Yunlong Wang

Yunlong Wang

Research Scientist
IHPC, A*STAR

Posts by Collection

portfolio

projects

Mobile Augmented Reality (AR) Framework

This is an engineering project, which aimed to create a AR framework for mobile devices. We created several applications based on this framework, e.g., an AR demonstration system for house decoration and an AR Newspaper of Shaanxi Daily.

HuddleLamp

This is an open-source research project, which aimed to enable cross-device interaction. I was not the main contributor in this project, but only added a feature to it. Please go to its homepage for more information.

SmartAct

This is an interdisciplinary research project, which funded by the German Federal Ministry of Education and Research (BMBF). It involves research groups from the University of Konstanz, the University of Mannheim and the Karlsruhe Institute of Technology (KIT). The aim of this project is to improve the long-term health behavior (e.g., food intake and physical activity) by using mobile technologies.

Mobile Application for Sedentary Behavior Change

This is an research project, which aimed to study the effect of visualizing sedentary behavior patters using smartphone on sedentary behavior change. We will provide the mobile application to the public in the near future.

PC Reminders for Sedentary Behavior Change

This is an research project, which aimed to study the effect of two kinds of PC reminders on sedentary behavior change. We will provide the application to the public in the near future.

publications

Lightweight Visual Data Analysis on Mobile Devices -Providing Self-Monitoring Feedback

Published in Adjunct Proceedings of the 2016 International Working Conference on Advanced Visual Interfaces (AVI 2016), 2016

The paper presents the SmartAct toolbox for health behavior change. This toolbox is a set of tools for personal mobile technology which decreases the implementation barrier for mHealth interventions. It consists of tools for physical activity tracking, food journaling, questionnaires, notifications, feedback and interventions, workflow management, data storage, and client-server synchronization.

Recommended citation: Simon Butscher, Yunlong Wang, Jens Mueller, Katrin Ziesemer, Karoline Villinger, Deborah Wahl, Laura Koenig, Gudrun Sproesser, Britta Renner, Harald T Schupp, Harald Reiterer: Lightweight Visual Data Analysis on Mobile Devices -Providing Self-Monitoring Feedback. In Adjunct with the 2016 International Working Conference on Advanced Visual Interfaces (AVI 2016). 2016.

“Fingerprints” : Detecting Meaningful Moments for Mobile Health Intervention

Published in Adjunct Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI 2016), 2016

This paper presents the “fingerprints” framework to collect and analyze the users’ daily behavior patterns, which is designed for analysing the meaningful moments to support mHealth interventions.

Recommended citation: Yunlong Wang, Le Duan, Simon Butscher, Jens Mueller, Harald Reiterer: "Fingerprints" : Detecting Meaningful Moments for Mobile Health Intervention. In Adjunct Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI 2016 Adjunct). ACM, 2016. DOI:10.1145/2957265.2965006.

Supporting Self-Assembly: The IKEA Effect on Mobile Health Persuasive Technology

Published in Proceedings of the 2016 ACM Workshop on Multimedia for Personal Health and Health Care (MMHealth 2016), 2016

This paper proposes a study design of examining the effect of self-assembly (IKEA Effect) on the success of mobile health persuasive technology.

Recommended citation: Yunlong Wang, Ulrike Pfeil, Harald Reiterer: Supporting Self-Assembly: The IKEA Effect on Mobile Health Persuasive Technology. In Proceedings of the 2016 ACM Workshop on Multimedia for Personal Health and Health Care (MMHealth 2016). ACM, 2016. DOI:10.1145/2985766.2985775.

Persuasive technology in reducing prolonged sedentary behavior at work: A systematic review

Published in Smart Health 7–8 (2018) 19–30, 2018

Prolonged sedentary behavior is prevalent among office workers and has been found to be detrimental to health. Preventing and reducing prolonged sedentary behavior require interventions, and persuasive technology is expected to make a contribution in this domain. In this paper, we use the framework of persuasive system design (PSD) principles to investigate the utilization and effectiveness of persuasive technology in intervention studies at reducing sedentary behavior at work. This systematic review reveals that reminders are the most frequently used PSD principle. The analysis on reminders shows that hourly PC reminders alone have no significant effect on reducing sedentary behavior at work, while coupling with education or other informative session seems to be promising. Details of deployed persuasive technology with behavioral theories and user experience evaluation are lacking and expected to be reported explicitly in the future intervention studies.

Recommended citation: Yunlong Wang, Lingdan Wu, Jan-Philipp Lange, Ahmed Fadhil, Harald Reiterer: Persuasive Technology in Reducing Prolonged Sedentary Behavior at Work: A Systematic Review. Smart Health 7-8 (2018) 19-30, Elsevier. 2018. DOI: 10.1016/j.smhl.2018.05.002.

