Authors:
Rafael Rego Drumond
1
;
Bruno A. Dorta Marques
2
;
Cristina Nader Vasconcelos
2
and
Esteban Clua
2
Affiliations:
1
University of Hildesheim, Germany
;
2
Universidade Federal Fluminense, Brazil
Keyword(s):
Motion Classifier, IMU Device, Deep Learning, Recurrent Neural Networks, Sparse Data, Machine Learning.
Related
Ontology
Subjects/Areas/Topics:
Advanced User Interfaces
;
Animation and Simulation
;
Animation from Motion Capture
;
Computer Vision, Visualization and Computer Graphics
;
Interactive Environments
Abstract:
Games and other applications are exploring many different modes of interaction in order to create intuitive
interfaces, such as touch screens, motion controllers, recognition of gesture or body movements among many
others. In that direction, human motion is being captured by different sensors, such as accelerometers, gyroscopes,
heat sensors and cameras. However, there is still room for investigation the analysis of motion data
captured from low-cost sensors. This article explores the extent to which a full body motion classification
can be achieved by observing only sparse data captured by two separate inherent wereable measurement unit
(IMU) sensors. For that, we developed a novel Recurrent Neural Network topology based on Long Short-Term
Memory cells (LSTMs) that are able to classify motions sequences of different sizes. Using cross-validation
tests, our model achieves an overall accuracy of 96% which is quite significant considering that the raw data
used was obtained us
ing only 2 simple and accessible IMU sensors capturing arms movements. We also built
and made public a motion database constructed by capturing sparse data from 11 actors performing five different
actions. For comparison with existent methods, other deep learning approaches for sequence evaluation
(more specifically, based on convolutional neural networks), were adapted to our problem and evaluated.
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