Challenges in Developing Applications for Aging Populations

Elderly individuals can greatly benefit from the use of computer applications, which can assist in monitoring health conditions, staying in contact with friends and family, and even learning new things. However, developing accessible applications for an elderly user can be a daunting task for developers. Since the advent of the personal computer, the benefits and challenges of developing applications for older adults have been a hot topic of discussion. In this chapter, the authors discuss the various challenges developers who wish to create applications for the elderly computer user face, including age-related impairments, generational differences in computer use, and the hardware constraints mobile devices pose for application developers. Although these challenges are concerning, each can be overcome after being properly identified

Piloting Multimodal Learning Analytics using Mobile Mixed Reality in Health Education

© 2019 IEEE. Mobile mixed reality has been shown to increase higher achievement and lower cognitive load within spatial disciplines. However, traditional methods of assessment restrict examiners ability to holistically assess spatial understanding. Multimodal learning analytics seeks to investigate how combinations of data types such as spatial data and traditional assessment can be combined to better understand both the learner and learning environment. This paper explores the pedagogical possibilities of a smartphone enabled mixed reality multimodal learning analytics case study for health education, focused on learning the anatomy of the heart. The context for this study is the first loop of a design based research study exploring the acquisition and retention of knowledge by piloting the proposed system with practicing health experts. Outcomes from the pilot study showed engagement and enthusiasm of the method among the experts, but also demonstrated problems to overcome in the pedagogical method before deployment with learners

Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns

We introduce Deep Thermal Imaging, a new approach for close-range automatic recognition of materials to enhance the understanding of people and ubiquitous technologies of their proximal environment. Our approach uses a low-cost mobile thermal camera integrated into a smartphone to capture thermal textures. A deep neural network classifies these textures into material types. This approach works effectively without the need for ambient light sources or direct contact with materials. Furthermore, the use of a deep learning network removes the need to handcraft the set of features for different materials. We evaluated the performance of the system by training it to recognise 32 material types in both indoor and outdoor environments. Our approach produced recognition accuracies above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584 images of 17 outdoor materials. We conclude by discussing its potentials for real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing System

Exploring Antecedents, Performance Outcomes And Psychological Processes Of Multi-Device Use

Given the widespread use of multiple devices (such as desktop computers, smartphones, and tablets) in performing a task, systematic, theoretic, and empirical studies pertaining to motivations regarding, performance outcomes, and attitudes toward multi-device use have become essential. Despite the increasing importance of multi-device use, research remains scarce regarding this topic. To comprehensively understand the issues of multi-device use, this dissertation comprises three complementary studies, each of which focuses on a different aspect of multi-device use: Given the availability of multiple devices, what are the motivations behind multi-device use as opposed to the use of only one device to complete a set of related tasks (antecedents; Study 1)? How do people use multiple devices to lead to better performance than when using a single device (performance outcomes; Study 2)? How do users feel about multi-device use when they are free or forced to switch from using one device to another to complete a task (psychological processes; Study 3)? Drawing on task\u27technology fit theory and mental workload, this dissertation presents two research models for Study 1 and Study 2, in order to gain deep insight into what happens before (i.e., motivations) and after multi-device use (i.e., task performance). Moreover, on the basis of task\u27technology fit theory and psychological reactance theory, this dissertation presents a research model for Study 3 to understand the impact of flexibility of multi-device use on users\u27 attitudes. A survey, video recording, and experiments were conducted to collect data for Study 1, 2, and 3, respectively. Partial least squares were used to analyze research models of Studies 1, 2, and 3. Our empirical findings of Study 1 indicate that perceived task fit with multi-device use is a critical factor that forms users\u27 attitudes toward and expected satisfaction with multi-device use, both of which trigger their intentions to use multiple devices. However, unfamiliarity with multi-device use increases perceived complexity of multi-device use. Such complexity hinders users from perceiving good task fit with multi-device use. The results of Study 2 show that when users can select the right device from their device portfolios to deal with a certain subtask, the task can be completed more quickly and accurately. They also indicate that increasing the number of device switches generates a higher number of application switches and physical movements, both of which add time to task completion. The results of Study 3 indicate the existence of psychological reactance (i.e., as assertive affective and cognitive reactions to a threatened or eliminated freedom) in the context of non-flexibility of multi-device use. This reactance negatively influences affective and cognitive appraisals and in turn affects users\u27 satisfaction with multi-device use and continued intention toward multi-device use. Furthermore, forcing users to use the devices with the best fit for dealing with a simple task forms positive affective appraisals, resulting in a reduction in the detrimental effects of psychological reactance. The results of this dissertation have several theoretical contributions and provide important guidelines for device manufacturers, such as Apple, Samsung, and Google, and for companies whose employees use multiple devices at work. I hope that this dissertation will inspire future research on this emerging and critical topic

Does emotion influence the use of auto-suggest during smartphone typing?

