Cuttone, Andrea

Jørgensen, Sune Lehmann

Larsen, Jakob Eg

In this paper we propose an interactive visualization technique QS Spiral that aims to capture the periodic properties of quantified self data and let the user explore those recurring patterns. The approach is based on time-series data visualized as a spiral structure. The interactivity includes the possibility of varying the time span and the time frame shown, allowing for different levels of detail and the discoverability of repetitive patterns in the data on multiple scales. We illustrate the capabilities of the visualization technique using two quantified self data sets involving self-tracking of geolocation and physical activity respectively

Online Research Database In Technology

                       General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal   If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.     Downloaded from orbit.dtu.dk on: Dec 20, 2017QS Spiral: Visualizing Periodic Quantified Self DataLarsen, Jakob Eg; Cuttone, Andrea; Jørgensen, Sune LehmannPublished in:Proceedings of CHI 2013 Workshop on Personal Informatics in the Wild: Hacking Habits for Health &amp;HappinessPublication date:2013Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Larsen, J. E., Cuttone, A., & Jørgensen, S. L. (2013). QS Spiral: Visualizing Periodic Quantified Self Data. InProceedings of CHI 2013 Workshop on Personal Informatics in the Wild: Hacking Habits for Health & HappinessQS Spiral: Visualizing PeriodicQuantified Self DataJakob Eg LarsenTech. University of DenmarkDept. of Applied Mathematicsand Computer ScienceMatematiktorvet, Bld 303B2800 Kgs. Lyngby, Denmarkjel@imm.dtu.dkAndrea CuttoneTech. University of DenmarkDept. of Applied Mathematicsand Computer ScienceMatematiktorvet, Bld 303B2800 Kgs. Lyngby, Denmarkandreacuttone@gmail.comSune LehmannTech. University of DenmarkDept. of Applied Mathematicsand Computer ScienceMatematiktorvet, Bld 303B2800 Kgs. Lyngby, Denmarkslj@imm.dtu.dkCopyright is held by the author/owner(s).CHI’13, Apr 27–May 2, 2013, Paris, France.AbstractIn this paper we propose an interactive visualizationtechnique QS Spiral that aims to capture the periodicproperties of quantified self data and let the user explorethose recurring patterns. The approach is based ontime-series data visualized as a spiral structure. Theinteractivity includes the possibility of varying the timespan and the time frame shown, allowing for differentlevels of detail and the discoverability of repetitivepatterns in the data on multiple scales. We illustrate thecapabilities of the visualization technique using twoquantified self data sets involving self-tracking ofgeolocation and physical activity respectively.Author KeywordsQuantified Self, Personal Informatics, Time-Series Data,Data Visualization, Periodic EventsACM Classification KeywordsH.5.m [Information interfaces and presentation (e.g.,HCI)]: Miscellaneous.IntroductionRecently self-tracking has gained increased attention anduptake with the availability of smartphones and low costwearable devices, such as, FitBit1, Basis2, and Zeo3.These devices are gradually lowering the barrier for peopleto engage in self-tracking and lifelogging. As suggested byLi et al.[4], self-reflection is an essential part of personalinformatics. Self-reflection can be facilitated through datavisualization enabling the user to interact with thecollected data to receive feedback on self-tracking and tobe guided in understanding of behavioral patterns.Different approaches to visualization of quantified selfdata has been proposed, ranging from traditionalstatistical charts to abstract art4.Figure 1: The QS Spiralinteractive visualization withcolor coded data points shown ona time spiral in a 7-day view.Figure 2: In this configurationthe time scale of the QS Spiral isshorter and more details areshown in a 24-hour view.An inherent property of lifelog self-tracking data is theperiodic properties of the data with patterns on differenttemporal scales including days, weeks, months, and years.Moreover self-tracking data may potentially becharacterized as multi-dimensional depending on thenumber of signals being tracked. Our motivation is tocreate an interactive visualization that captures thesespecific properties of quantified self data. Thus in thispaper we propose the data visualization approach QSSpiral that aims to capture both the continuity andperiodic properties of quantified self data by introducingvisualization of time-series data on a spiral, enabling theuser to interact with and interpret the data throughsimple touch-based gestures.Related WorkIn visualization of lifelogging and quantified self time seriesdata traditional charts have been the norm, but severalother