Remote detection of social interactions in indoor environments through bluetooth low energy beacons

Abstract

The way people interact in daily life is a challenging phenomenon to be captured and studied without altering the natural rhythm of the interactions. We investigate the development of automated tools that may provide information to the researchers that analyse interactions among humans. One important requirement of these tools is that should not interfere with the subjects under observation, in order to avoid any alteration in the subject’s normal behaviour. Our approach is based on the detection of proximity among groups of people that is obtained using commercial wearable wireless tags based on Bluetooth Low Energy (BLE) and a novel algorithm called Remote Detection of Human Proximity (ReD-HuP) that analyses the wireless signal of tags and produce the proximity information. The algorithm, which has been validated against the ground truth of an experimental dataset, achieves an accuracy of 95.91% and an F-Score of 95.79%.

Keywords

Proximity bluetooth low energy social interactions

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References

Bacciu,

Chessa,

Ferro,

Fortunati,

Gallicchio,

La Rosa,

Llorente,

Micheli,

Palumbo,

Parodi et al., Detecting socialization events in ageing people: The experience of the DOREMI project, in: 2016 12th International Conference on Intelligent Environments (IE), IEEE, 2016, pp. 132–135. doi:10.1109/IE.2016.28.

Bacciu,

Gallicchio,

Micheli,

Chessa and

Barsocchi, Predicting user movements in heterogeneous indoor environments by reservoir computing, in: Proc. of the IJCAI Workshop on Space, Time and Ambient Intelligence (STAMI), Barcellona, Spain, 2011, pp. 1–6.

Baronti,

Barsocchi,

Chessa,

Mavilia and

Palumbo, Indoor bluetooth low energy dataset for localization, tracking, occupancy, and social interaction, Sensors 18(12) (2018), 4462. doi:10.3390/s18124462.

Barsocchi,

Chessa,

Ferro,

Furfari and

Potorti, Context driven enhancement of RSS-based localization systems, in: 2011 IEEE Symposium on Computers and Communications (ISCC), IEEE, 2011, pp. 463–468. doi:10.1109/ISCC.2011.5983880.

Barsocchi,

Crivello,

Girolami,

Mavilia and

Ferro, Are you in or out? Monitoring the human behavior through an occupancy strategy, in: MoCS, 2016, ISSN 15301346. ISBN 9781509006793. doi:10.1109/ISCC.2016.7543732.

Barsocchi,

Crivello,

Girolami,

Mavilia and

Palumbo, Occupancy detection by multi-power bluetooth low energy beaconing, in: Indoor Positioning and Indoor Navigation (IPIN), 2017 International Conference on, IEEE, 2017, pp. 1–6.

Barsocchi,

Crivello,

La Rosa and

Palumbo, A multisource and multivariate dataset for indoor localization methods based on WLAN and geo-magnetic field fingerprinting, in: 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), IEEE, 2016, pp. 1–8.

Barsocchi,

Ferro,

Fortunati,

Mavilia and

Palumbo, EMS@CNR: An energy monitoring sensor network infrastructure for in-building location-based services, in: 2014 International Conference on High Performance Computing & Simulation (HPCS), IEEE, 2014, pp. 857–862. doi:10.1109/HPCSim.2014.6903779.

Barsocchi,

Furfari,

Nepa and

Potortì, RSSI localisation with sensors placed on the user, in: 2010 International Conference on Indoor Positioning and Indoor Navigation, IEEE, 2010, pp. 1–6.

10.

Bazzani,

Cristani and

Murino, Decentralized particle filter for joint individual-group tracking, in: 2012 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2012, pp. 1886–1893. doi:10.1109/CVPR.2012.6247888.

11.

Bazzani,

Cristani,

Tosato,

Farenzena,

Paggetti,

Menegaz and

Murino, Social interactions by visual focus of attention in a three-dimensional environment, Expert Systems 30(2) (2013), 115–127. doi:10.1111/j.1468-0394.2012.00622.x.

12.

Belmonte-Fernandez,

Gasco-Compte,

Sansano-Sansano,

Quinde,

Manuel,

Gines and

J.C.

