A Comprehensive Study of Activity Recognition Using Accelerometers

This paper serves as a survey and empirical evaluation of the state-of-the-art in activity recognition methods using accelerometers. The paper is particularly focused on long-term activity recognition in real-world settings. In these environments, data collection is not a trivial matter; thus, there...

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Bibliographic Details
Published in:Informatics (Basel) 2018-05, Vol.5 (2), p.27
Main Authors: Twomey, Niall, Diethe, Tom, Fafoutis, Xenofon, Elsts, Atis, McConville, Ryan, Flach, Peter, Craddock, Ian
Format: Article
Language:English
Subjects:
Accelerometers
Active learning
Activities of daily living
Activity recognition
Algorithms
Artificial intelligence
Classification
Configuration management
Configurations
Data acquisition
Datasets
Energy consumption
Feature extraction
Knowledge management
machine learning
Performance prediction
Sensors
State-of-the-art reviews
Tradeoffs
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Summary:This paper serves as a survey and empirical evaluation of the state-of-the-art in activity recognition methods using accelerometers. The paper is particularly focused on long-term activity recognition in real-world settings. In these environments, data collection is not a trivial matter; thus, there are performance trade-offs between prediction accuracy, which is not the sole system objective, and keeping the maintenance overhead at minimum levels. We examine research that has focused on the selection of activities, the features that are extracted from the accelerometer data, the segmentation of the time-series data, the locations of accelerometers, the selection and configuration trade-offs, the test/retest reliability, and the generalisation performance. Furthermore, we study these questions from an experimental platform and show, somewhat surprisingly, that many disparate experimental configurations yield comparable predictive performance on testing data. Our understanding of these results is that the experimental setup directly and indirectly defines a pathway for context to be delivered to the classifier, and that, in some settings, certain configurations are more optimal than alternatives. We conclude by identifying how the main results of this work can be used in practice, specifically in experimental configurations in challenging experimental conditions.
ISSN:2227-9709
2227-9709
DOI:10.3390/informatics5020027