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A spherical-plot solution to linking acceleration metrics with animal performance, state, behaviour and lifestyle
Rory Wilson 
,
Mark Holton ,
James S. Walker,
Emily Shepard ,
D. Mike Scantlebury,
Vianney L. Wilson,
Gwendoline I. Wilson,
Brenda Tysse,
Mike Gravenor,
Javier Ciancio,
Melitta McNarry ,
Kelly Mackintosh ,
Lama Qasem,
Frank Rosell,
Patricia M. Graf,
Flavio Quintana,
Agustina Gomez-Laich,
Juan-Emilio Sala,
Christina C. Mulvenna,
Nicola J. Marks,
Mark Jones ,
Michael Gravenor
,
Mark Holton ,
James S. Walker,
Emily Shepard ,
D. Mike Scantlebury,
Vianney L. Wilson,
Gwendoline I. Wilson,
Brenda Tysse,
Mike Gravenor,
Javier Ciancio,
Melitta McNarry ,
Kelly Mackintosh ,
Lama Qasem,
Frank Rosell,
Patricia M. Graf,
Flavio Quintana,
Agustina Gomez-Laich,
Juan-Emilio Sala,
Christina C. Mulvenna,
Nicola J. Marks,
Mark Jones ,
Michael Gravenor
Movement Ecology, Volume: 4, Issue: 1
Swansea University Authors:
Rory Wilson , Mark Holton , Emily Shepard , Melitta McNarry , Kelly Mackintosh , Mark Jones , Michael Gravenor
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© 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated
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DOI (Published version): 10.1186/s40462-016-0088-3
Abstract
Background We are increasingly using recording devices with multiple sensors operating at high frequencies to produce large volumes of data which are problematic to interpret. A particularly challenging example comes from studies on animals and humans where researchers use animal-attached accelerome...
| Published in: | Movement Ecology |
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| ISSN: | 2051-3933 |
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2016
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<?xml version="1.0"?><rfc1807><datestamp>2020-07-21T11:40:10.6772704</datestamp><bib-version>v2</bib-version><id>30318</id><entry>2016-10-03</entry><title>A spherical-plot solution to linking acceleration metrics with animal performance, state, behaviour and lifestyle</title><swanseaauthors><author><sid>017bc6dd155098860945dc6249c4e9bc</sid><ORCID>0000-0003-3177-0177</ORCID><firstname>Rory</firstname><surname>Wilson</surname><name>Rory Wilson</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>0e1d89d0cc934a740dcd0a873aed178e</sid><ORCID>0000-0001-8834-3283</ORCID><firstname>Mark</firstname><surname>Holton</surname><name>Mark Holton</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>54729295145aa1ea56d176818d51ed6a</sid><ORCID>0000-0001-7325-6398</ORCID><firstname>Emily</firstname><surname>Shepard</surname><name>Emily Shepard</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>062f5697ff59f004bc8c713955988398</sid><ORCID>0000-0003-0813-7477</ORCID><firstname>Melitta</firstname><surname>McNarry</surname><name>Melitta McNarry</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>bdb20e3f31bcccf95c7bc116070c4214</sid><ORCID>0000-0003-0355-6357</ORCID><firstname>Kelly</firstname><surname>Mackintosh</surname><name>Kelly Mackintosh</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>2e1030b6e14fc9debd5d5ae7cc335562</sid><ORCID>0000-0001-8991-1190</ORCID><firstname>Mark</firstname><surname>Jones</surname><name>Mark Jones</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>70a544476ce62ba78502ce463c2500d6</sid><ORCID>0000-0003-0710-0947</ORCID><firstname>Michael</firstname><surname>Gravenor</surname><name>Michael Gravenor</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2016-10-03</date><deptcode>SBI</deptcode><abstract>Background We are increasingly using recording devices with multiple sensors operating at high frequencies to produce large volumes of data which are problematic to interpret. A particularly challenging example comes from studies on animals and humans where researchers use animal-attached accelerometers on moving subjects to attempt to quantify behaviour, energy expenditure and condition. Results The approach taken effectively concatenated three complex lines of acceleration into one visualization that highlighted patterns that were otherwise not obvious. The summation of data points within sphere facets and presentation into histograms on the sphere surface effectively dealt with data occlusion. Further frequency binning of data within facets and representation of these bins as discs on spines radiating from the sphere allowed patterns in dynamic body accelerations (DBA) associated with different postures to become obvious. Method We examine the extent to which novel, gravity-based spherical plots can produce revealing visualizations to incorporate the complexity of such multidimensional acceleration data