Systematic Evaluation of Social Behaviour Modelling - UbiComp

as the test data. We performed 10-fold cross-validationten times on random permutations of the sequences andthe classification performance is summarised in Table 2.speaking was most detected most robustly while gesturing

gesture step drinkPrecision 0.59 1.00 1.00Recall 0.24 0.21 0.21F1 0.34 0.35 0.35

laugh speakPrecision 1.00 0.64Recall 0.38 0.82F1 0.56 0.72

Table 2: Average precision,recall and F-measure for thedifferent action categories in ourdataset over 10 repetitions of10-fold cross validation.

was the most difficult to detect. The remaining behaviourswere detected with a very high precision but low recall.

We would like to exploit these behaviours to detect who isspeaking with whom. Social scientists have found thatpeople talking together have certain distinctivesynchronous behaviour [5]. Preliminary analysis of ourdata showed that gesturing and stepping occurssynchronously more frequently, and speakingsimultaneously occurs less often for people in the samegroup compared to different groups.

Conclusion and Future WorkWe have presented systematic evaluatons of theestimation of social attributes and behaviour fromacclerometer data recorded during crowded socialgatherings. Future work will be focused on understandinghow fusing information from the social attributes andactions could help to improve the estimation performance.

AcknowledgementsWe thank researchers at the VU University of Amsterdam(Matthew Dobson, Claudio Martella, and Maarten vanSteen) for the use of their wearable sensors and helpduring the data collection, and the University ofAmsterdam (Jeroen Kools and Ben Krose) for their datacollection and annotation help.

References[1] Castellano, G., Kessous, L., and Caridakis, G.

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[2] Cattuto, C., Van den Broeck, W., Barrat, A.,Colizza, V., Pinton, J., and Vespignani, A. Dynamicsof Person-to-Person Interactions from DistributedRFID Sensor Networks. PLOS ONE 5, 7 (07 2010).

[3] Englebienne, G., and Hung, H. Mining formotivation: using a single wearable accelerometer todetect people’s interests. In ACM MM Workshops,ACM (2012).

[4] Hung, H., Englebienne, G., and Kools, J. ClassifyingSocial Actions with a Single Accelerometer. InUbicomp (2013).

[5] Kendon, A. Conducting Interaction: Patterns ofBehavior in Focused Encounters. CambridgeUniversity Press, 1990.

[6] Kim, T., McFee, E., Olguin, D. O., Waber, B., andPentland, A. S. Sociometric badges: Using sensortechnology to capture new forms of collaboration.Journal of Organizational Behavior (2012).

[7] Laibowitz, M., and Paradiso, J. The UbER-Badge, aversatile platform at the juncture between wearableand social computing. Advances in PervasiveComputing (2004).

[8] Olguin, D. O., Waber, B. N., Kim, T., Mohan, A.,Ara, K., and Pentland, A. Sensible Organizations:Technology and Methodology for AutomaticallyMeasuring Organizational Behavior. IEEETransactions on Systems, Man, and Cybernetics,Part B (2009).

[9] Pantic, M., Pentland, A., Nijholt, A., and Huang, T.Human computing and machine understanding ofhuman behavior: a survey. Artifical Intelligence forHuman Computing (2007).

[10] Wyatt, D., Choudhury, T., Bilmes, J., and Kitts,J. A. Inferring colocation and conversation networksfrom privacy-sensitive audio with implications forcomputational social science. ACM Trans. Intell.Syst. Technol. 2, 1 (Jan. 2011).

Session: Poster, Demo, & Video Presentations UbiComp’13, September 8–12, 2013, Zurich, Switzerland

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