Journal
SENSORS
Volume 18, Issue 12, Pages -Publisher
MDPI
DOI: 10.3390/s18124354
Keywords
human activity recognition; symbolic representation; inertial sensors; smartphone
Funding
- FAPEAM through the Posgrad and PROTI Amazonia research project [01.10.0728-00]
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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.
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