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Smartphone-Based Activity Recognition Using Multistream Movelets Combining Accelerometer and Gyroscope Data

Physical activity patterns can reveal information about one’s health status. Built-in sensors in a smartphone, in comparison to a patient’s self-report, can collect activity recognition data more objectively, unobtrusively, and continuously. A variety of data analysis approaches have been proposed i...

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Autores principales: Huang, Emily J., Yan, Kebin, Onnela, Jukka-Pekka
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002497/
https://www.ncbi.nlm.nih.gov/pubmed/35408232
http://dx.doi.org/10.3390/s22072618
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author Huang, Emily J.
Yan, Kebin
Onnela, Jukka-Pekka
author_facet Huang, Emily J.
Yan, Kebin
Onnela, Jukka-Pekka
author_sort Huang, Emily J.
collection PubMed
description Physical activity patterns can reveal information about one’s health status. Built-in sensors in a smartphone, in comparison to a patient’s self-report, can collect activity recognition data more objectively, unobtrusively, and continuously. A variety of data analysis approaches have been proposed in the literature. In this study, we applied the movelet method to classify the activities performed using smartphone accelerometer and gyroscope data, which measure a phone’s acceleration and angular velocity, respectively. The movelet method constructs a personalized dictionary for each participant using training data and classifies activities in new data with the dictionary. Our results show that this method has the advantages of being interpretable and transparent. A unique aspect of our movelet application involves extracting unique information, optimally, from multiple sensors. In comparison to single-sensor applications, our approach jointly incorporates the accelerometer and gyroscope sensors with the movelet method. Our findings show that combining data from the two sensors can result in more accurate activity recognition than using each sensor alone. In particular, the joint-sensor method reduces errors of the gyroscope-only method in differentiating between standing and sitting. It also reduces errors in the accelerometer-only method when classifying vigorous activities.
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spelling pubmed-90024972022-04-13 Smartphone-Based Activity Recognition Using Multistream Movelets Combining Accelerometer and Gyroscope Data Huang, Emily J. Yan, Kebin Onnela, Jukka-Pekka Sensors (Basel) Article Physical activity patterns can reveal information about one’s health status. Built-in sensors in a smartphone, in comparison to a patient’s self-report, can collect activity recognition data more objectively, unobtrusively, and continuously. A variety of data analysis approaches have been proposed in the literature. In this study, we applied the movelet method to classify the activities performed using smartphone accelerometer and gyroscope data, which measure a phone’s acceleration and angular velocity, respectively. The movelet method constructs a personalized dictionary for each participant using training data and classifies activities in new data with the dictionary. Our results show that this method has the advantages of being interpretable and transparent. A unique aspect of our movelet application involves extracting unique information, optimally, from multiple sensors. In comparison to single-sensor applications, our approach jointly incorporates the accelerometer and gyroscope sensors with the movelet method. Our findings show that combining data from the two sensors can result in more accurate activity recognition than using each sensor alone. In particular, the joint-sensor method reduces errors of the gyroscope-only method in differentiating between standing and sitting. It also reduces errors in the accelerometer-only method when classifying vigorous activities. MDPI 2022-03-29 /pmc/articles/PMC9002497/ /pubmed/35408232 http://dx.doi.org/10.3390/s22072618 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Huang, Emily J.
Yan, Kebin
Onnela, Jukka-Pekka
Smartphone-Based Activity Recognition Using Multistream Movelets Combining Accelerometer and Gyroscope Data
title Smartphone-Based Activity Recognition Using Multistream Movelets Combining Accelerometer and Gyroscope Data
title_full Smartphone-Based Activity Recognition Using Multistream Movelets Combining Accelerometer and Gyroscope Data
title_fullStr Smartphone-Based Activity Recognition Using Multistream Movelets Combining Accelerometer and Gyroscope Data
title_full_unstemmed Smartphone-Based Activity Recognition Using Multistream Movelets Combining Accelerometer and Gyroscope Data
title_short Smartphone-Based Activity Recognition Using Multistream Movelets Combining Accelerometer and Gyroscope Data
title_sort smartphone-based activity recognition using multistream movelets combining accelerometer and gyroscope data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002497/
https://www.ncbi.nlm.nih.gov/pubmed/35408232
http://dx.doi.org/10.3390/s22072618
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