approaches have been proposed to enable easier andfaster perception and exploration. For instance physical1http://www.fitbit.com2http://www.mybasis.com3http://www.myzeo.com4The work of Laurie Frick is a good example of art based onself-tracking data sets http://www.lauriefrick.com/activity indicated by the growth of a flower on the FitBitwearable device, but also abstract visualizations or avatarshave been proposed. While visualization of time-seriesdata in a spiral structure has been proposed in [3], [5], and[6] visualization of quantified self data is novel. AlthoughSpark[2] is an example where abstract representations ofphysical activity data include configurations with a spiralstructure and concentric circles of data points, thisvisualization is fairly abstract and aimed as ambient art,and does not communicate details nor periodic events.QS SpiralOur proposed visualization technique is shown in Figure 1and 2 and builds on top of a clock-dial metaphor with acircle corresponding to a time span (hour, day, week, oryear). The continuity of the time series is captured by thecontinuous spiral timeline, with the present in the outerring, and the past towards the center. In the presentconfigurations one full circle represents 24 hours or 7 daysas shown, and the spiral is logarithmic with the distancebetween each turn decreasing in geometric progression.This way the outer arcs are thicker, and becomeprogressively thinner as they approach the center, so thatmost space is allocated for information in the outer ring,representing the most recent time frame. The start- andend time of each event are converted to angles and thecorresponding arc is drawn on the spiral. Data points canbe shown using different color coding of the timeline aswell as symbols or even animations. Depending on theamount of information that needs to be embedded pertime frame, the thickness of the timeline can be varied byzooming in or out. Thin lines optimizes the visualizationfor overview and discovery of patterns on longer timeframes whereas thick lines optimizes the visualization fordetails on a shorter time frame. For navigational supportan overlay with time of the day or day of the week isadded on top of the spiral.PrototypeWe have built a prototype of the QS Spiral visualizationfor Android smartphones enabling us to experiment withtouch-based interfaces as the basis for the interactivevisualization. The single tap gesture allows the user tohighlight specific data elements. A pinch gesture allow theuser to zoom into a particular part of the data and swipegestures allow to pan, resulting in a zoomable mapmetaphor. The double-tap gesture switches betweendifferent time views. The combination of these gesturesallow to focus into the details of a specific period, orprovide overview of a larger time frame.Case StudiesFigure 3: A week view showingthe geolocation data capturedover a 4 month duration. A tagcloud of color-coded geolocationsare shown at the top and atimeline of map segments at thebottom. Tapping the mapsegments will highlight thosegeolocations in the spiral.To demonstrate the capabilities of the interactivevisualization we have experimented with different types ofquantified self data sets. Two data sets are used toillustrate the capabilities of visualizing and interactingwith quantified self data, and in particular to discoverperiodic trends inherent in the data.Geolocation Self-Tracking DataIn the first example we use geolocation data capturedwith sensing software on an Android smartphone[1]. Thesoftware continuously acquire and log data from theembedded location sensors (GPS and WiFi). From thelocation data, static locations are extracted according tomovement speed and grouped into logical groups usingthe DBSCAN clustering algorithm. The data wascaptured over a duration of 4 months. A screenshot fromthe interactive visualization is shown in Figure 3. In thisinteractive visualization the display is separated in threeparts with the QS Spiral in the center. On the top is atag cloud showing the most frequently visited places incolors corresponding to the colors shown in the spiral.The bottom of the display contains time series of placeswith map segments showing the places visited. When theuser taps a visited place the corresponding places will behighlighted in the spiral.Physical Activity Self-Tracking DataThe second example data set contains physical activitydata captured using the FitBit step counter for a durationof 12 months. A screenshot from the interactivevisualization is shown in Figure 4. In this interactivevisualization the display is separated in two parts with thespiral in the center showing the physical activity levelmeasured in number of steps color coded with colorintensity