Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices

Smartphone and wearable devices are widely used in behavioral and clinical research to collect longitudinal data that, along with ground truth data, are used to create models of human behavior. Mobile sensing researchers often program data processing and analysis code from scratch even though many r...

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Autores principales:	Julio Vega, Meng Li, Kwesi Aguillera, Nikunj Goel, Echhit Joshi, Kirtiraj Khandekar, Krina C. Durica, Abhineeth R. Kunta, Carissa A. Low
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	digital health digital phenotyping mobile sensing smartphone wearable digital biomarkers Medicine R Public aspects of medicine RA1-1270 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/1c6126599ac7436da02d6cff86259de6
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spelling	oai:doaj.org-article:1c6126599ac7436da02d6cff86259de62021-11-18T05:38:11ZReproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices2673-253X10.3389/fdgth.2021.769823https://doaj.org/article/1c6126599ac7436da02d6cff86259de62021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fdgth.2021.769823/fullhttps://doaj.org/toc/2673-253XSmartphone and wearable devices are widely used in behavioral and clinical research to collect longitudinal data that, along with ground truth data, are used to create models of human behavior. Mobile sensing researchers often program data processing and analysis code from scratch even though many research teams collect data from similar mobile sensors, platforms, and devices. This leads to significant inefficiency in not being able to replicate and build on others' work, inconsistency in quality of code and results, and lack of transparency when code is not shared alongside publications. We provide an overview of Reproducible Analysis Pipeline for Data Streams (RAPIDS), a reproducible pipeline to standardize the preprocessing, feature extraction, analysis, visualization, and reporting of data streams coming from mobile sensors. RAPIDS is formed by a group of R and Python scripts that are executed on top of reproducible virtual environments, orchestrated by a workflow management system, and organized following a consistent file structure for data science projects. We share open source, documented, extensible and tested code to preprocess, extract, and visualize behavioral features from data collected with any Android or iOS smartphone sensing app as well as Fitbit and Empatica wearable devices. RAPIDS allows researchers to process mobile sensor data in a rigorous and reproducible way. This saves time and effort during the data analysis phase of a project and facilitates sharing analysis workflows alongside publications.Julio VegaMeng LiKwesi AguilleraNikunj GoelEchhit JoshiKirtiraj KhandekarKrina C. DuricaAbhineeth R. KuntaCarissa A. LowFrontiers Media S.A.articledigital healthdigital phenotypingmobile sensingsmartphonewearabledigital biomarkersMedicineRPublic aspects of medicineRA1-1270Electronic computers. Computer scienceQA75.5-76.95ENFrontiers in Digital Health, Vol 3 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	digital health digital phenotyping mobile sensing smartphone wearable digital biomarkers Medicine R Public aspects of medicine RA1-1270 Electronic computers. Computer science QA75.5-76.95
spellingShingle	digital health digital phenotyping mobile sensing smartphone wearable digital biomarkers Medicine R Public aspects of medicine RA1-1270 Electronic computers. Computer science QA75.5-76.95 Julio Vega Meng Li Kwesi Aguillera Nikunj Goel Echhit Joshi Kirtiraj Khandekar Krina C. Durica Abhineeth R. Kunta Carissa A. Low Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices
description	Smartphone and wearable devices are widely used in behavioral and clinical research to collect longitudinal data that, along with ground truth data, are used to create models of human behavior. Mobile sensing researchers often program data processing and analysis code from scratch even though many research teams collect data from similar mobile sensors, platforms, and devices. This leads to significant inefficiency in not being able to replicate and build on others' work, inconsistency in quality of code and results, and lack of transparency when code is not shared alongside publications. We provide an overview of Reproducible Analysis Pipeline for Data Streams (RAPIDS), a reproducible pipeline to standardize the preprocessing, feature extraction, analysis, visualization, and reporting of data streams coming from mobile sensors. RAPIDS is formed by a group of R and Python scripts that are executed on top of reproducible virtual environments, orchestrated by a workflow management system, and organized following a consistent file structure for data science projects. We share open source, documented, extensible and tested code to preprocess, extract, and visualize behavioral features from data collected with any Android or iOS smartphone sensing app as well as Fitbit and Empatica wearable devices. RAPIDS allows researchers to process mobile sensor data in a rigorous and reproducible way. This saves time and effort during the data analysis phase of a project and facilitates sharing analysis workflows alongside publications.
format	article
author	Julio Vega Meng Li Kwesi Aguillera Nikunj Goel Echhit Joshi Kirtiraj Khandekar Krina C. Durica Abhineeth R. Kunta Carissa A. Low
author_facet	Julio Vega Meng Li Kwesi Aguillera Nikunj Goel Echhit Joshi Kirtiraj Khandekar Krina C. Durica Abhineeth R. Kunta Carissa A. Low
author_sort	Julio Vega
title	Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices
title_short	Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices
title_full	Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices
title_fullStr	Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices
title_full_unstemmed	Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices
title_sort	reproducible analysis pipeline for data streams: open-source software to process data collected with mobile devices
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/1c6126599ac7436da02d6cff86259de6
work_keys_str_mv	AT juliovega reproducibleanalysispipelinefordatastreamsopensourcesoftwaretoprocessdatacollectedwithmobiledevices AT mengli reproducibleanalysispipelinefordatastreamsopensourcesoftwaretoprocessdatacollectedwithmobiledevices AT kwesiaguillera reproducibleanalysispipelinefordatastreamsopensourcesoftwaretoprocessdatacollectedwithmobiledevices AT nikunjgoel reproducibleanalysispipelinefordatastreamsopensourcesoftwaretoprocessdatacollectedwithmobiledevices AT echhitjoshi reproducibleanalysispipelinefordatastreamsopensourcesoftwaretoprocessdatacollectedwithmobiledevices AT kirtirajkhandekar reproducibleanalysispipelinefordatastreamsopensourcesoftwaretoprocessdatacollectedwithmobiledevices AT krinacdurica reproducibleanalysispipelinefordatastreamsopensourcesoftwaretoprocessdatacollectedwithmobiledevices AT abhineethrkunta reproducibleanalysispipelinefordatastreamsopensourcesoftwaretoprocessdatacollectedwithmobiledevices AT carissaalow reproducibleanalysispipelinefordatastreamsopensourcesoftwaretoprocessdatacollectedwithmobiledevices
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Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices

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