Multi-view onboard clustering of skeleton data for fall risk discovery

Daisuke Takayama, Yutaka Deguchi, Shigeru Takano, Vasile Marian Scuturici, Jean Marc Petit, Einoshin Suzuki

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


We propose a multi-view onboard clustering of skeleton data for fall risk discovery. Clustering by an autonomous mobile robot opens the possibility for monitoring older adults from the most appropriate positions, respecting their privacies1, and adapting to various changes. Since the data that the robot observes is a data stream and communication network can be unreliable, the clustering method in this case should be onboard. Motivated by the rapid increase of older adults in number and the severe outcomes of their falls, we adopt Kinect equipped robots and focus on gait skeleton analysis for fall risk discovery. Our key contributions are new between-skeleton distance measures for risk discovery and two series of experiments with our onboard clustering. The experiments revealed several key findings for the method and the application as well as interesting outcomes such as clusters which consist of unexpected risky postures.

Original languageEnglish
Pages (from-to)258-273
Number of pages16
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication statusPublished - Jan 1 2014

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Multi-view onboard clustering of skeleton data for fall risk discovery'. Together they form a unique fingerprint.

Cite this