Activity recognition invariant to sensor orientation with wearable motion sensors

Date
2017
Authors
Yurtman, A.
Barshan, B.
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Source Title
Sensors (Switzerland)
Print ISSN
1424-8220
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Publisher
MDPI AG
Volume
17
Issue
8
Pages
Language
English
Type
Article
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Abstract

Most activity recognition studies that employ wearable sensors assume that the sensors are attached at pre-determined positions and orientations that do not change over time. Since this is not the case in practice, it is of interest to develop wearable systems that operate invariantly to sensor position and orientation. We focus on invariance to sensor orientation and develop two alternative transformations to remove the effect of absolute sensor orientation from the raw sensor data. We test the proposed methodology in activity recognition with four state-of-the-art classifiers using five publicly available datasets containing various types of human activities acquired by different sensor configurations. While the ordinary activity recognition system cannot handle incorrectly oriented sensors, the proposed transformations allow the sensors to be worn at any orientation at a given position on the body, and achieve nearly the same activity recognition performance as the ordinary system for which the sensor units are not rotatable. The proposed techniques can be applied to existing wearable systems without much effort, by simply transforming the time-domain sensor data at the pre-processing stage. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.

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Keywords
Accelerometer, Artificial neural networks, Bayesian decision making, Human activity recognition, Inertial sensors, K-nearest-neighbor classifier, Machine learning, Magnetometer, Motion sensors, Orientation-invariant sensing, Sensor orientation, Singular value decomposition, Support vector machines, Wearable sensing, Accelerometers, Bayesian networks, Classification (of information), Decision making, Gyroscopes, Learning systems, Magnetometers, Metadata, Nearest neighbor search, Neural networks, Pattern recognition, Singular value decomposition, Support vector machines, Time domain analysis, Wearable technology, Bayesian decision makings, Human activity recognition, Inertial sensor, K-nearest neighbor classifier, Sensor orientation, Wearable sensing, Wearable sensors
Citation
Published Version (Please cite this version)