using a suite of different acceleration-derived metrics with a view to highlighting patterns that are not obvious using current approaches. The basis for the visualisation involved three-dimensional plots of the smoothed acceleration values, which then occupied points on the surface of a sphere. This sphere was divided into facets and point density within each facet expressed as a histogram. Within each facet-dependent histogram, data were also grouped into frequency bins of any desirable parameters, most particularly dynamic body acceleration (DBA), which were then presented as discs on a central spine radiating from the facet. Greater radial distances from the sphere surface indicated greater DBA values while greater disc diameter indicated larger numbers of data points with that particular value. Conclusions We indicate how this approach links behaviour and proxies for energetics and can inform our identification and understanding of movement-related processes, highlighting subtle differences in movement and its associated energetics. This approach has ramifications that should expand to areas as disparate as disease identification, lifestyle, sports practice and wild animal ecology.</abstract><type>Journal Article</type><journal>Movement Ecology</journal><volume>4</volume><journalNumber>1</journalNumber><publisher/><issnElectronic>2051-3933</issnElectronic><keywords/><publishedDay>23</publishedDay><publishedMonth>9</publishedMonth><publishedYear>2016</publishedYear><publishedDate>2016-09-23</publishedDate><doi>10.1186/s40462-016-0088-3</doi><url/><notes/><college>COLLEGE NANME</college><department>Biosciences</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SBI</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2020-07-21T11:40:10.6772704</lastEdited><Created>2016-10-03T15:16:14.8577062</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Rory</firstname><surname>Wilson</surname><orcid>0000-0003-3177-0177</orcid><order>1</order></author><author><firstname>Mark</firstname><surname>Holton</surname><orcid>0000-0001-8834-3283</orcid><order>2</order></author><author><firstname>James S.</firstname><surname>Walker</surname><order>3</order></author><author><firstname>Emily</firstname><surname>Shepard</surname><orcid>0000-0001-7325-6398</orcid><order>4</order></author><author><firstname>D. Mike</firstname><surname>Scantlebury</surname><order>5</order></author><author><firstname>Vianney L.</firstname><surname>Wilson</surname><order>6</order></author><author><firstname>Gwendoline I.</firstname><surname>Wilson</surname><order>7</order></author><author><firstname>Brenda</firstname><surname>Tysse</surname><order>8</order></author><author><firstname>Mike</firstname><surname>Gravenor</surname><order>9</order></author><author><firstname>Javier</firstname><surname>Ciancio</surname><order>10</order></author><author><firstname>Melitta</firstname><surname>McNarry</surname><orcid>0000-0003-0813-7477</orcid><order>11</order></author><author><firstname>Kelly</firstname><surname>Mackintosh</surname><orcid>0000-0003-0355-6357</orcid><order>12</order></author><author><firstname>Lama</firstname><surname>Qasem</surname><order>13</order></author><author><firstname>Frank</firstname><surname>Rosell</surname><order>14</order></author><author><firstname>Patricia M.</firstname><surname>Graf</surname><order>15</order></author><author><firstname>Flavio</firstname><surname>Quintana</surname><order>16</order></author><author><firstname>Agustina</firstname><surname>Gomez-Laich</surname><order>17</order></author><author><firstname>Juan-Emilio</firstname><surname>Sala</surname><order>18</order></author><author><firstname>Christina C.</firstname><surname>Mulvenna</surname><order>19</order></author><author><firstname>Nicola J.</firstname><surname>Marks</surname><order>20</order></author><author><firstname>Mark</firstname><surname>Jones</surname><orcid>0000-0001-8991-1190</orcid><order>21</order></author><author><firstname>Michael</firstname><surname>Gravenor</surname><orcid>0000-0003-0710-0947</orcid><order>22</order></author></authors><documents><document><filename>30318__3868__ead8c2973b2a4991ae53cfc1503e2241.pdf</filename><originalFilename>gSpheres2016.pdf</originalFilename><uploaded>2016-10-03T15:18:33.3400000</uploaded><type>Output</type><contentLength>2505091</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><embargoDate>2016-10-03T00:00:00.0000000</embargoDate><documentNotes>© 2016 The Author(s). 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reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
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| spelling |
