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Article

MBOSS: A Symbolic Representation of Human Activity Recognition Using Mobile Sensors

1
Computer Institute, Federal University of Amazonas, Manaus 69080-900, Brazil
2
Department of Computer Science, University of São Paulo, São Paulo 05508-090, Brazil
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(12), 4354; https://doi.org/10.3390/s18124354
Received: 16 November 2018 / Revised: 29 November 2018 / Accepted: 1 December 2018 / Published: 10 December 2018
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensors Networks)
Human activity recognition (HAR) through sensors embedded in smartphones has allowed for the development of systems that are capable of detecting and monitoring human behavior. However, such systems have been affected by the high consumption of computational resources (e.g., memory and processing) needed to effectively recognize activities. In addition, existing HAR systems are mostly based on supervised classification techniques, in which the feature extraction process is done manually, and depends on the knowledge of a specialist. To overcome these limitations, this paper proposes a new method for recognizing human activities based on symbolic representation algorithms. The method, called “Multivariate Bag-Of-SFA-Symbols” (MBOSS), aims to increase the efficiency of HAR systems and maintain accuracy levels similar to those of conventional systems based on time and frequency domain features. The experiments conducted on three public datasets showed that MBOSS performed the best in terms of accuracy, processing time, and memory consumption. View Full-Text
Keywords: human activity recognition; symbolic representation; inertial sensors; smartphone human activity recognition; symbolic representation; inertial sensors; smartphone
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MDPI and ACS Style

Montero Quispe, K.G.; Sousa Lima, W.; Macêdo Batista, D.; Souto, E. MBOSS: A Symbolic Representation of Human Activity Recognition Using Mobile Sensors. Sensors 2018, 18, 4354. https://doi.org/10.3390/s18124354

AMA Style

Montero Quispe KG, Sousa Lima W, Macêdo Batista D, Souto E. MBOSS: A Symbolic Representation of Human Activity Recognition Using Mobile Sensors. Sensors. 2018; 18(12):4354. https://doi.org/10.3390/s18124354

Chicago/Turabian Style

Montero Quispe, Kevin G., Wesllen Sousa Lima, Daniel Macêdo Batista, and Eduardo Souto. 2018. "MBOSS: A Symbolic Representation of Human Activity Recognition Using Mobile Sensors" Sensors 18, no. 12: 4354. https://doi.org/10.3390/s18124354

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