Supporting Action Planning for Sedentary Behavior Change by Visualizing Personal Mobility Patterns on Smartphone

Published in Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth 2018), 2018

This paper presents a interactive visualization tool to show temporal and spatial patterns of personal sedentary and walking behaviour, which could potentially help users to reduce sedentary behaviour and increase daily steps.

Recommended citation: Yunlong Wang, Ahmed Fadhil, and Harald Reiterer: Supporting Action Planning for Sedentary Behavior Change by Visualizing Personal Mobility Patterns on Smartphone. In Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth 2018), pp. 396-401. ACM, 2018. DOI: 10.1145/3240925.3240962.

The Effect of Emojis when interacting with Conversational Interface Assisted Health Coaching System

Published in Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth 2018), 2018

This paper presents an empirical study comparing users feedback when interacting with chatbot applications that use different dialogue styles, i.e., plain text or text with emoji, when asking different health related questions. The analysis found that when participants had to score an interaction with a chatbot that asks personal questions on their mental wellbeing, they rated the interaction with higher scores with respect to enjoyment, attitude and confidence. Differently, participants rated with lower scores a chatbot that uses emojis when asking information on their physical wellbeing compared to a dialogue with plain text.

Recommended citation: Fadhil, Ahmed, Gianluca Schiavo, Yunlong Wang, and Bereket A. Yilma: The Effect of Emojis when interacting with Conversational Interface Assisted Health Coaching System. In Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth 2018), pp. 378-383. ACM, 2018. DOI: 10.1145/3240925.3240965.

Clustering with Temporal Constraints on Spatio-Temporal Data of Human Mobility

Published in Arxiv.org (pre-print), 2018

In this paper, we propose a method to extracting significant places or places of interest (POIs) using individuals’ spatio-temporal data for human mobility analysis. General clustering methods – such as DBSCAN - were often used for detecting POIs from human mobility data. But these methods do not well use the temporal information in human mobility data. In related work, the only applied temporal information is the time interval of consecutive location data for stay-point detection. Considering temporal constraints in human mobility, we propose a POI clustering approach – namely POI clustering with temporal constraints (PC-TC) – to extract POIs from spatio-temporal data of human mobility. Following human mobility nature in modern society, our approach aims to extract both global POIs (e.g., workplace or university) and local POIs (e.g., library, lab, and canteen). Based on two publicly available datasets including 193 individuals, our evaluation results show that PC-TC has advanced features in POI granularity and the potential of sequential POI prediction. We also tested PC-TC in a real-world mobile application: the user study results show high precision of our method for POI extraction in the university environment.

Recommended citation: Yunlong Wang, Bjoern Sommer, Falk Schreiber, and Harald Reiterer: Clustering with Temporal Constraints on Spatio-Temporal Data of Human Mobility. 2018, arXiv:1807.00546.

Comparing Sequential and Temporal Patterns from Human Mobility Data for Next-Place Prediction

Published in Adjunct Proceedings of the 2018 Conference on User Modeling Adaptation and Personalization (UMAP 2018), 2018

In the domain of human behavior prediction, next-place prediction is an active research field. While prior work has applied sequential and temporal patterns for next-place prediction, no work has yet studied the prediction performance of combining sequential with temporal patterns compared to using them separately. In this paper, we address next-place prediction using the sequential and temporal patterns embedded in human mobility data that has been collected using the GPS sensor of smartphones. We test five next-place prediction methods, including single pattern-based methods and hybrid methods that combine temporal and sequential patterns. Instead of only examining average accuracy as in related work, we additionally evaluate the selected methods using incremental-prediction accuracy on two publicly available datasets (the MDC dataset and the StudentLife dataset). Our results suggest that (1) integrating multiple patterns is not necessarily more effective than using single patterns in average prediction accuracy, (2) most of the tested methods can outperform others for a certain time period (either for the prediction of all places or each place individually), and (3) average prediction accuracies of the top-three candidates using sequential patterns are relatively high (up to 0.77 and 0.91 in the median for both datasets). For real-time applications, we recommend applying multiple methods in parallel and choosing the prediction of the best method according to incrementalprediction accuracy. Lastly, we present an expert tool for visualizing the prediction results.

Recommended citation: Yunlong Wang, Corinna Breitinger, Björn Sommer, Falk Schreiber, and Harald Reiterer. "Comparing Sequential and Temporal Patterns from Human Mobility Data for Next-Place Prediction." In Adjunct Proceedings of the 2018 Conference on User Modeling Adaptation and Personalization (UMAP 2018). ACM, 2018. DOI: 10.1145/3213586.3226212.

