Lesson learned from collecting quantified self information via mobile and wearable devices

Journal of Sensor and Actuator Networks

Volumn 4, Issue 4, 2015, Pages 315-335

  Rawassizadeh, Reza ^a   Momeni, Elaheh ^b   Dobbins, Chelsea ^c   Mirza Babaei, Pejman ^d   Rahnamoun, Ramin ^e  

^a University of California Riverside   (United States)

^b UNIVERSITY OF VIENNA   (Austria)

^c LIVERPOOL JOHN MOORES UNIVERSITY   (United Kingdom)

^d UNIVERSITY OF ONTARIO INSTITUTE OF TECHNOLOGY   (Canada)

^e ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

Data collection; Lifelogging; Quantified Self; Smartphone; Smartwatch; User experiment

Indexed keywords

EID: 84978724927     PISSN: None     EISSN: 22242708     Source Type: Journal    
DOI: 10.3390/jsan4040315     Document Type: Article

References (51)

1. Dodge, M.; Kitchin, R. Outlines of a world coming into existence: Pervasive computing and the ethics of forgetting. Environ. Plan. 2007, 34, 431-445.
2. Dobbins, C.; Merabti, M.; Fergus, P.; Llewellyn-Jones, D. Augmenting Human Digital Memories with Physiological Data. In Proceedings of the 3rd IEEE International Conference on Network Embedded Systems for Every Application (NESEA), Liverpool, UK, 13-14 December 2012; pp. 35-41.
3. Dobbins, C.; Merabti, M.; Fergus, P.; Llewellyn-Jones, D. Capturing Human Digital Memories for Assisting Memory Recall. In Advances in Physiological Computing; Springer: London, UK, 2014; pp. 211-234.
4. Rawassizadeh, R.; Price, B.A.; Petre, M. Wearables: Has the age of smartwatches finally arrived? Commun. ACM 2015, 58, 45-47.
5. Eagle, N.; Pentland, A. Reality mining: Sensing Complex Social Systems. Pers. Ubiquitous Comput. 2006, 10, 255-268.
6. Aharony, N.; Pan, W.; Ip, C.; Khayal, I.; Pentland, A. Social fMRI: Investigating and Shaping Social Mechanisms in the Real World. Pervasive Mob. Comput. 2011, 7, 643-659.
7. Kiukkonen, N.; Blom, J.; Dousse, O.; Gatica-Perez, D.; Laurila, J. Towards Rich Mobile Phone Datasets: Lausanne Data Collection Campaign. In Proceedings of the International Conference on Pervasive Services, Berlin, Germany, 13-15 July 2010.
8. Wagner, D.T.; Rice, A.; Beresford, A.R. Device analyzer: Largescale Mobile Data Collection. ACM SIGMETRICS Perform. Eval. Rev. 2014, 41, 53-56.
9. Leger, D. Inside Wearables Part 2: A Look at the Uncertain Future of Smart Wearable Devices, and Five Industry Developments that Will Be Necessary for Meaningful Mass Market Adoption and Sustained Engagement; Technical Report; Endeavour Partners: Cambridge, MA, USA, 2014.
10. Whittaker, S.; Kalnikaite, V.; Petrelli, D.; Sellen, A.; Villar, N.; Bergman, O.; Clough P.; Brockmeier, J. Socio-Technical lifelogging: Deriving design principles for a future proof digital past. Hum. Comput. Interact. 2012, 27, 37-62.
11. Hasan, M.; Kashyap, A.; Hristidis, V.; Tsotras, V. User Effort Minimization through Adaptive Diversification. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 24-27 August 2014; pp. 203-212.
12. Wearables are Totally Failing the People Who Need them Most. Available online: http://www.wired.com/2014/11/where-fitness-Trackers-fail (accessed on 3 November 2015).
13. Shih, P.C.; Han, K.; Poole, E.S.; Rosson, M.B.; Carroll, J.M. Use and Adoption Challenges of Wearable Activity Trackers. In Proceedings of the iConference, Newport Beach, CA, USA, 24-27 March 2015.
14. Marcengo, A.; Rapp, A. Visualization of Human Behavior Data: The Quantified Self. In Innovative Approaches of Data Visualization and Visual Analytics; IGI Global: Hershey, PA, USA, 2013; pp. 236-265.
