Lesson Learned from Collecting Quantified Self Information via Mobile and Wearable Devices

Journal of Sensor and Actuator Networks

Reza Rawassizadeh¹  Elaheh Momeni³  Chelsea Dobbins²  Pejman Mirza-Babaei⁵  Ramin Rahnamoun⁶  Yang Liu⁴ 

[1] Department of Computer Science and Engineering, University of California Riverside, Riverside 92521, CA, USA;Department of Computer Science, Liverpool John Moores University, Liverpool L33AF, UK; E-Mail:;Faculty of Computer Science, University of Vienna, Vienna A-1090, Austria; E-Mail:;id="af1-jsan-04-00315">1Department of Computer Science and Engineering, University of California Riverside, Riverside 92521, CA, U;Institute of Technology, University of Ontario, Oshawa L1H7K4, ON, Canada; E-Mail:;Islamic Azad University Central Tehran Branch, Tehran 1469669191, Iran; E-Mail:

关键词: lifelogging;    Quantified Self;    user experiment;    smartwatch;    smartphone;    data collection;   

DOI  :  10.3390/jsan4040315

来源: mdpi

【 摘 要 】

The ubiquity and affordability of mobile and wearable devices has enabled us to continually and digitally record our daily life activities. Consequently, we are seeing the growth of data collection experiments in several scientific disciplines. Although these have yielded promising results, mobile and wearable data collection experiments are often restricted to a specific configuration that has been designed for a unique study goal. These approaches do not address all the real-world challenges of “continuous data collection” systems. As a result, there have been few discussions or reports about such issues that are faced when “implementing these platforms” in a practical situation. To address this, we have summarized our technical and user-centric findings from three lifelogging and Quantified Self data collection studies, which we have conducted in real-world settings, for both smartphones and smartwatches. In addition to (i) privacy and (ii) battery related issues; based on our findings we recommend further works to consider (iii) implementing multivariate reflection of the data; (iv) resolving the uncertainty and data loss; and (v) consider to minimize the manual intervention required by users. These findings have provided insights that can be used as a guideline for further Quantified Self or lifelogging studies.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.