2020-07-21T11:40:10.6772704 v2 30318 2016-10-03 A spherical-plot solution to linking acceleration metrics with animal performance, state, behaviour and lifestyle 017bc6dd155098860945dc6249c4e9bc 0000-0003-3177-0177 Rory Wilson Rory Wilson true false 0e1d89d0cc934a740dcd0a873aed178e 0000-0001-8834-3283 Mark Holton Mark Holton true false 54729295145aa1ea56d176818d51ed6a 0000-0001-7325-6398 Emily Shepard Emily Shepard true false 062f5697ff59f004bc8c713955988398 0000-0003-0813-7477 Melitta McNarry Melitta McNarry true false bdb20e3f31bcccf95c7bc116070c4214 0000-0003-0355-6357 Kelly Mackintosh Kelly Mackintosh true false 2e1030b6e14fc9debd5d5ae7cc335562 0000-0001-8991-1190 Mark Jones Mark Jones true false 70a544476ce62ba78502ce463c2500d6 0000-0003-0710-0947 Michael Gravenor Michael Gravenor true false 2016-10-03 SBI Background We are increasingly using recording devices with multiple sensors operating at high frequencies to produce large volumes of data which are problematic to interpret. A particularly challenging example comes from studies on animals and humans where researchers use animal-attached accelerometers on moving subjects to attempt to quantify behaviour, energy expenditure and condition. Results The approach taken effectively concatenated three complex lines of acceleration into one visualization that highlighted patterns that were otherwise not obvious. The summation of data points within sphere facets and presentation into histograms on the sphere surface effectively dealt with data occlusion. Further frequency binning of data within facets and representation of these bins as discs on spines radiating from the sphere allowed patterns in dynamic body accelerations (DBA) associated with different postures to become obvious. Method We examine the extent to which novel, gravity-based spherical plots can produce revealing visualizations to incorporate the complexity of such multidimensional acceleration data using a suite of different acceleration-derived metrics with a view to highlighting patterns that are not obvious using current approaches. The basis for the visualisation involved three-dimensional plots of the smoothed acceleration values, which then occupied points on the surface of a sphere. This sphere was divided into facets and point density within each facet expressed as a histogram. Within each facet-dependent histogram, data were also grouped into frequency bins of any desirable parameters, most particularly dynamic body acceleration (DBA), which were then presented as discs on a central spine radiating from the facet. Greater radial distances from the sphere surface indicated greater DBA values while greater disc diameter indicated larger numbers of data points with that particular value. Conclusions We indicate how this approach links behaviour and proxies for energetics and can inform our identification and understanding of movement-related processes, highlighting subtle differences in movement and its associated energetics. This approach has ramifications that should expand to areas as disparate as disease identification, lifestyle, sports practice and wild animal ecology. Journal Article Movement Ecology 4 1 2051-3933 23 9 2016 2016-09-23 10.1186/s40462-016-0088-3 COLLEGE NANME Biosciences COLLEGE CODE SBI Swansea University 2020-07-21T11:40:10.6772704 2016-10-03T15:16:14.8577062 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Rory Wilson 0000-0003-3177-0177 1 Mark Holton 0000-0001-8834-3283 2 James S. Walker 3 Emily Shepard 0000-0001-7325-6398 4 D. Mike Scantlebury 5 Vianney L. Wilson 6 Gwendoline I. Wilson 7 Brenda Tysse 8 Mike Gravenor 9 Javier Ciancio 10 Melitta McNarry 0000-0003-0813-7477 11 Kelly Mackintosh 0000-0003-0355-6357 12 Lama Qasem 13 Frank Rosell 14 Patricia M. Graf 15 Flavio Quintana 16 Agustina Gomez-Laich 17 Juan-Emilio Sala 18 Christina C. Mulvenna 19 Nicola J. Marks 20 Mark Jones 0000-0001-8991-1190 21 Michael Gravenor 0000-0003-0710-0947 22 30318__3868__ead8c2973b2a4991ae53cfc1503e2241.pdf gSpheres2016.pdf 2016-10-03T15:18:33.3400000 Output 2505091 application/pdf Version of Record true 2016-10-03T00:00:00.0000000 © 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated true |
| title |
A spherical-plot solution to linking acceleration metrics with animal performance, state, behaviour and lifestyle |
| spellingShingle |
A spherical-plot solution to linking acceleration metrics with animal performance, state, behaviour and lifestyle Rory Wilson Mark Holton Emily Shepard Melitta McNarry Kelly Mackintosh Mark Jones Michael Gravenor |
| title_short |
A spherical-plot solution to linking acceleration metrics with animal performance, state, behaviour and lifestyle |
| title_full |
A spherical-plot solution to linking acceleration metrics with animal performance, state, behaviour and lifestyle |