corresponding to the number of steps taken inthat time frame, as indicated by the legend shown at thetop.DiscussionThe interactive visualization facilitates exploratory dataanalysis and helps identify periodic patterns. In both caseswe found that that periodic events and the daily/weeklyrhythms were discoverable using the QS Spiralvisualization, but also that the repetitive nature of thedata makes deviations stand out clearly. The spiralvisualization represents events with similar period alignedalong similar angles of the arcs, resulting in alignedsections of the spiral. Moreover, the color coding ofevents highlights the relation between groups of events.The combination of angle and color allows one to explorepatterns of events ”at a glance”. As examples using theinteractive visualization of geolocation data we discoveredtwo distinct periodic patterns during (9am to 3pm and7pm to 10pm). In addition patterns on Mondays andWednesdays, and a pattern on Tuesdays and Fridays werefound. Events with the same color belonging to the sameregion but with different duration signal an irregularity induration. Events not in the same region as others withthe same color signal an irregularity in a periodicoccurrence, and isolated events are easily discovered too.Also the choice of the scale of period changes theperception of periodicity, where a period of 24 hourshighlights patterns for events happening in the same timeof the day, while a period of 7 days highlights eventshappening in the same day of the week. In the interactivevisualization of physical activity repetitive patterns werefound indicating lower activity levels during weekendscompared to week days. Also higher activity levels werediscovered in the last quarter of the year.Figure 4: A four week viewshowing physical activity datacaptured over a 12 monthduration, with the color densitycorresponding to the number ofsteps taken daily.In the present data sets, the 00:00am–06:00am hour timeperiod is almost empty meaning that a substantial part ofthe visualization area is unutilized. Integration of multipledata sets including geolocation, physical activity, sleep,calendar data and other personal information data sourceswith temporal properties could utilize the full space andallow the user to discover correlations between differentquantified self data streams. To make quantified self datauseful a large volume of data needs to be easily accessible,so that rhythms and progress can be monitored. Aninteractive visualization is essential to engage users byproviding feedback at a glance and the QS Spiral isconsidered a compact representation that fits on a smallaccessible display such as a smartphone display or even awearable/wrist-worn (self-tracking) device. Future workinclude experiments with additional self-tracking data sets(with different resolution) and variations of interfaceconfigurations (such as distortions and animations) and tofurther explore the design language to map touch gesturesto navigation of the interactive visualization.ConclusionsWe have presented QS Spiral as an approach to visualizeand explore quantified self data with the aim for users todiscover patterns and the periodic properties of the data.The visualization has been demonstrated with geolocationand physical activity self-tracking data sets.AcknowledgmentsThis work funded in part by the SensibleDTU project(The Villum Foundation).References[1] Aharony, N., Pan, W., Ip, C., Khayal, I., andPentland, A. Social fmri: Investigating and shapingsocial mechanisms in the real world. Pervasive andMobile Computing (2011).[2] Fan, C., Forlizzi, J., and Dey, A. A spark of activity:Exploring informative art as visualization for physicalactivity. In Proc of the CHI2012 Workshop onPersonal Informatics (2012).[3] Hewagamage, K., Hirakawa, M., and Ichikawa, T.Interactive visualization of spatiotemporal patternsusing spirals on a geographical map. In VisualLanguages, IEEE (1999), 296–303.[4] Li, I., Dey, A., and Forlizzi, J. A stage-based model ofpersonal informatics systems. In Proc of the 28th intconf on Human factors in computing systems, ACM(2010), 557–566.[5] Suntinger, M., Schiefer, J., Obweger, H., and Groller,M. The event tunnel: Interactive visualization ofcomplex event streams for business process patternanalysis. In Visualization Symposium, IEEE (2008),111–118.[6] Weber, M., Alexa, M., and Mu¨ller, W. Visualizingtime-series on spirals. In Proceedings of the IEEESymposium on Information Visualization (2001), 7.

QS Spiral: Visualizing Periodic Quantified Self Data

http://orbit.dtu.dk/ws/files/73859557/chi2013_pi.pdf

QS Spiral: Visualizing Periodic Quantified Self Data

Abstract

Similar works

Full text

Available Versions

Online Research Database In Technology