Smartphone sensor accuracy varies from device to device in mobile research: The case of spatial orientation
Smartphone usage is increasing around the globe—in daily life and as a research device in behavioral science. Smartphones offer the possibility to gather longitudinal data at little cost to researchers and participants. They provide the option to verify self-report data with data from sensors built...
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Veröffentlicht in: | Behavior Research Methods 2021-02, Vol.53 (1), p.22-33 |
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description | Smartphone usage is increasing around the globe—in daily life and as a research device in behavioral science. Smartphones offer the possibility to gather longitudinal data at little cost to researchers and participants. They provide the option to verify self-report data with data from sensors built into most smartphones. How accurate this sensor data is when gathered via different smartphone devices, e.g., in a typical experience sampling framework, has not been investigated systematically. With the present study, we investigated the accuracy of orientation data about the spatial position of smartphones via a newly invented measurement device, the
RollPitcher
. Objective status of
pitch
(vertical orientation) and
roll
(horizontal orientation) of the smartphone was compared to data gathered from the sensors via web browsers and native apps. Bayesian ANOVAs confirmed that the deviations in pitch and roll differed between smartphone models, with mean inaccuracies per device of up to 2.1° and 6.6°, respectively. The inaccuracies for measurements of roll were higher than for pitch, d = .28,
p
< .001. Our results confirm the presence of heterogeneities when gathering orientation data from different smartphone devices. In most cases, measurement via a web browser was identical to measurement via a native app, but this was not true for all smartphone devices. As a solution to lack of sensor accuracy, we recommend the development and implementation of a coherent research framework and also discuss the implications of the heterogeneities in orientation data for different research designs. |
doi_str_mv | 10.3758/s13428-020-01404-5 |
format | Article |
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RollPitcher
. Objective status of
pitch
(vertical orientation) and
roll
(horizontal orientation) of the smartphone was compared to data gathered from the sensors via web browsers and native apps. Bayesian ANOVAs confirmed that the deviations in pitch and roll differed between smartphone models, with mean inaccuracies per device of up to 2.1° and 6.6°, respectively. The inaccuracies for measurements of roll were higher than for pitch, d = .28,
p
< .001. Our results confirm the presence of heterogeneities when gathering orientation data from different smartphone devices. In most cases, measurement via a web browser was identical to measurement via a native app, but this was not true for all smartphone devices. As a solution to lack of sensor accuracy, we recommend the development and implementation of a coherent research framework and also discuss the implications of the heterogeneities in orientation data for different research designs.</description><identifier>ISSN: 1554-3528</identifier><identifier>ISSN: 1554-351X</identifier><identifier>EISSN: 1554-3528</identifier><identifier>DOI: 10.3758/s13428-020-01404-5</identifier><identifier>PMID: 32472500</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Accuracy ; Bayes Theorem ; Bayesian analysis ; Behavioral Science and Psychology ; Cognitive Psychology ; Humans ; Information management ; Mathematical models ; Orientation, Spatial ; Psychology ; Self Report ; Sensors ; Smart phones ; Smartphone ; Smartphones ; Society for Computers in Psychology Collection ; Web applications</subject><ispartof>Behavior Research Methods, 2021-02, Vol.53 (1), p.22-33</ispartof><rights>The Author(s) 2020</rights><rights>COPYRIGHT 2021 Springer</rights><rights>The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c541t-b223734c0e88c2e5f17e50db1254a116f5b89eb5c77a4cd1235201c3e86c73853</citedby><cites>FETCH-LOGICAL-c541t-b223734c0e88c2e5f17e50db1254a116f5b89eb5c77a4cd1235201c3e86c73853</cites><orcidid>0000-0003-4673-1733</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.3758/s13428-020-01404-5$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.3758/s13428-020-01404-5$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32472500$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kuhlmann, Tim</creatorcontrib><creatorcontrib>Garaizar, Pablo</creatorcontrib><creatorcontrib>Reips, Ulf-Dietrich</creatorcontrib><title>Smartphone sensor accuracy varies from device to device in mobile research: The case of spatial orientation</title><title>Behavior Research Methods</title><addtitle>Behav Res</addtitle><addtitle>Behav Res Methods</addtitle><description>Smartphone usage is increasing around the globe—in daily life and as a research device in behavioral science. Smartphones offer the possibility to gather longitudinal data at little cost to researchers and participants. They provide the option to verify self-report data with data from sensors built into most smartphones. How accurate this sensor data is when gathered via different smartphone devices, e.g., in a typical experience sampling framework, has not been investigated systematically. With the present study, we investigated the accuracy of orientation data about the spatial position of smartphones via a newly invented measurement device, the
RollPitcher
. Objective status of
pitch
(vertical orientation) and
roll
(horizontal orientation) of the smartphone was compared to data gathered from the sensors via web browsers and native apps. Bayesian ANOVAs confirmed that the deviations in pitch and roll differed between smartphone models, with mean inaccuracies per device of up to 2.1° and 6.6°, respectively. The inaccuracies for measurements of roll were higher than for pitch, d = .28,
p
