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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


eISSN: 1178-5608



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


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



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

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