A Clustering Approach to Categorizing 7 Degree-of-Freedom Arm Motions during Activities of Daily Living
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.
| Author(s): | Yuri Gloumakov and Adam J. Spiers and Aaron M. Dollar |
| Book Title: | Proceedings of the International Conference on Robotics and Automation (ICRA) |
| Pages: | 7214--7220 |
| Year: | 2019 |
| Month: | May |
| Bibtex Type: | Conference Paper (inproceedings) |
| Address: | Montreal, Canada |
| DOI: | 10.1109/ICRA.2019.8794421 |
| State: | Published |
| Electronic Archiving: | grant_archive |
BibTex
@inproceedings{Gloumakov19-ICRA-Clustering,
title = {A Clustering Approach to Categorizing 7 Degree-of-Freedom Arm Motions during Activities of Daily Living},
booktitle = {Proceedings of the International Conference on Robotics and Automation (ICRA)},
abstract = {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.},
pages = {7214--7220},
address = {Montreal, Canada},
month = may,
year = {2019},
slug = {gloumakov19-icra-clustering},
author = {Gloumakov, Yuri and Spiers, Adam J. and Dollar, Aaron M.},
month_numeric = {5}
}