Augusto, Evaluation of crowdsourcing wi-fi radio map creation in a real scenario for AAL applications, in: 2019 15th International Conference on Intelligent Environments (IE), IEEE, 2019.

13.

Ó.

Belmonte-Fernández,

Puertas-Cabedo,

Torres-Sospedra,

Montoliu-Colás and

Trilles-Oliver, An indoor positioning system based on wearables for ambient-assisted living, Sensors 17(1) (2017), 36.

14.

Cassará,

Potortí,

Barsocchi and

Girolami, Choosing an RSS device-free localization algorithm for ambient assisted living, in: 2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2015, pp. 1–8. doi:10.1109/IPIN.2015.7346788.

15.

Crivello,

Mavilia,

Barsocchi,

Ferro and

Palumbo, Detecting occupancy and social interaction via energy and environmental monitoring, International Journal of Sensor Networks 27(1) (2018), 61–69. doi:10.1504/IJSNET.2018.092136.

16.

Efstratiou,

Leontiadis,

Picone,

K.K.

Rachuri,

Mascolo and

Crowcroft, Sense and sensibility in a pervasive world, in: International Conference on Pervasive Computing, Springer, 2012, pp. 406–424. doi:10.1007/978-3-642-31205-2_25.

17.

Gallicchio,

Micheli,

Barsocchi and

Chessa, User movements forecasting by reservoir computing using signal streams produced by mote-class sensors, in: International Conference on Mobile Lightweight Wireless Systems, Springer, 2011, pp. 151–168.

18.

Girolami,

Mavilia,

Delmastro and

Distefano, Detecting social interactions through commercial mobile devices, in: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2018, pp. 125–130. doi:10.1109/PERCOMW.2018.8480397.

19.

Granovetter, The strength of weak ties: A network theory revisited, Sociological Theory 1 (1983), 201–233, http://www.jstor.org/stable/202051 . doi:10.2307/202051.

20.

Gupte and

Eliassi-Rad, Measuring tie strength in implicit social networks, in: Proceedings of the 3rd Annual ACM Web Science Conference on – WebSci ’12, 2012.

21.

E.T.

Hall, The Hidden Dimension / Edward T. Hall, 1st edn, Doubleday, Garden City, N.Y., 1966, p. xii, 201 p., [6] leaves of plates.

22.

Hung and

Kröse, Detecting f-formations as dominant sets, in: Proceedings of the 13th International Conference on Multimodal Interfaces, ACM, 2011, pp. 231–238.

23.

Kim,

McFee,

D.O.

Olguin,

Waber and

Pentland, Sociometric badges: Using sensor technology to capture new forms of collaboration, Journal of Organizational Behavior 33(3) (2012), 412–427. doi:10.1002/job.1776.

24.

Lederman,

Mohan,

Calacci and

A.S.

Pentland, Rhythm: A unified measurement platform for human organizations, IEEE Multimedia 25(1) (2018), 26–38. doi:10.1109/MMUL.2018.112135958.

25.

Martınez,

Dimitriadis,

Rubia,

Gómez and

De La Fuente, Combining qualitative evaluation and social network analysis for the study of classroom social interactions, Computers & Education 41(4) (2003), 353–368. doi:10.1016/j.compedu.2003.06.001.

26.

Mastrandrea,

Fournet and

Barrat, Contact patterns in a high school: A comparison between data collected using wearable sensors, contact diaries and friendship surveys, PLoS ONE 10(9) (2015), 1–26. ISSN 1932-6203 (Electronic)∖r1932-6203 (Linking). doi:10.1371/journal.pone.0136497.

27.

Matic,

Osmani,

Maxhuni and

Mayora, Multi-modal mobile sensing of social interactions, in: 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops, IEEE, 2012, pp. 105–114.

28.

Mavilia,

Palumbo,

Barsocchi,

Chessa and

Girolami, Remote detection of indoor human proximity using bluetooth low energy beacons, in: 2019 15th International Conference on Intelligent Environments (IE), IEEE, 2019.

29.

Montanari,

Nawaz,

Mascolo and

Sailer, A study of bluetooth low energy performance for human proximity detection in the workplace, in: Pervasive Computing and Communications (PerCom), 2017 IEEE International Conference on, IEEE, 2017, pp. 90–99. doi:10.1109/PERCOM.2017.7917855.

