new challenges may emerge. Objective: The aim of our study was to review the state of the art of technologies for persons with dementia regarding issues on development, usability, effectiveness and cost-effectiveness, deployment, and ethics in 3 fields of application of technologies: (1) support with managing everyday life, (2) support with participating in pleasurable and meaningful activities, and (3) support with dementia health and social care provision. The study also aimed to identify gaps in the evidence and challenges for future research. Methods: Reviews of literature and expert opinions were used in our study. Literature searches were conducted on usability, effectiveness and cost-effectiveness, and ethics using PubMed, Embase, CINAHL, and PsycINFO databases with no time limit. Selection criteria in our selected technology fields were reviews in English for community-dwelling persons with dementia. Regarding deployment issues, searches were done in Health Technology Assessment databases Results: According to our results, persons with dementia want to be included in the development of technologies; there is little research on the usability of assistive technologies; various benefits are reported but are mainly based on low-quality studies; barriers to deployment of technologies in dementia care were identified, and ethical issues were raised by researchers but often not studied. Many challenges remain such as including the target group more often in development, performing more high-quality studies on usability and effectiveness and cost-effectiveness, creating and having access to high-quality datasets on existing technologies to enable adequate deployment of technologies in dementia care, and ensuring that ethical issues are considered an important topic for researchers to include in their evaluation of assistive technologies. Conclusions: Based on these findings, various actions are recommended for development, usability, effectiveness and cost-effectiveness, deployment, and ethics of assistive and health technologies across Europe. These include avoiding replication of technology development that is unhelpful or ineffective and focusing on how technologies succeed in addressing individual needs of persons with dementia. Furthermore, it is suggested to include these recommendations in national and international calls for funding and assistive technology research programs. Finally, practitioners, policy makers, care insurers, and care providers should work together with technology enterprises and researchers to prepare strategies for the implementation of assistive technologies in different care settings. This may help future generations of persons with dementia to utilize available and affordable technologies and, ultimately, to benefit from them

