Detecting Body Mass Index from a Facial Photograph in Lifestyle Intervention
This study aimed to identify whether a research participant’s body-mass index (BMI) can be correctly identified from their facial image (photograph) in order to improve data capturing in dissemination and implementation research. Facial BMI (fBMI) was measured using an algorithm formulated...
Ausführliche Beschreibung
Autor*in: |
Makenzie L. Barr [verfasserIn] Guodong Guo [verfasserIn] Sarah E. Colby [verfasserIn] Melissa D. Olfert [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2018 |
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Übergeordnetes Werk: |
In: Technologies - MDPI AG, 2014, 6(2018), 3, p 83 |
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Übergeordnetes Werk: |
volume:6 ; year:2018 ; number:3, p 83 |
Links: |
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DOI / URN: |
10.3390/technologies6030083 |
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Katalog-ID: |
DOAJ040370275 |
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10.3390/technologies6030083 doi (DE-627)DOAJ040370275 (DE-599)DOAJd435af0ce7b1467c91dfe31998b52f82 DE-627 ger DE-627 rakwb eng Makenzie L. Barr verfasserin aut Detecting Body Mass Index from a Facial Photograph in Lifestyle Intervention 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study aimed to identify whether a research participant’s body-mass index (BMI) can be correctly identified from their facial image (photograph) in order to improve data capturing in dissemination and implementation research. Facial BMI (fBMI) was measured using an algorithm formulated to identify points on each enrolled participant’s face from a photograph. Once facial landmarks were detected, distances and ratios between them were computed to characterize facial fatness. A regression function was then used to represent the relationship between facial measures and BMI values to then calculate fBMI from each photo image. Simultaneously, BMI was physically measured (mBMI) by trained researchers, calculated as weight in kilograms divided by height in meters squared (adult BMI). Correlation analysis of fBMI to mBMI (n = 1210) showed significant correlation between fBMI and BMIs in normal and overweight categories (p < 0.0001). Further analysis indicated fBMI to be less accurate in underweight and obese participants. Matched pair data for each individual indicated that fBMI identified participant BMI an average of 0.4212 less than mBMI (p < 0.0007). Contingency table analysis found 109 participants in the ‘obese’ category of mBMI were positioned into a lower category for fBMI. Facial imagery is a viable measure for dissemination of human research; however, further testing to sensitize fBMI measures for underweight and obese individuals are necessary. Body Mass Index (BMI) facial image BMI prediction young adults Technology T Guodong Guo verfasserin aut Sarah E. Colby verfasserin aut Melissa D. Olfert verfasserin aut In Technologies MDPI AG, 2014 6(2018), 3, p 83 (DE-627)736557288 (DE-600)2703026-X 22277080 nnns volume:6 year:2018 number:3, p 83 https://doi.org/10.3390/technologies6030083 kostenfrei https://doaj.org/article/d435af0ce7b1467c91dfe31998b52f82 kostenfrei http://www.mdpi.com/2227-7080/6/3/83 kostenfrei https://doaj.org/toc/2227-7080 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 3, p 83 |
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10.3390/technologies6030083 doi (DE-627)DOAJ040370275 (DE-599)DOAJd435af0ce7b1467c91dfe31998b52f82 DE-627 ger DE-627 rakwb eng Makenzie L. Barr verfasserin aut Detecting Body Mass Index from a Facial Photograph in Lifestyle Intervention 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study aimed to identify whether a research participant’s body-mass index (BMI) can be correctly identified from their facial image (photograph) in order to improve data capturing in dissemination and implementation research. Facial BMI (fBMI) was measured using an algorithm formulated to identify points on each enrolled participant’s face from a photograph. Once facial landmarks were detected, distances and ratios between them were computed to characterize facial fatness. A regression function was then used to represent the relationship between facial measures and BMI values to then calculate fBMI from each photo image. Simultaneously, BMI was physically measured (mBMI) by trained researchers, calculated as weight in kilograms divided by height in meters squared (adult BMI). Correlation analysis of fBMI to mBMI (n = 1210) showed significant correlation between fBMI and BMIs in normal and overweight categories (p < 0.0001). Further analysis indicated fBMI to be less accurate in underweight and obese participants. Matched pair data for each individual indicated that fBMI identified participant BMI an average of 0.4212 less than mBMI (p < 0.0007). Contingency table analysis found 109 participants in the ‘obese’ category of mBMI were positioned into a lower category for fBMI. Facial imagery is a viable measure for dissemination of human research; however, further testing to sensitize fBMI measures for underweight and obese individuals are necessary. Body Mass Index (BMI) facial image BMI prediction young adults Technology T Guodong Guo verfasserin aut Sarah E. Colby verfasserin aut Melissa D. Olfert verfasserin aut In Technologies MDPI AG, 2014 6(2018), 3, p 83 (DE-627)736557288 (DE-600)2703026-X 22277080 nnns volume:6 year:2018 number:3, p 83 https://doi.org/10.3390/technologies6030083 kostenfrei https://doaj.org/article/d435af0ce7b1467c91dfe31998b52f82 kostenfrei http://www.mdpi.com/2227-7080/6/3/83 kostenfrei https://doaj.org/toc/2227-7080 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 3, p 83 |
