Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection
This paper aims to examine the role of global positioning system (GPS) sensor data in real-life physical activity (PA) type detection. Thirty-three young participants wore devices including GPS and accelerometer sensors on five body positions and performed daily PAs in two protocols, namely semi-str...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038120/ https://www.ncbi.nlm.nih.gov/pubmed/31973129 http://dx.doi.org/10.3390/s20030588 |
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author | Allahbakhshi, Hoda Conrow, Lindsey Naimi, Babak Weibel, Robert |
author_facet | Allahbakhshi, Hoda Conrow, Lindsey Naimi, Babak Weibel, Robert |
author_sort | Allahbakhshi, Hoda |
collection | PubMed |
description | This paper aims to examine the role of global positioning system (GPS) sensor data in real-life physical activity (PA) type detection. Thirty-three young participants wore devices including GPS and accelerometer sensors on five body positions and performed daily PAs in two protocols, namely semi-structured and real-life. One general random forest (RF) model integrating data from all sensors and five individual RF models using data from each sensor position were trained using semi-structured (Scenario 1) and combined (semi-structured + real-life) data (Scenario 2). The results showed that in general, adding GPS features (speed and elevation difference) to accelerometer data improves classification performance particularly for detecting non-level and level walking. Assessing the transferability of the models on real-life data showed that models from Scenario 2 are strongly transferable, particularly when adding GPS data to the training data. Comparing individual models indicated that knee-models provide comparable classification performance (above 80%) to general models in both scenarios. In conclusion, adding GPS data improves real-life PA type classification performance if combined data are used for training the model. Moreover, the knee-model provides the minimal device configuration with reliable accuracy for detecting real-life PA types. |
format | Online Article Text |
id | pubmed-7038120 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-70381202020-03-10 Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection Allahbakhshi, Hoda Conrow, Lindsey Naimi, Babak Weibel, Robert Sensors (Basel) Article This paper aims to examine the role of global positioning system (GPS) sensor data in real-life physical activity (PA) type detection. Thirty-three young participants wore devices including GPS and accelerometer sensors on five body positions and performed daily PAs in two protocols, namely semi-structured and real-life. One general random forest (RF) model integrating data from all sensors and five individual RF models using data from each sensor position were trained using semi-structured (Scenario 1) and combined (semi-structured + real-life) data (Scenario 2). The results showed that in general, adding GPS features (speed and elevation difference) to accelerometer data improves classification performance particularly for detecting non-level and level walking. Assessing the transferability of the models on real-life data showed that models from Scenario 2 are strongly transferable, particularly when adding GPS data to the training data. Comparing individual models indicated that knee-models provide comparable classification performance (above 80%) to general models in both scenarios. In conclusion, adding GPS data improves real-life PA type classification performance if combined data are used for training the model. Moreover, the knee-model provides the minimal device configuration with reliable accuracy for detecting real-life PA types. MDPI 2020-01-21 /pmc/articles/PMC7038120/ /pubmed/31973129 http://dx.doi.org/10.3390/s20030588 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Allahbakhshi, Hoda Conrow, Lindsey Naimi, Babak Weibel, Robert Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection |
title | Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection |
title_full | Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection |
title_fullStr | Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection |
title_full_unstemmed | Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection |
title_short | Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection |
title_sort | using accelerometer and gps data for real-life physical activity type detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038120/ https://www.ncbi.nlm.nih.gov/pubmed/31973129 http://dx.doi.org/10.3390/s20030588 |
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