현장 데이터 수집 능력을 확장하기 위한 자유도 높은 셀프 트래킹 기술의 디자인

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2019. 2. 서진욱.Collecting and tracking data in everyday contexts is a common practice for both individual self-trackers and researchers. The increase in wearable and mobile technologies for self-tracking encourages people to gain personal insights from the data about themselves. Also, researchers exploit self-tracking to gather data in situ or to foster behavioral change. Despite a diverse set of available tracking tools, however, it is still challenging to find ones that suit unique tracking needs, preferences, and commitments. Individual self-tracking practices are constrained by the tracking tools' initial design, because it is difficult to modify, extend, or mash up existing tools. Limited tool support also impedes researchers' efforts to conduct in situ data collection studies. Many researchers still build their own study instruments due to the mismatch between their research goals and the capabilities of existing toolkits. The goal of this dissertation is to design flexible self-tracking technologies that are generative and adaptive to cover diverse tracking contexts, ranging from personal tracking to research contexts. Specifically, this dissertation proposes OmniTrack, a flexible self-tracking approach leveraging a semi-automated tracking concept that combines manual and automated tracking methods to generate an arbitrary tracker design. OmniTrack was implemented as a mobile app for individuals. The OmniTrack app enables self-trackers to construct their own trackers and customize tracking items to meet their individual needs. A usability study and a field development study were conducted with the goal of assessing how people adopt and adapt OmniTrack to fulfill their needs. The studies revealed that participants actively used OmniTrack to create, revise, and appropriate trackers, ranging from a simple mood tracker to a sophisticated daily activity tracker with multiple fields. Furthermore, OmniTrack was extended to cover research contexts that enclose manifold personal tracking contexts. As part of the research, this dissertation presents OmniTrack Research Kit, a research platform that allows researchers without programming expertise to configure and conduct in situ data collection studies by deploying the OmniTrack app on participants' smartphones. A case study in deploying the research kit for conducting a diary study demonstrated how OmniTrack Research Kit could support researchers who manage study participants' self-tracking process. This work makes artifacts contributions to the fields of human-computer interaction and ubiquitous computing, as well as expanding empirical understanding of how flexible self-tracking tools can enhance the practices of individual self-trackers and researchers. Moreover, this dissertation discusses design challenges for flexible self-tracking technologies, opportunities for further improving the proposed systems, and future research agenda for reaching the audiences not covered in this research.일상의 맥락에서 데이터를 모으는 활동인 셀프 트래킹(self-tracking)은 개인과 연구의 영역에서 활발히 활용되고 있다. 웨어러블 디바이스와 모바일 기술의 발달로 인해 사람들은 각자의 삶에 대해 말해주는 데이터를 더 쉽게 수집하고, 통찰할 수 있게 되었다. 또한, 연구자들은 현장(in situ) 데이터를 수집하거나 사람들에게 행동 변화를 일으키는 데에 셀프 트래킹을 활용한다. 비록 셀프 트래킹을 위한 다양한 도구들이 존재하지만, 트래킹에 대해 다양화된 요구와 취향을 완벽히 충족하는 것들을 찾는 것은 쉽지 않다. 대부분의 셀프 트래킹 도구는 이미 설계된 부분을 수정하거나 확장하기에 제한적이다. 그렇기 때문에 사람들의 셀프 트래킹에 대한 자유도는 기존 도구들의 디자인 공간에 의해 제약을 받을 수밖에 없다. 마찬가지로, 현장 데이터를 수집하는 연구자들도 이러한 도구의 한계로 인해 여러 문제에 봉착한다. 연구자들이 데이터를 통해 답하고자 하는 연구 질문(research question)은 분야가 발전할수록 세분되고, 치밀해지기 때문에 이를 위해서는 복잡하고 고유한 실험 설계가 필요하다. 하지만 현존하는 연구용 셀프 트래킹 플랫폼들은 이에 부합하는 자유도를 발휘하지 못한다. 이러한 간극으로 인해 많은 연구자들이 각자의 현장 데이터 수집 연구에 필요한 디지털 도구들을 직접 구현하고 있다. 본 연구의 목표는 자유도 높은---연구적 맥락과 개인적 맥락을 아우르는 다양한 상황에 활용할 수 있는---셀프 트래킹 기술을 디자인하는 것이다. 이를 위해 본고에서는 옴니트랙(OmniTrack)이라는 디자인 접근법을 제안한다. 옴니트랙은 자유도 높은 셀프 트래킹을 위한 방법론이며, 반자동 트래킹(semi-automated tracking)이라는 컨셉을 바탕으로 수동 방식과 자동 방식의 조합을 통해 임의의 트래커를 표현할 수 있다. 먼저 옴니트랙을 개인을 위한 모바일 앱 형태로 구현하였다. 옴니트랙 앱은 개개인이 자신의 트래킹 니즈에 맞는 트래커를 커스터마이징하여 활용할 수 있도록 구성되어 있다. 본고에서는 사람들이 어떻게 옴니트랙을 자신의 니즈에 맞게 활용하는지 알아보고자 사용성 테스트(usability testing)와 필드 배포 연구(field deployment study)를 수행하였다. 참가자들은 옴니트랙을 활발히 이용해 다양한 디자인의 트래커—아주 단순한 감정 트래커부터 여러 개의 필드를 가진 복잡한 일일 활동 트래커까지—들을 생성하고, 수정하고, 활용하였다. 다음으로, 옴니트랙을 현장 데이터 수집 연구에 활용할 수 있도록 연구 플랫폼 형태의 '옴니트랙 리서치 킷(OmniTrack Research Kit)'으로 확장하였다. 옴니트랙 리서치 킷은 연구자들이 프로그래밍 언어 없이 원하는 실험을 설계하고 옴니트랙 앱을 참가자들의 스마트폰으로 배포할 수 있도록 디자인되었다. 그리고 옴니트랙 리서치 킷을 이용해 일지기록 연구(diary study)를 직접 수행하였고, 이를 통해 옴니트랙 접근법이 어떻게 연구자들의 연구 목적을 이루는 데에 도움을 줄 수 있는지 직접 확인하였다. 본 연구는 휴먼-컴퓨터 인터랙션(Human-Computer Interaction) 및 유비쿼터스 컴퓨팅(Ubiquitous Computing) 분야에 기술적 산출물로써 기여하며, 자유도 높은 셀프 트래킹 도구가 어떻게 개인과 연구자들을 도울 수 있는지 실증적인 이해를 증진한다. 