Paper Info
Title
Estimation of Full-Body Poses Using Only Five Inertial Sensors: An Eager or Lazy Learning Approach?
Abstract
Human movement analysis has become easier with the wide availability of motion capture systems. Inertial sensing has made it possible to capture human motion without external infrastructure, therefore allowing measurements in any environment. As high-quality motion capture data is available in large quantities, this creates possibilities to further simplify hardware setups, by use of data-driven methods to decrease the number of body-worn sensors. In this work, we contribute to this field by analyzing the capabilities of using either artificial neural networks (eager learning) or nearest neighbor search (lazy learning) for such a problem. Sparse orientation features, resulting from sensor fusion of only five inertial measurement units with magnetometers, are mapped to full-body poses. Both eager and lazy learning algorithms are shown to be capable of constructing this mapping. The full-body output poses are visually plausible with an average joint position error of approximately 7 cm, and average joint angle error of 7 degrees. Additionally, the effects of magnetic disturbances typical in orientation tracking on the estimation of full-body poses was also investigated, where nearest neighbor search showed better performance for such disturbances.
Year
DOI
Venue
2016
10.3390/s16122138
SENSORS
Keywords
Field
DocType
inertial motion capture,orientation tracking,machine learning,neural networks,nearest neighbor search,human movement,reduced sensor set
Motion capture,Computer vision,Units of measurement,Computer science,Lazy learning,Eager learning,Sensor fusion,Inertial measurement unit,Artificial intelligence,Artificial neural network,Nearest neighbor search
Journal
Volume
Issue
ISSN
16
12.0
1424-8220
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Frank J. Wouda101.35
Matteo Giuberti2386.35
Giovanni Bellusci3173.50
Peter H. Veltink429142.38