15. Reiss, A.; Stricker, D. Personalized mobile physical activity recognition. In Proceedings of the 2013 International Symposium on Wearable Computers, Zurich, Switzerland, 8-12 September 2013; pp. 25-28.
16. Bao, H.; Wong, W.C. A Novel Map-based Dead-Reckoning Algorithm for Indoor Localization. J. Sens. Actuator Netw. 2014, 3, 44-63.
17. Rawassizadeh, R.; Tomitsch, M.; Wac, K.; Tjoa, A.M. UbiqLog: A generic mobile phone-based life-log framework. Pers. Ubiquitous Comput. 2013, 17, 621-637.
18. Gurrin, C.; Smeaton, A.F.; Doherty, A.R. LifeLogging: Personal big data. Found. Trends Inform. Retr. 2014, 8, 1-125.
19. Chon, Y.; Wanchang, R.; Hojung, C. Predicting smartphone battery usage using cell tower ID monitoring. Pervasive Mob. Comput. 2014, 13, 99-110.
20. Rawassizadeh, R.; Tomitsch, M.; Nourizadeh, M.; Momeni, E.; Peery, A.; Ulanova, L.; Pazzani, M. Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches. Sensors 2015, 15, 22616-22645.
21. Poh, M.Z.; Swenson, N.C.; Picard, R.W. A Wearable Sensor for Unobtrusive, Long-Term Assessment of Electrodermal Activity. IEEE Trans. Biomed. Eng. 2007, 57, 1243-1252.
22. Berlin, E.; Laerhoven, K.V. Detecting Leisure Activities with Dense Motif Discovery. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Pittsburgh, PA, USA, 5-8 September 2012; pp. 250-259.
23. Mankodiya, K.; de la Torre, F.; Zhang, A.; Ryan, N.; Ton, T.G.; Ganhdi, R.; Jain, S. SPARK: Personalized Parkinson Disease Interventions through Synergy between a Smartphone and a Smartwatch. In Design, User Experience, and Usability. User Experience Design for Everyday Life Applications and Services; Springer International Publishing: Crete, Greece, 2014; pp. 103-114.
24. Dong, Y.; Hoover, A.; Scisco, J.; Muth, E. A new method for measuring meal intake in humans via automated wrist motion tracking. Appl. Psychophysiol. Biofeedback 2012, 37, 205-215.
25. Maurer, U.; Rowe, A.; Smailagic, A.; Siewiorek, D.P. eWatch: A wearable sensor and notification platform. In Proceedings of the IEEE Workshop on Wearable and Implantable Body Sensor Networks, Cambridge, MA, USA, 3-5 April 2006.
26. Begum, N.; Keogh, E. Rare Time Series Motif Discovery from Unbounded Streams. Proc. VLDB Endow. 2014, 8, 149-160.
27. Henze, N.; Poppinga, B.; Pielot, M.; Schinke, T.; Boll, S. My app is an experiment: Experience from user studies in mobile app stores. Int. J. Mob. Hum. Comput. Interact. 2011, 3, doi:10.4018/jmhci.2011100105.
28. Ferreira, D.; Kostakos, V.; Dey, A.K. Lessons learned from large-scale user studies: Using android market as a source of data. Int. J. Mob. Hum. Comput. Interact. 2012, 4, 28-43.
29. Blanke, U.; Troster, G.; Franke, T.; Lukowicz, P. Capturing crowd dynamics at large scale events using participatory GPS-localization. In Proceedings of the International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Singapore, 21-24 April 2014; pp. 1-7.
30. Li, I.; Dey, A.K.; Forlizzi, J. Understanding my Data, Myself: Supporting Self-Reflection with Ubicomp Technologies. In Proceedings of the 13th International Conference on Ubiquitous Computing, Beijing, China, 17-21 September 2011; pp. 405-414.
31. Oh, J.; Lee, U. Exploring UX issues in Quantified Self technologies. In Proceedings of the Eighth International Conference on Mobile Computing and Ubiquitous Networking (ICMU), Hakodate, Japan, 20-22 January 2015; pp. 53-59.