또한, 자유도 높은 셀프트래킹 기술에 대한 디자인적 난제, 연구에서 제시한 시스템에 대한 개선방안, 마지막으로 본 연구에서 다루지 못한 다른 집단을 지원하기 위한 향후 연구 논제에 대하여 논의한다.Abstract CHAPTER 1. Introduction 1.1 Background and Motivation 1.2 Research Questions and Approaches 1.2.1 Designing a Flexible Self-Tracking Approach Leveraging Semiautomated Tracking 1.2.2 Design and Evaluation of OmniTrack in Individual Tracking Contexts 1.2.3 Designing a Research Platform for In Situ Data Collection Studies Leveraging OmniTrack 1.2.4 A Case Study of Conducting an In Situ Data Collection Study using the Research Platform 1.3 Contributions 1.4 Structure of this Dissertation CHAPTER 2. Related Work 2.1 Background on Self-Tracking 2.1.1 Self-Tracking in Personal Tracking Contexts 2.1.2 Utilization of Self-Tracking in Other Contexts 2.2 Barriers Caused by Limited Tool Support 2.2.1 Limited Tools and Siloed Data in Personal Tracking 2.2.2 Challenges of the Instrumentation for In Situ Data Collection 2.3 Flexible Self-Tracking Approaches 2.3.1 Appropriation of Generic Tools 2.3.2 Universal Tracking Systems for Individuals 2.3.3 Research Frameworks for In Situ Data Collection 2.4 Grounding Design Approach: Semi-Automated Tracking 2.5 Summary of Related Work CHAPTER3 DesigningOmniTrack: a Flexible Self-Tracking Approach 3.1 Design Goals and Rationales 3.2 System Design and User Interfaces 3.2.1 Trackers: Enabling Flexible Data Inputs 3.2.2 Services: Integrating External Trackers and Other Services 3.2.3 Triggers: Retrieving Values Automatically 3.2.4 Streamlining Tracking and Lowering the User Burden 3.2.5 Visualization and Feedback 3.3 OmniTrack Use Cases 3.3.1 Tracker 1: Beer Tracker 3.3.2 Tracker 2: SleepTight++ 3.3.3 Tracker 3: Comparison of Automated Trackers 3.4 Summary CHAPTER 4. Understanding HowIndividuals Adopt and Adapt OmniTrack 4.1 Usability Study 4.1.1 Participants 4.1.2 Procedure and Study Setup 4.1.3 Tasks 4.1.4 Results and Discussion 4.1.5 Improvements A_er the Usability Study 4.2 Field Deployment Study 4.2.1 Study Setup 4.2.2 Participants 4.2.3 Data Analysis and Results 4.2.4 Reflections on the Deployment Study 4.3 Discussion 4.3.1 Expanding the Design Space for Self-Tracking 4.3.2 Leveraging Other Building Blocks of Self-Tracking 4.3.3 Sharing Trackers with Other People 4.3.4 Studying with a Broader Audience 4.4 Summary CHAPTER 5. Extending OmniTrack for Supporting In Situ Data Collection Studies 5.1 Design Space of Study Instrumentation for In-Situ Data Collection 5.1.1 Experiment-Level Dimensions 5.1.2 Condition-Level Dimensions 5.1.3 Tracker-Level Dimensions 5.1.4 Reminder/Trigger-Level Dimensions 5.1.5 Extending OmniTrack to Cover the Design Space 5.2 Design Goals and Rationales 5.3 System Design and User Interfaces 5.3.1 Experiment Management and Collaboration 5.3.2 Experiment-level Configurations 5.3.3 A Participants Protocol for Joining the Experiment 5.3.4 Implementation 5.4 Replicated Study Examples 5.4.1 Example A: Revisiting the Deployment Study of OmniTrack 5.4.2 Example B: Exploring the Clinical Applicability of a Mobile Food Logger 5.4.3 Example C: Understanding the Effect of Cues and Positive Reinforcement on Habit Formation 5.4.4 Example D: Collecting Stress and Activity Data for Building a Prediction Model 5.5 Discussion 5.5.1 Supporting Multiphase Experimental Design 5.5.2 Serving as Testbeds for Self-Tracking Interventions 5.5.3 Exploiting the Interaction Logs 5.6 Summary CHAPTER 6. Using the OmniTrack Research Kit: A Case Study 6.1 Study Background and Motivation 6.2 OmniTrack Configuration for Study Instruments 6.3 Participants 6.4 Study Procedure 6.5 Dataset and Analysis 6.6 Study Result 6.6.1 Diary Entries 6.6.2 Aspects of Productivity Evaluation 6.6.3 Productive Activities 6.7 Experimenter Experience of OmniTrack 6.8 Participant Experience of OmniTrack 6.9 Implications 6.9.1 Visualization Support for Progressive, Preliminary Analysis of Collected Data 6.9.2 Inspection to Prevent Misconfiguration 6.9.3 Providing More Alternative Methods to Capture Data 6.10 Summary CHAPTER 7. Discussion 7.1 Lessons Learned 7.2 Design Challenges and Implications 7.2.1 Making the Flexibility Learnable 7.2.2 Additive vs. Subtractive Design for Flexibility 7.3 Future Opportunities for Improvement 7.3.1 Utilizing External Information and Contexts 7.3.2 Providing Flexible Visual Feedback 7.4 Expanding Audiences of OmniTrack 7.4.1 Supporting Clinical Contexts 7.4.2 Supporting Self-Experimenters 7.5 Limitations CHAPTER 8. Conclusion 8.1 Summary of the Approaches 8.2 Summary of Contributions 8.2.1 Artifact Contributions 8.2.2 Empirical Research Contributions 8.3 Future Work 8.3.1 Understanding the Long-term E_ect of OmniTrack 8.3.2 Utilizing External Information and Contexts 8.3.3 Extending the Input Modality to Lower the Capture Burden 8.3.4 Customizable Visual Feedback 8.3.5 Community-Driven Tracker Sharing 8.3.6 Supporting Multiphase Study Design 8.4 Final Remarks APPENDIX A. Study Material for Evaluations of the OmniTrack App A.1 Task Instructions for Usability Study A.2 The SUS (System Usability Scale) Questionnaire A.3 Screening Questionnaire for Deployment Study A.4 Exit Interview Guide for Deployment Study A.5 Deployment Participant Information APPENDIX B Study Material for Productivity Diary Study B.1 Recruitment Screening Questionnaire B.2 Exit Interview Guide Abstract (Korean)Docto

