loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Pekka Siirtola and Juha Röning

Affiliation: University of Oulu, Finland

Keyword(s): Accelerometer, Sensor Fusion, Activity Recognition, Machine Learning, Mobile Phones.

Related Ontology Subjects/Areas/Topics: Applications ; Cardiovascular Imaging and Cardiography ; Cardiovascular Technologies ; Classification ; Computer Vision, Visualization and Computer Graphics ; Feature Selection and Extraction ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Motion and Tracking ; Motion, Tracking and Stereo Vision ; Pattern Recognition ; Physiological Computing Systems ; Sensors and Early Vision ; Signal Processing ; Software Engineering ; Theory and Methods

Abstract: In this study, a novel user-independent method to recognize activities accurately in situations where traditional accelerometer based classification contains a lot of uncertainty is presented. The method uses two recognition models: one using only accelerometer data and other based on sensor fusion. However, as a sensor fusionbased method is known to consume more battery than an accelerometer-based, sensor fusion is only used when the classification result obtained using acceleration contains uncertainty and, therefore, is unreliable. This reliability is measured based on the posterior probabilities of the classification result and it is studied in the article how high the probability needs to be to consider it reliable. The method is tested using two data sets: daily activity data set collected using accelerometer and magnetometer, and tool recognition data set consisting of data from accelerometer and gyroscope measurements. The results show that by applying the presented method, t he recognition rates can be improved compared to using only accelerometers. It was noted that all the classification results should not be trusted as posterior probabilities under 95% cannot be considered reliable, and by replacing these results with the results of sensor fusion -based model, the recognition accuracy improves from three to six percentage units. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 44.220.245.254

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Siirtola, P. and Röning, J. (2016). Reducing Uncertainty in User-independent Activity Recognition - A Sensor Fusion-based Approach. In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-173-1; ISSN 2184-4313, SciTePress, pages 611-619. DOI: 10.5220/0005743106110619

@conference{icpram16,
author={Pekka Siirtola. and Juha Röning.},
title={Reducing Uncertainty in User-independent Activity Recognition - A Sensor Fusion-based Approach},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2016},
pages={611-619},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005743106110619},
isbn={978-989-758-173-1},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Reducing Uncertainty in User-independent Activity Recognition - A Sensor Fusion-based Approach
SN - 978-989-758-173-1
IS - 2184-4313
AU - Siirtola, P.
AU - Röning, J.
PY - 2016
SP - 611
EP - 619
DO - 10.5220/0005743106110619
PB - SciTePress