UNSUPERVISED GROUPING OF MOVING OBJECTS BASED ON AGGLOMERATIVE HIERARCHICAL CLUSTERING

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering , Engineering, Electrical & Electronic

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VOLUME 9 , ISSUE 4 (December 2016) > List of articles

UNSUPERVISED GROUPING OF MOVING OBJECTS BASED ON AGGLOMERATIVE HIERARCHICAL CLUSTERING

Kaori Fujinami *

Keywords : Smart Objects, Agglomerative Hierarchical Clustering, Grouping, Accelerometer.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 9, Issue 4, Pages 2,276-2,296, DOI: https://doi.org/10.21307/ijssis-2017-964

License : (CC BY-NC-ND 4.0)

Received Date : 17-August-2016 / Accepted: 30-October-2016 / Published Online: 01-December-2016

ARTICLE

ABSTRACT

In this article, we present a method to identify a grouping of sensor nodes that show similar
movement patterns in an ad-hoc manner. The motivation behind the ad-hoc grouping is to allow a
system to monitor complex and concrete situations of people and/or devices such as “who is/are
utilizing what object(s)” and “what objects are carried together” without any supervision of human
before and at the time of interaction. An agglomerative hierarchical clustering algorithm was applied to
a data stream to find the group members as a set of clusters within a certain height. A threshold was
also determined in an unsupervised way based on simple statistics obtained from the previous clustering
results. An off-line analysis was conducted on data collected in realistic situations. Although grouping
two of the same but unrelated activities proved to be difficult, the proposed algorithm performed well in
other relaxed cases such as walking with a bag vs. pushing a platform hand truck. Furthermore, we
confirmed the effectiveness of clustering-based grouping in comparison with simple distance-based
grouping.