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10.3390/technologies6030083 doi (DE-627)DOAJ040370275 (DE-599)DOAJd435af0ce7b1467c91dfe31998b52f82 DE-627 ger DE-627 rakwb eng Makenzie L. Barr verfasserin aut Detecting Body Mass Index from a Facial Photograph in Lifestyle Intervention 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study aimed to identify whether a research participant’s body-mass index (BMI) can be correctly identified from their facial image (photograph) in order to improve data capturing in dissemination and implementation research. Facial BMI (fBMI) was measured using an algorithm formulated to identify points on each enrolled participant’s face from a photograph. Once facial landmarks were detected, distances and ratios between them were computed to characterize facial fatness. A regression function was then used to represent the relationship between facial measures and BMI values to then calculate fBMI from each photo image. Simultaneously, BMI was physically measured (mBMI) by trained researchers, calculated as weight in kilograms divided by height in meters squared (adult BMI). Correlation analysis of fBMI to mBMI (n = 1210) showed significant correlation between fBMI and BMIs in normal and overweight categories (p < 0.0001). Further analysis indicated fBMI to be less accurate in underweight and obese participants. Matched pair data for each individual indicated that fBMI identified participant BMI an average of 0.4212 less than mBMI (p < 0.0007). Contingency table analysis found 109 participants in the ‘obese’ category of mBMI were positioned into a lower category for fBMI. Facial imagery is a viable measure for dissemination of human research; however, further testing to sensitize fBMI measures for underweight and obese individuals are necessary. Body Mass Index (BMI) facial image BMI prediction young adults Technology T Guodong Guo verfasserin aut Sarah E. Colby verfasserin aut Melissa D. Olfert verfasserin aut In Technologies MDPI AG, 2014 6(2018), 3, p 83 (DE-627)736557288 (DE-600)2703026-X 22277080 nnns volume:6 year:2018 number:3, p 83 https://doi.org/10.3390/technologies6030083 kostenfrei https://doaj.org/article/d435af0ce7b1467c91dfe31998b52f82 kostenfrei http://www.mdpi.com/2227-7080/6/3/83 kostenfrei https://doaj.org/toc/2227-7080 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 3, p 83 |
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10.3390/technologies6030083 doi (DE-627)DOAJ040370275 (DE-599)DOAJd435af0ce7b1467c91dfe31998b52f82 DE-627 ger DE-627 rakwb eng Makenzie L. Barr verfasserin aut Detecting Body Mass Index from a Facial Photograph in Lifestyle Intervention 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study aimed to identify whether a research participant’s body-mass index (BMI) can be correctly identified from their facial image (photograph) in order to improve data capturing in dissemination and implementation research. Facial BMI (fBMI) was measured using an algorithm formulated to identify points on each enrolled participant’s face from a photograph. Once facial landmarks were detected, distances and ratios between them were computed to characterize facial fatness. A regression function was then used to represent the relationship between facial measures and BMI values to then calculate fBMI from each photo image. Simultaneously, BMI was physically measured (mBMI) by trained researchers, calculated as weight in kilograms divided by height in meters squared (adult BMI). Correlation analysis of fBMI to mBMI (n = 1210) showed significant correlation between fBMI and BMIs in normal and overweight categories (p < 0.0001). Further analysis indicated fBMI to be less accurate in underweight and obese participants. Matched pair data for each individual indicated that fBMI identified participant BMI an average of 0.4212 less than mBMI (p < 0.0007). Contingency table analysis found 109 participants in the ‘obese’ category of mBMI were positioned into a lower category for fBMI. Facial imagery is a viable measure for dissemination of human research; however, further testing to sensitize fBMI measures for underweight and obese individuals are necessary. Body Mass Index (BMI) facial image BMI prediction young adults Technology T Guodong Guo verfasserin aut Sarah E. Colby verfasserin aut Melissa D. Olfert verfasserin aut In Technologies MDPI AG, 2014 6(2018), 3, p 83 (DE-627)736557288 (DE-600)2703026-X 22277080 nnns volume:6 year:2018 number:3, p 83 https://doi.org/10.3390/technologies6030083 kostenfrei https://doaj.org/article/d435af0ce7b1467c91dfe31998b52f82 kostenfrei http://www.mdpi.com/2227-7080/6/3/83 kostenfrei https://doaj.org/toc/2227-7080 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 3, p 83 |
