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Human Activity Recognition With Accelerometer and Gyroscope: A Data Fusion Approach.

Authors :
Webber, Mitchell
Rojas, Raul Fernandez
Source :
IEEE Sensors Journal; Aug2021, Vol. 21 Issue 15, p16979-16989, 11p
Publication Year :
2021

Abstract

This paper compares the three levels of data fusion with the goal of determining the optimal level of data fusion for multi-sensor human activity data. Using the data processing pipeline, gyroscope and accelerometer data was fused at the sensor-level, feature-level and decision-level. For each level of data fusion four different techniques were used with varying levels of success. This analysis was performed on four human activity publicly-available datasets along with four well-known machine learning classifiers to validate the results. The decision-level fusion (Acc = 0.7443±0.0850) outperformed the other two levels of fusion in regards to accuracy, sensor level (Acc = 0.5934 ± 0.1110) and feature level (Acc = 0.6742 ± 0.0053), but, the processing time and computational power required for training and classification were far greater than practical for a HAR system. However, Kalman filter appear to be the more efficient method, since it exhibited both good accuracy (Acc = 0.7536 ± 0.1566) and short processing time (time = 61.71ms ± 63.85); properties that play a large role in real-time applications using wearable devices. The results of this study also serve as baseline information in the HAR literature to compare future methods of data fusion. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1530437X
Volume :
21
Issue :
15
Database :
Complementary Index
Journal :
IEEE Sensors Journal
Publication Type :
Academic Journal
Accession number :
153094599
Full Text :
https://doi.org/10.1109/JSEN.2021.3079883