< .001. Our results confirm the presence of heterogeneities when gathering orientation data from different smartphone devices. In most cases, measurement via a web browser was identical to measurement via a native app, but this was not true for all smartphone devices. As a solution to lack of sensor accuracy, we recommend the development and implementation of a coherent research framework and also discuss the implications of the heterogeneities in orientation data for different research designs.</description><subject>Accuracy</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Behavioral Science and Psychology</subject><subject>Cognitive Psychology</subject><subject>Humans</subject><subject>Information management</subject><subject>Mathematical models</subject><subject>Orientation, Spatial</subject><subject>Psychology</subject><subject>Self Report</subject><subject>Sensors</subject><subject>Smart phones</subject><subject>Smartphone</subject><subject>Smartphones</subject><subject>Society for Computers in Psychology Collection</subject><subject>Web applications</subject><issn>1554-3528</issn><issn>1554-351X</issn><issn>1554-3528</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><recordid>eNp9UU1v1DAQtRAVbRf-AAdkiQuXFI8_GocDUlW1gFSpB8rZcpzJrktiL3ayUv893qYthQOaw4w0b97Mm0fIW2Anolb6YwYhua4YZxUDyWSlXpAjUEpWQnH98ll9SI5zvmVMaA7yFTkUXNZcMXZEfn4fbZq2mxiQZgw5Jmqdm5N1d3Rnk8dM-xRH2uHOO6RTfKx8oGNs_YA0YUab3OYTvdkgdTYjjT3NWzt5O9BYOMJU6hhek4PeDhnfPOQV-XF5cXP-tbq6_vLt_OyqckrCVLWci1pIx1Brx1H1UKNiXQtcSQtw2qtWN9gqV9dWug54UcjACdSnrhZaiRX5vPBu53bEzpX9yQ5mm3zRemei9ebvTvAbs447U2vNmsK3Ih8eCFL8NWOezOizw2GwAeOcDZdMQwPA9tD3_0Bv45xCkVdQumEgmJQFdbKg1nZA40Mfy15XosPRu_L7vjzSnNUgGl4CygBfBlyKOSfsn64HZvbmm8V8U8w39-abve53z3U_jTy6XQBiAeTSCmtMf479D-1vDUC6oQ</recordid><startdate>20210201</startdate><enddate>20210201</enddate><creator>Kuhlmann, Tim</creator><creator>Garaizar, Pablo</creator><creator>Reips, Ulf-Dietrich</creator><general>Springer US</general><general>Springer</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IAO</scope><scope>4T-</scope><scope>7TK</scope><scope>K9.</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-4673-1733</orcidid></search><sort><creationdate>20210201</creationdate><title>Smartphone sensor accuracy varies from device to device in mobile research: The case of spatial orientation</title><author>Kuhlmann, Tim ; Garaizar, Pablo ; Reips, Ulf-Dietrich</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c541t-b223734c0e88c2e5f17e50db1254a116f5b89eb5c77a4cd1235201c3e86c73853</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>Behavioral Science and Psychology</topic><topic>Cognitive Psychology</topic><topic>Humans</topic><topic>Information management</topic><topic>Mathematical models</topic><topic>Orientation, Spatial</topic><topic>Psychology</topic><topic>Self Report</topic><topic>Sensors</topic><topic>Smart phones</topic><topic>Smartphone</topic><topic>Smartphones</topic><topic>Society for Computers in Psychology Collection</topic><topic>Web applications</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kuhlmann, Tim</creatorcontrib><creatorcontrib>Garaizar, Pablo</creatorcontrib><creatorcontrib>Reips, Ulf-Dietrich</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale Academic OneFile</collection><collection>Docstoc</collection><collection>Neurosciences Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Behavior Research Methods</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kuhlmann, Tim</au><au>Garaizar, Pablo</au><au>Reips, Ulf-Dietrich</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Smartphone sensor accuracy varies from device to device in mobile research: The case of spatial orientation</atitle><jtitle>Behavior Research Methods</jtitle><stitle>Behav Res</stitle><addtitle>Behav Res Methods</addtitle><date>2021-02-01</date><risdate>2021</risdate><volume>53</volume><issue>1</issue><spage>22</spage><epage>33</epage><pages>22-33</pages><issn>1554-3528</issn><issn>1554-351X</issn><eissn>1554-3528</eissn><abstract>Smartphone usage is increasing around the globe—in daily life and as a research device in behavioral science. Smartphones offer the possibility to gather longitudinal data at little cost to researchers and participants. They provide the option to verify self-report data with data from sensors built into most smartphones. How accurate this sensor data is when gathered via different smartphone devices, e.g., in a typical experience sampling framework, has not been investigated systematically. With the present study, we investigated the accuracy of orientation data about the spatial position of smartphones via a newly invented measurement device, the
RollPitcher
. Objective status of
pitch
(vertical orientation) and
roll
(horizontal orientation) of the smartphone was compared to data gathered from the sensors via web browsers and native apps. Bayesian ANOVAs confirmed that the deviations in pitch and roll differed between smartphone models, with mean inaccuracies per device of up to 2.1° and 6.6°, respectively. The inaccuracies for measurements of roll were higher than for pitch, d = .28,
p
< .001. Our results confirm the presence of heterogeneities when gathering orientation data from different smartphone devices. In most cases, measurement via a web browser was identical to measurement via a native app, but this was not true for all smartphone devices. As a solution to lack of sensor accuracy, we recommend the development and implementation of a coherent research framework and also discuss the implications of the heterogeneities in orientation data for different research designs.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>32472500</pmid><doi>10.3758/s13428-020-01404-5</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0003-4673-1733</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Accuracy Bayes Theorem Bayesian analysis Behavioral Science and Psychology Cognitive Psychology Humans Information management Mathematical models Orientation, Spatial Psychology Self Report Sensors Smart phones Smartphone Smartphones Society for Computers in Psychology Collection Web applications |
title | Smartphone sensor accuracy varies from device to device in mobile research: The case of spatial orientation |
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