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REFERENCES

[1] M.R.Addlesee, S. Hodges, J. Newman, P. Steggles, and A. Ward, “Implementing a
Sentient Computing System”, IEEE Computer, 34 (8), (2001), 50-56.
[2] M. Beigl, H.W. Gellersen, and A.Schmidt, “MediaCups: Experience with Design and Use
of Computer-Augmented Everyday Objects”, Computer Networks, 35 (4), (2001), 401-409.
[3] D.Bichler, G.Stromberg, M.Huemer, and M.Löw, “Key generation based on acceleration
data of shaking processes”, Proceedings of the 9th International Conference on Ubiquitous
Computing (Ubicomp2007), pp. 304-317, 2007.
[4] S.Bosch, R.Marin-Merianu, P.Havinga, A.Horst, M.Marin-Perianu, and A.Vasilescu, “A
study on automatic recognition of object use exploiting motion correlation of wireless sensors”,
Personal and Ubiquitous Computing, 16 (7), (2012), pp. 875-895.
[5] W.Brunette, C.Hartung, B.Nordstrom, and G.Borriello. “Proximity Interactions between
Wireless Sensors and their Application”, Proceedings of the Second ACM International
Workshop on Wireless Sensor Networks and Applications (WSNA 2003), pp. 30-37, 2003.
[6] J.T. Bryant, H.W. Wevers, and P.J. Lowe, “Methods of data smoothing for instantaneous
centre of rotation measurements”, Medical and Biological Engineering and Computing, 22(6),
(1984), pp.597-602.
[7] A. Dey, “Providing Architectural Support for Building Context-Aware Applications”,
PhD Thesis. Georgia Institute of Technology, 2000.
[8] R.O.Duda, P.E.Hart and D.G.Stork, “Pattern Classification - Second Edition –“, John
Wiley & Sons, 2001.
[9] C.Efstratiou, N.Davies, G.Kortuem, J.Finney, R.Hooper, and M.Lowton, “Experiences of
designing and deploying intelligent sensor nodes to monitor hand-arm vibrations in the field”,
Proceedings of the 5th International Conference on Mobile Systems, Applications and Services
(MobiSys’07), pp. 127-138, 2007.
[10] K. Fujinami and T. Nakajima, “Sentient Artefact: Acquiring User’s Context Through
Daily Objects”, Proceedings of the 2nd International Symposium on Ubiquitous Intelligence and
Smart Worlds (UISW2005), pp. 335-344, 2005.
[11] K.Fujinami and S. Pirttikangas, “Kuka: An Architecture for Associating an Augmented
Artefact with its User using Wearable Sensors”, Proceedings of IEEE International Conference
on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC2008), pp.154-161, 2008.
[12] K.Fujinami and S.Pirttikangas, “A Study on a Correlation Coefficient to associate an
Object with its User”, Proceedings of the 3rd IET International Conference on Intelligent
Environment (IE07), pp. 288-295, 2007.
[13] L.E. Holmquist, F.Mattern, B.Schiele, P.Alahuhta, M.Beigl, and H.-W. Gellersen,
“Smart-Its Friends: A Technique for Users to Easily Establish Connections between Smart
Artefacts”, Lecture Notes in Computer Science 2201 (Ubicomp2001), pp. 116-22, 2001.
[14] K.Kunze, P.Lukowicz, H.Junker, and G.Tröster, “Where am I: Recognizing On-body
Positions of Wearable Sensors”, Proceedings of International Workshop on Location and
Context-Awareness (LoCA 2005), LNCS 3479, pp. 264-2275, 2005.
[15] J.Lester, B.Hannaford, and G.Borriello, “Are You With Me? - Using Accelerometers to
Determine if Two Devices are Carried by the Same Person”, Proceedings of International
Conference on Pervasive Computing (Pervasive 2004), pp. 33-50, 2004.
[16] R.Marin-Perianu, C.Lombriser, P.J.M. Havinga, H.Scholten, and G.Tröster, “Tandem: A
context-aware method for spontaneous clustering of dynamic wireless sensor nodes”,
Proceedings of the 1st International Conference on the Internet of Things (IOT2008), pp. 341-
359, 2008.
[17] R.Marin-Perianu, M.Marin-Perianu, P.J.M. Havinga, and H.Scholten, “Movement-based
group awareness with wireless sensor networks”, Proceedings of the 5th International Conference
on Pervasive Computing (Pervasive2007), pp. 298-315, 2007.
[18] R.Mayrhofer and H.Gellersen, “Shake well before use: Intuitive and secure pairing of
mobile devices”, IEEE Transactions on Mobile Computing, 8(6), (2009), pp.792-806.
[19] M.Philipose, K.P. Fishkin, M.Perkowitz, D.J. Patterson, D.Fox, H.Kautz, and D.Hähnel,
“Inferring Activities from Interactions with Objects”, IEEE Pervasive Computing, 3:50-57, 2004.
[20] A.R.Rocha, L.Pirmez, F.C.Delicato, E.Lemos, I.Santos, D.G.Gomes, and J.N.Souza,
“WSNs clustering based on semantic neighborhood relationships”, Computer Networks, 56(5),
2012, pp. 1627-1645.
[21] D.Roggen, N.B. Bharatula, M.Stäger, P.Lukowicz, and G.Tröster, “From Sensors to
Miniature Networked SensorButtons”, Proceedings of the 3rd International Conference on
Networked Sensing Systems (INSS’06), pp. 119-122, 2006.
[22] H. Scholten and P. Bakker, “Opportunistic sensing in train safety systems”, International
Journal on Advances in Networks and Services, Vol. 4, No. 3-4, (2011), pp. 353-362.
[23] Sun SPOT World, http://sunspotdev.org/ (accessed: May 2016)
[24] H.Yüzugüzel, J. Niemi, S. Kiranyaz, and M. Gabbouj, “ShakeMe: Key Generation from
Shared Motion”, Proceedings of the 2015 IEEE International Conference on Computer and
Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic
and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), pp.
2130-2133, 2015.
[25] J.H. Ward, “Hierarchical groupings to optimize an objective function”, Journal of the
American Statistical Association, 58(301), pp. 236-244, 1963.

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