Typing based interfaces are common across many mobile applications, especially messaging apps. To reduce the difficulty of typing using keyboard applications on smartphones, smartwatches with restricted space, several techniques, such as auto-complete, auto-suggest, are implemented. Although helpful, these techniques do add more cognitive load on the user. Hence beyond the importance to improve the word recommendations, it is useful to understand the pattern of use of auto-suggestions during typing. Among several factors that may influence use of auto-suggest, the role of emotion has been mostly overlooked, often due to the difficulty of unobtrusively inferring emotion. With advances in affective computing, and ability to infer user's emotional states accurately, it is imperative to investigate how auto-suggest can be guided by emotion aware decisions. In this work, we investigate correlations between user emotion and usage of auto-suggest i.e. whether users prefer to use auto-suggest in specific emotion states. We developed an Android keyboard application, which records auto-suggest usage and collects emotion self-reports from users in a 3-week in-the-wild study. Analysis of the dataset reveals relationship between user reported emotion state and use of auto-suggest. We used the data to train personalized models for predicting use of auto-suggest in specific emotion state. The model can predict use of auto-suggest with an average accuracy (AUCROC) of 82% showing the feasibility of emotion-aware auto-suggestion

모바일 기기를 통한 식품 구매행동

학위논문(석사)--서울대학교 대학원 :농업생명과학대학 농경제사회학부(지역정보전공),2019. 8. 문정훈.The number of people using mobile devices with a touch-screen interface for online shopping is increasing rapidly. This study focused on the use of mobile devices (as opposed to PCs) when shopping for groceries online. Essay 1 discusses the differences between the use of mobile devices and PCs with regard to consumers grocery purchasing behaviors in online shopping malls. To achieve the aim of the study, online grocery purchase records from consumer household panels was analyzed. The results show that using a mobile device significantly influences consumers purchasing behavior. Essay 2 discusses the effect of touching on a product through a screen (vs. clicking on a product) on consumers in online shopping malls. The experiments were conducted with 107 participants. The results indicate that touch screens positively affect affective thinking style, mental simulation of a product, shopping enjoyment, and price premium. In addition, the main paths that affect the price premium differ when using a touch screen rather than a mouse.터치 인터페이스 기반인 모바일 기기를 사용하여 온라인 쇼핑을 하는 사람들이 급속도로 증가하고 있다. 본 연구는 온라인에서 식료품을 구입할 때 PC와 비교하여 모바일 기기의 사용이 미치는 영향에 초점을 맞추었다. 첫번째 연구에서는 온라인 쇼핑몰에서 모바일 기기 사용과 PC 사용의 차이가 소비자의 식료품 구매패턴에 미치는 영향을 확인하였다. 연구의 목적을 달성하기 위해 소비자 패널들의 온라인 식료품 구매 지출내역을 분석하였다. 분석결과, 사용하는 기기의 차이에 따라 온라인 쇼핑몰에서 소비자의 구매 행동이 달라진다는 것을 보여준다. 두번째 연구에서는 온라인 쇼핑몰에서 화면을 통해 제품을 터치하는 것이 마우스를 사용할 때와 비교하여 소비자 구매행동에 미치는 영향을 조사하였다. 본 연구의 목적을 위해 107명의 참가자들을 대상으로 실험을 진행했다. 그 결과, 스크린을 통한 제품의 터치는 사고 방식, 제품에 대한 정신적 시뮬레이션, 쇼핑에 대한 즐거움, 가격 프리미엄에 유의미한 영향을 미쳤다. 또한, 사용하는 인터페이스의 차이(터치 vs. 클릭)에 따라가격 프리미엄에 영향을 미치는 주요 경로가 다르다는 것을 보여준다.Essay 1: Difference in Online Grocery Purchasing Behaviors when Using Mobile Devices and PCs 1. Introduction · · 1 1.1 Research Background · · 1 1.2 Research Objectives · · 4 2. Literature Review · · 5 2.1 Features of Mobile Commerce · · 5 2.2 Difference in Devices (PC vs. Mobile) · · 7 3. Research Model and Hypotheses · · 11 3.1 Research Model · · 11 3.2 Hypotheses Development · · 11 4. Methodology · · 14 4.1 Data Collection · · 14 4.2 Operationalization of Smartphone Group (vs. PC group) · · 15 4.3 Operationalization of Purchasing Behavior · · 17 5. Data Analysis and Results · · 20 5.1 Sample Characteristics · · 20 5.2 Descriptive Statistics of Major Variables · · 22 5.3 Correlation Analysis · · 23 5.4 Hypothesis Test · · 25 6. Discussion · · 32 6.1 Summary of Findings · · 32 6.2 Contribution and Limitation · · 34 Essay 2: The Effect of Product Image Touch on Consumers Grocery Purchasing Behavior 1. Introduction · · 39 1.1 Research Background · · 39 1.2 Research Objectives · · 41 2. Theoretical Background · · 42 2.1 Thinking Style · · 42 2.2 Embodied Cognition Theory · · 45 3. Research model and Hypotheses · · 48 4. Methodology · · 53 4.1 Stimulus Material & Measurements Development · · 53 4.2 Procedure of Experiment · · 56 5. Data Analysis and Results · · 58 5.1 Data Collection · · 58 5.2 Demographic Information · · 58 5.3 Descriptive Statistics of Major Variables · · 60 5.4 Assessment of Measurement Model · · 61 5.5 Hypothesis Test · · 63 6. Discussion · · 68 6.1 Summary of Findings · · 68 6.2 Contribution and Limitation · · 70 Reference · · 74 Appendix A. Survey of Essay 1 · · 85 Appendix B. Stimulus of Essay 2 · · 86 Appendix C. Survey of Essay 2 · · 92 Abstract in Korean · · 98Maste