32. Choe, E.K.; Lee, N.B.; Lee, B.; Pratt, W.; Kientz, J.A. Understanding Quantified-Selfers Practices in Collecting and Exploring Personal Data. In Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, Toronto, ON, Canada, 26 April-1 May 2014; pp. 1143-1152.
33. Rooksby, J.; Rost, M.; Morrison, A.; Chalmers, M.C. Personal Tracking as Lived Informatics. In Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, Toronto, ON, Canada, 26 April-1 May 2014; pp. 1163-1172.
34. Fritz, T.; Huang, E.M.; Murphy, G.C.; Zimmermann, T. Persuasive technology in the real world: A study of long-Term use of activity sensing devices for fitness. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Toronto, ON, Canada, 26 April-1 May 2014; pp. 487-496.
35. Bargas-Avila, J.; Hornbæk, K. Foci and Blind Spots in User Experience Research. ACM Interact. 2012, 19, 24-27.
36. UbiqLog. Available online: http://github.com/rezar/ubiqlog (accessed on 3 November 2015).
37. Rawassizadeh, R.; Heurix, J.; Khosravipour, S.; Tjoa, A.M. LiDSec-A Lightweight Pseudonymization Approach for Privacy-Preserving Publishing of Textual Personal Information. In Proceedings of the Sixth International Conference on Availability, Reliability and Security, Vienna, Austria, 22-26 August 2011; pp. 603-608.
38. SleepBot. Available online: http://mysleepbot.com (accessed on 3 November 2015).
39. Watson, D.; Lee, A.C.; Auke, T. Development and validation of brief measures of positive and negative affect: The PANAS scales. J. Personal. Soc. Psychol. 1998, 54, 1063-1070.
40. Im Watch smartwatch. Available online: http://www.imsmart.com (accessed on 3 November 2015).
41. Russell, J.A. A Circumplex Model of Affect. J. Personal. Soc. Psychol. 1980, 39, 1161-1178.
42. Paradis, F.; Berrut, C. Experiments with theme extraction in explanatory texts. In Proceedings of the Second International Conference on Conceptions of Library and Information Science, CoLIS, Copenhagen, Denmark, 13-16 October 1996; pp. 433-437.
43. Rawassizadeh, R. Towards sharing life-log information with society. Behav. Inform. Technol. 2012, 31, 1057-1067.
44. Rawassizadeh, R. Mobile application benchmarking based on the resource usage monitoring. Int. J. Mob. Comput. Multimed. Commun. 2009, 1, 64-75.
45. Dey, A.K.; Wac, K.; Ferreira, D.; Tassini, K.; Hong, J.H.; Ramos, J. Getting closer: An empirical investigation of the proximity of user to their smart phones. In Proceedings of the 13th International Conference on Ubiquitous Computing, Beijing, China, 17-21 September 2011; pp. 163-172.
46. Lee, J.M.; Youngwon K.; Gregory, J.W. Validity of Consumer-based Physical Activity Monitors. Med. Sci. Sports Exerc. 2014, 46, 1840-1848.
47. Lane, N.D.; Georgiev, P. Can Deep Learning Revolutionize Mobile Sensing? In Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications, Santa Fe, NM, USA, 12-13 February 2015; pp. 117-122.
48. Moves. Available online: http://movestheapp.com (accessed on 3 November 2015).
49. Swan, M. Sensor mania! The internet of things, wearable computing, objective metrics, and the quantified self 2.0. J. Sens. Actuator Netw. 2012, 1, 217-213.
50. Cordeiro, F.; Epstein, D.A.; Thomaz, E.; Bales, E.; Jagannathan, A.K.; Abowd, G.D.; Fogarty, J. Barriers and Negative Nudges: Exploring Challenges in Food Journaling. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Korea, 18-23 April 2015; pp. 1159-1162.
51. Sleep Monitoring Device. Available online: http://www.wired.com/2014/10/nintendo-sleep-qol-health (accessed on 3 November 2015).