A Clustering Approach to Categorizing 7 Degree-of-Freedom Arm Motions during Activities of Daily Living

2019

Conference Paper

hi

In this paper we present a novel method of categorizing naturalistic human arm motions during activities of daily living using clustering techniques. While many current approaches attempt to define all arm motions using heuristic interpretation, or a combination of several abstract motion primitives, our unsupervised approach generates a hierarchical description of natural human motion with well recognized groups. Reliable recommendation of a subset of motions for task achievement is beneficial to various fields, such as robotic and semi-autonomous prosthetic device applications. The proposed method makes use of well-known techniques such as dynamic time warping (DTW) to obtain a divergence measure between motion segments, DTW barycenter averaging (DBA) to get a motion average, and Ward's distance criterion to build the hierarchical tree. The clusters that emerge summarize the variety of recorded motions into the following general tasks: reach-to-front, transfer-box, drinking from vessel, on-table motion, turning a key or door knob, and reach-to-back pocket. The clustering methodology is justified by comparing against an alternative measure of divergence using Bezier coefficients and K-medoids clustering.

@inproceedings{Gloumakov19-ICRA-Clustering,
  title = {A Clustering Approach to Categorizing 7 Degree-of-Freedom Arm Motions during Activities of Daily Living},
  author = {Gloumakov, Yuri and Spiers, Adam J. and Dollar, Aaron M.},
  booktitle = {Proceedings of the International Conference on Robotics and Automation (ICRA)},
  pages = {7214--7220},
  address = {Montreal, Canada},
  month = may,
  year = {2019},
  doi = {10.1109/ICRA.2019.8794421},
  month_numeric = {5}
}