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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9002497 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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