Validation of deep learning-based markerless 3D pose estimation.
Deep learning-based approaches to markerless 3D pose estimation are being adopted by researchers in psychology and neuroscience at an unprecedented rate. Yet many of these tools remain unvalidated. Here, we report on the validation of one increasingly popular tool (DeepLabCut) against simultaneous m...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0276258 |
| _version_ | 1811329509185552384 |
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| author | Veronika Kosourikhina Diarmuid Kavanagh Michael J Richardson David M Kaplan |
| author_facet | Veronika Kosourikhina Diarmuid Kavanagh Michael J Richardson David M Kaplan |
| author_sort | Veronika Kosourikhina |
| collection | DOAJ |
| description | Deep learning-based approaches to markerless 3D pose estimation are being adopted by researchers in psychology and neuroscience at an unprecedented rate. Yet many of these tools remain unvalidated. Here, we report on the validation of one increasingly popular tool (DeepLabCut) against simultaneous measurements obtained from a reference measurement system (Fastrak) with well-known performance characteristics. Our results confirm close (mm range) agreement between the two, indicating that under specific circumstances deep learning-based approaches can match more traditional motion tracking methods. Although more work needs to be done to determine their specific performance characteristics and limitations, this study should help build confidence within the research community using these new tools. |
| first_indexed | 2024-04-13T15:46:24Z |
| format | Article |
| id | doaj.art-618b6e639b40439fb2b2dbf3666e33a6 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-13T15:46:24Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-618b6e639b40439fb2b2dbf3666e33a62022-12-22T02:40:59ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011710e027625810.1371/journal.pone.0276258Validation of deep learning-based markerless 3D pose estimation.Veronika KosourikhinaDiarmuid KavanaghMichael J RichardsonDavid M KaplanDeep learning-based approaches to markerless 3D pose estimation are being adopted by researchers in psychology and neuroscience at an unprecedented rate. Yet many of these tools remain unvalidated. Here, we report on the validation of one increasingly popular tool (DeepLabCut) against simultaneous measurements obtained from a reference measurement system (Fastrak) with well-known performance characteristics. Our results confirm close (mm range) agreement between the two, indicating that under specific circumstances deep learning-based approaches can match more traditional motion tracking methods. Although more work needs to be done to determine their specific performance characteristics and limitations, this study should help build confidence within the research community using these new tools.https://doi.org/10.1371/journal.pone.0276258 |
| spellingShingle | Veronika Kosourikhina Diarmuid Kavanagh Michael J Richardson David M Kaplan Validation of deep learning-based markerless 3D pose estimation. PLoS ONE |
| title | Validation of deep learning-based markerless 3D pose estimation. |
| title_full | Validation of deep learning-based markerless 3D pose estimation. |
| title_fullStr | Validation of deep learning-based markerless 3D pose estimation. |
| title_full_unstemmed | Validation of deep learning-based markerless 3D pose estimation. |
| title_short | Validation of deep learning-based markerless 3D pose estimation. |
| title_sort | validation of deep learning based markerless 3d pose estimation |
| url | https://doi.org/10.1371/journal.pone.0276258 |
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