30.

Neburka,

Tlamsa,

Benes,

Polak,

Kaller,

Bolecek,

Sebesta and

Kratochvil, Study of the performance of RSSI based bluetooth smart indoor positioning, in: 2016 26th International Conference Radioelektronika (RADIOELEKTRONIKA), IEEE, 2016, pp. 121–125. doi:10.1109/RADIOELEK.2016.7477344.

31.

J.-P.

Onnela,

B.N.

Waber,

Pentland,

Schnorf and

Lazer, Using sociometers to quantify social interaction patterns, Scientific Reports 4 (2014), 5604 EP -.

32.

Palaghias,

S.A.

Hoseinitabatabaei,

Nati,

Gluhak and

Moessner, Accurate detection of real-world social interactions with smartphones, in: 2015 IEEE International Conference on Communications (ICC), IEEE, 2015, pp. 579–585. doi:10.1109/ICC.2015.7248384.

33.

Palumbo and

Barsocchi, Salt: Source-agnostic localization technique based on context data from binary sensor networks, in: European Conference on Ambient Intelligence, Springer, 2014, pp. 17–32. doi:10.1007/978-3-319-14112-1_2.

34.

Palumbo,

Barsocchi,

Chessa and

J.C.

Augusto, A stigmergic approach to indoor localization using bluetooth low energy beacons, in: 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, 2015, pp. 1–6.

35.

Potortì,

Cassarà and

Palumbo, Robust device-free localisation with few anchors, in: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, ACM, 2018, pp. 1184–1189.

36.

Potortì,

Corucci,

Nepa,

Furfari,

Barsocchi and

Buffi, Accuracy limits of in-room localisation using RSSI, in: 2009 IEEE Antennas and Propagation Society International Symposium, IEEE, 2009, pp. 1–4.

37.

Potortì and

Palumbo, CEO: A context event only indoor localization technique for AAL, Journal of Ambient Intelligence and Smart Environments 7(6) (2015), 745–760. doi:10.3233/AIS-150343.

38.

Sapiezynski,

Stopczynski,

D.K.

Wind,

Leskovec and

Lehmann, Inferring person-to-person proximity using WiFi signals, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1(2) (2017), 24. doi:10.1145/3090089.

39.

Sekara and

Lehmann, The strength of friendship ties in proximity sensor data, PLoS ONE 9(7) (2014), 1–14. doi:10.1371/journal.pone.0100915.

40.

Sora and

J.C.

Augusto, Managing multi-user smart environments through BLE based system, in: Intelligent Environments 2018: Workshop Proceedings of the 14th International Conference on Intelligent Environments, Vol. 23,

Y.T.

Ioannis Chatzigiannakis and

O.A.

Paulo Novais, eds, IOS Press, Amsterdam, The Netherlands, 2018, pp. 234–243.

41.

Stehlé,

Voirin,

Barrat,

Cattuto,

Isella,

J.F.

Pinton,

Quaggiotto,

van den Broeck,

Régis,

Lina and

Vanhems, High-resolution measurements of face-to-face contact patterns in a primary school, PLoS ONE 6(8) (2011). ISSN 1932-6203 (Electronic)∖r1932-6203 (Linking). doi:10.1371/journal.pone.0023176.

42.

Stopczynski,

Sekara,

Sapiezynski,

Cuttone,

M.M.

Madsen,

J.E.

Larsen and

Lehmann, Measuring large-scale social networks with high resolution, PLoS ONE 9(4) (2014). ISSN 1932-6203. doi:10.1371/journal.pone.0095978.

43.

Torres-Sospedra,

Montoliu,

Trilles,

Ó.

Belmonte and

Huerta, Comprehensive analysis of distance and similarity measures for wi-fi fingerprinting indoor positioning systems, Expert Systems with Applications 42(23) (2015), 9263–9278. doi:10.1016/j.eswa.2015.08.013.

44.

Zhang,

Du,

Zhou,

Li and

Mohapatra, DopEnc: Acoustic-based encounter profiling using smartphones, in: Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking, ACM, 2016, pp. 294–307.