Semantics-based selection of everyday concepts in visual lifelogging

Concept-based indexing, based on identifying various semantic concepts appearing in multimedia, is an attractive option for multimedia retrieval and much research tries to bridge the semantic gap between the media’s low-level features and high-level semantics. Research into concept-based multimedia retrieval has generally focused on detecting concepts from high quality media such as broadcast TV or movies, but it is not well addressed in other domains like lifelogging where the original data is captured with poorer quality. We argue that in noisy domains such as lifelogging, the management of data needs to include semantic reasoning in order to deduce a set of concepts to represent lifelog content for applications like searching, browsing or summarisation. Using semantic concepts to manage lifelog data relies on the fusion of automatically-detected concepts to provide a better understanding of the lifelog data. In this paper, we investigate the selection of semantic concepts for lifelogging which includes reasoning on semantic networks using a density-based approach. In a series of experiments we compare different semantic reasoning approaches and the experimental evaluations we report on lifelog data show the efficacy of our approach

A Pilot Study for Utilizing Additive Manufacturing and Responsive Rewards in Physical Activity Gamification

Health related issues from being overweight or obese are significant global challenges, and whilst increased activity is known to reduce the health risks associated with these conditions, current wearable and activity tracking devices alone are insufficient to motivate everyone over the long-term necessary to make significant change. This paper explores novel gamified systems as part of a pilot study to leverage additive manufacturing and Internet of Things technologies to increase motivation for physical activity, creating new ways for people to be rewarded in the physical world, and for activity data to be communicated in more abstract and customisable ways. These systems were exhibited and discussed at the 2017 Design 4 Health conference in Melbourne, Australia, and are intended to contribute to research by designers and fitness companies in thinking beyond the digital interface, and in particular to engage young people in the physical world

Sensing and Visualizing Social Context from Spatial Proximity

The concept of pervasive computing, as introduced by Marc Weiser under the name ubiquitous computing in the early 90s, spurred research into various kinds of context-aware systems and applications. There is a wide range of contextual parameters, including location, time, temperature, devices and people in proximity, which have been part of the initial ideas about context-aware computing. While locational context is already a well understood concept, social context---based on the people around us---proves to be harder to grasp and to operationalize. This work continues the line of research into social context, which is based on the proximity and meeting patterns of people in the physical space. It takes this research out of the lab and out of well controlled situations into our urban environments, which are full of ambiguity and opportunities. The key to this research is the tool that caused dramatic change in individual and collective behavior during the last 20 years and which is a manifestation of many of the ideas of the pervasive computing paradigm: the mobile phone. In this work, the mobile is regarded as a proxy for people. Through it, the social environment becomes accessible to digital measurement and processing. To understand the large amount of data that now becomes available to automatic measurement, we will turn to the discipline of social network analysis. It provides powerful methods, that are able to condense data and extract relevant meaning. Visualization helps to understand and interpret the results. This thesis contains a number of experiments, that demonstrate how the automatic measurement of social proximity data through Bluetooth can be used to measure variables of personal behavior, group behavior and the behavior of groups in relation to places. The principal contributions are: * A methodology to visualize personal social context by using an ego proximity network. Specific episodes can be localized and compared. * method to compare different days in terms of social context, e.g. to support automatic diary applications. * A method to compose social geographic maps. Locations of similar social context are detected and combined. * Functions to measure short-term changes in social activity, based on the distinction between strange and familiar devices. * The characterization of Bluetooth inquiries for social proximity sensing. * A dataset of Bluetooth sightings from an ego perspective in seven different settings. Additionally, some settings feature multiple stationary scanners and Cell-ID measurements. * Soft- and hardware to capture, collect, store and analyze Bluetooth proximity data