| title_fullStr |
A spherical-plot solution to linking acceleration metrics with animal performance, state, behaviour and lifestyle |
| title_full_unstemmed |
A spherical-plot solution to linking acceleration metrics with animal performance, state, behaviour and lifestyle |
| title_sort |
A spherical-plot solution to linking acceleration metrics with animal performance, state, behaviour and lifestyle |
| author_id_str_mv |
017bc6dd155098860945dc6249c4e9bc 0e1d89d0cc934a740dcd0a873aed178e 54729295145aa1ea56d176818d51ed6a 062f5697ff59f004bc8c713955988398 bdb20e3f31bcccf95c7bc116070c4214 2e1030b6e14fc9debd5d5ae7cc335562 70a544476ce62ba78502ce463c2500d6 |
| author_id_fullname_str_mv |
017bc6dd155098860945dc6249c4e9bc_***_Rory Wilson 0e1d89d0cc934a740dcd0a873aed178e_***_Mark Holton 54729295145aa1ea56d176818d51ed6a_***_Emily Shepard 062f5697ff59f004bc8c713955988398_***_Melitta McNarry bdb20e3f31bcccf95c7bc116070c4214_***_Kelly Mackintosh 2e1030b6e14fc9debd5d5ae7cc335562_***_Mark Jones 70a544476ce62ba78502ce463c2500d6_***_Michael Gravenor |
| author |
Rory Wilson Mark Holton Emily Shepard Melitta McNarry Kelly Mackintosh Mark Jones Michael Gravenor |
| author2 |
Rory Wilson Mark Holton James S. Walker Emily Shepard D. Mike Scantlebury Vianney L. Wilson Gwendoline I. Wilson Brenda Tysse Mike Gravenor Javier Ciancio Melitta McNarry Kelly Mackintosh Lama Qasem Frank Rosell Patricia M. Graf Flavio Quintana Agustina Gomez-Laich Juan-Emilio Sala Christina C. Mulvenna Nicola J. Marks Mark Jones Michael Gravenor |
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Movement Ecology |
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4 |
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2051-3933 |
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10.1186/s40462-016-0088-3 |
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Faculty of Science and Engineering |
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facultyofscienceandengineering |
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Faculty of Science and Engineering |
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School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science |
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Background We are increasingly using recording devices with multiple sensors operating at high frequencies to produce large volumes of data which are problematic to interpret. A particularly challenging example comes from studies on animals and humans where researchers use animal-attached accelerometers on moving subjects to attempt to quantify behaviour, energy expenditure and condition. Results The approach taken effectively concatenated three complex lines of acceleration into one visualization that highlighted patterns that were otherwise not obvious. The summation of data points within sphere facets and presentation into histograms on the sphere surface effectively dealt with data occlusion. Further frequency binning of data within facets and representation of these bins as discs on spines radiating from the sphere allowed patterns in dynamic body accelerations (DBA) associated with different postures to become obvious. Method We examine the extent to which novel, gravity-based spherical plots can produce revealing visualizations to incorporate the complexity of such multidimensional acceleration data using a suite of different acceleration-derived metrics with a view to highlighting patterns that are not obvious using current approaches. The basis for the visualisation involved three-dimensional plots of the smoothed acceleration values, which then occupied points on the surface of a sphere. This sphere was divided into facets and point density within each facet expressed as a histogram. Within each facet-dependent histogram, data were also grouped into frequency bins of any desirable parameters, most particularly dynamic body acceleration (DBA), which were then presented as discs on a central spine radiating from the facet. Greater radial distances from the sphere surface indicated greater DBA values while greater disc diameter indicated larger numbers of data points with that particular value. Conclusions We indicate how this approach links behaviour and proxies for energetics and can inform our identification and understanding of movement-related processes, highlighting subtle differences in movement and its associated energetics. This approach has ramifications that should expand to areas as disparate as disease identification, lifestyle, sports practice and wild animal ecology. |
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2016-09-23T03:37:00Z |
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11.012678 |