Integrating Taxonomies Into Theory-Based Digital Health Interventions for Behavior Change: A Holistic Framework

Published in JMIR Research Protocols, 2019

Digital health interventions (DHIs) have been emerging in the last decade. Due to their interdisciplinary nature, DHIs are guided and influenced by theories (eg, behavioral theories, behavior change technologies, and persuasive technology) from different research communities. However, DHIs are always coded using various taxonomies and reported in insufficient perspectives. This inconsistency and incomprehensiveness will cause difficulty in conducting systematic reviews and sharing contributions among communities. Therefore, based on existing related work, we propose a holistic framework that embeds behavioral theories, behavior change technique taxonomy, and persuasive system design principles. Including four development steps, two toolboxes, and one workflow, our framework aims to guide DHI developers to design, evaluate, and report their work in a formative and comprehensive way.

Recommended citation: Yunlong Wang, Ahmed Fadhil, Jan-Philipp Lange, Harald Reiterer: Integrating Taxonomies into Theory-Based Digital Health Interventions for Behavior Change: A Holistic Framework. JMIR Res Protoc 2019;8(1):e8055. DOI:10.2196/resprot.8055.

Health Behavior Change in HCI: Trends, Patterns, and Opportunities

Published in Arxiv.org (pre-print), 2019

Unhealthy lifestyles could cause many chronic diseases, which bring patients and their families much burden. Research has shown the potential of digital technologies for supporting health behavior change to help us prevent these chronic diseases. The HCI community has contributed to the research on health behavior change for more than a decade. In this paper, we aim to explore the research trends and patterns of health behavior change in HCI. Our systematic review showed that physical activity drew much more attention than other behaviors. Most of the participants in the reviewed studies were adults, while children and the elderly were much less addressed. Also, we found there is a lack of standardized approaches to evaluating the user experience of interventions for health behavior change in HCI.

Recommended citation: Yunlong Wang, Ahmed Fadhil, and Harald Reiterer: Health Behavior Change in HCI: Trends, Patterns, and Opportunities. 2019, arXiv:1901.10449

The Point-of-Choice Prompt or the Always-On Progress Bar?: A Pilot Study of Reminders for Prolonged Sedentary Behavior Change

Published in Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, 2019

Prolonged sedentary behavior contributes to many chronic diseases. An appropriate reminder could help screen-based workers to reduce their prolonged sedentary behavior. The fixed-duration point-of-choice prompt has been frequently used in related work. However, this prompting system has several drawbacks. In this paper, we propose the SedentaryBar, a context-aware reminding system using an always-on progress bar to show the duration of a working session, as an alternative to the prompt. The new reminding system uses both users’ keyboard/mouse events on the computer and the state-of-the-art computer vision algorithm with the webcam to detect users’ presence, which makes the system more accurate and intelligent. Our evaluation study compared the SedentaryBar and the prompt using subjective and objective measurements. After using each method for a week respectively, more participants preferred the SedentaryBar. The participants’ perceived interruption and usefulness also suggested the SedentaryBar was more popular during the study. However, the logged data of the participants’ working durations indicated the prompt was more effective in reducing their sedentary behavior.

Recommended citation: Yunlong Wang and Harald Reiterer: The Point-of-Choice Prompt or the Always-On Progress Bar?: A Pilot Study of Reminders for Prolonged Sedentary Behavior Change. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, 2019. DOI:10.1145/3290607.3313050

Promoting the Research of Health Behavior Change in Chinese HCI Community

Published in Adjunct Workshop (HCI in China) of 2019 CHI Conference on Human Factors in Computing Systems, 2019

In this position paper, we first illustrate the research of health behavior change in the HCI community based on our previous systematic review. According to the unique properties of Chinese society, we then discuss both the potential advantages and challenges of conducting health behavior change research in China. Lastly, we briefly introduce the SMARTACT project in Germany to provide a reference for future related research. This paper aims to draw more attention to this research field and promote its development in China.

Recommended citation: Yunlong Wang and Harald Reiterer: Promoting the Research of Health Behavior Change in Chinese HCI Community. In Workshop of 2019 CHI Conference on Human Factors in Computing Systems. 2019.

Assistive Conversational Agent for Health Coaching: A Validation Study

Published in Methods of Information in Medicine, 2019

The study provided a set of dimensions when building a human–conversational agent powered health intervention tool. The results provided interesting insights when using human–conversational agent mediated approach in health coaching systems. The findings revealed that users who were highly engaged were also more adherent to conversational-agent activities. This research made key contributions to the literature on techniques in providing social, yet tailored health coaching support: (1) identifying habitual patterns to understand user preferences; (2) the role of a conversational agent in delivering health promoting micro-activities; (3) building the technology while adhering to individuals’ daily messaging routine; and (4) a socio-technical system that fits with the role of conversational agent as an assistive component.

Recommended citation: Ahmed Fadhil, Yunlong Wang, Harald Reiterer: Assistive Conversational Agent for Health Coaching: A Validation Study. In Methods of Information in Medicine 2019;58(01):009-023. DOI: 10.1055/s-0039-1688757.

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.