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10.3390/technologies6030083 doi (DE-627)DOAJ040370275 (DE-599)DOAJd435af0ce7b1467c91dfe31998b52f82 DE-627 ger DE-627 rakwb eng Makenzie L. Barr verfasserin aut Detecting Body Mass Index from a Facial Photograph in Lifestyle Intervention 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study aimed to identify whether a research participant’s body-mass index (BMI) can be correctly identified from their facial image (photograph) in order to improve data capturing in dissemination and implementation research. Facial BMI (fBMI) was measured using an algorithm formulated to identify points on each enrolled participant’s face from a photograph. Once facial landmarks were detected, distances and ratios between them were computed to characterize facial fatness. A regression function was then used to represent the relationship between facial measures and BMI values to then calculate fBMI from each photo image. Simultaneously, BMI was physically measured (mBMI) by trained researchers, calculated as weight in kilograms divided by height in meters squared (adult BMI). Correlation analysis of fBMI to mBMI (n = 1210) showed significant correlation between fBMI and BMIs in normal and overweight categories (p < 0.0001). Further analysis indicated fBMI to be less accurate in underweight and obese participants. Matched pair data for each individual indicated that fBMI identified participant BMI an average of 0.4212 less than mBMI (p < 0.0007). Contingency table analysis found 109 participants in the ‘obese’ category of mBMI were positioned into a lower category for fBMI. Facial imagery is a viable measure for dissemination of human research; however, further testing to sensitize fBMI measures for underweight and obese individuals are necessary. Body Mass Index (BMI) facial image BMI prediction young adults Technology T Guodong Guo verfasserin aut Sarah E. Colby verfasserin aut Melissa D. Olfert verfasserin aut In Technologies MDPI AG, 2014 6(2018), 3, p 83 (DE-627)736557288 (DE-600)2703026-X 22277080 nnns volume:6 year:2018 number:3, p 83 https://doi.org/10.3390/technologies6030083 kostenfrei https://doaj.org/article/d435af0ce7b1467c91dfe31998b52f82 kostenfrei http://www.mdpi.com/2227-7080/6/3/83 kostenfrei https://doaj.org/toc/2227-7080 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 3, p 83 |
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Detecting Body Mass Index from a Facial Photograph in Lifestyle Intervention |
abstract |
This study aimed to identify whether a research participant’s body-mass index (BMI) can be correctly identified from their facial image (photograph) in order to improve data capturing in dissemination and implementation research. Facial BMI (fBMI) was measured using an algorithm formulated to identify points on each enrolled participant’s face from a photograph. Once facial landmarks were detected, distances and ratios between them were computed to characterize facial fatness. A regression function was then used to represent the relationship between facial measures and BMI values to then calculate fBMI from each photo image. Simultaneously, BMI was physically measured (mBMI) by trained researchers, calculated as weight in kilograms divided by height in meters squared (adult BMI). Correlation analysis of fBMI to mBMI (n = 1210) showed significant correlation between fBMI and BMIs in normal and overweight categories (p < 0.0001). Further analysis indicated fBMI to be less accurate in underweight and obese participants. Matched pair data for each individual indicated that fBMI identified participant BMI an average of 0.4212 less than mBMI (p < 0.0007). Contingency table analysis found 109 participants in the ‘obese’ category of mBMI were positioned into a lower category for fBMI. Facial imagery is a viable measure for dissemination of human research; however, further testing to sensitize fBMI measures for underweight and obese individuals are necessary. |
abstractGer |
This study aimed to identify whether a research participant’s body-mass index (BMI) can be correctly identified from their facial image (photograph) in order to improve data capturing in dissemination and implementation research. Facial BMI (fBMI) was measured using an algorithm formulated to identify points on each enrolled participant’s face from a photograph. Once facial landmarks were detected, distances and ratios between them were computed to characterize facial fatness. A regression function was then used to represent the relationship between facial measures and BMI values to then calculate fBMI from each photo image. Simultaneously, BMI was physically measured (mBMI) by trained researchers, calculated as weight in kilograms divided by height in meters squared (adult BMI). Correlation analysis of fBMI to mBMI (n = 1210) showed significant correlation between fBMI and BMIs in normal and overweight categories (p < 0.0001). Further analysis indicated fBMI to be less accurate in underweight and obese participants. Matched pair data for each individual indicated that fBMI identified participant BMI an average of 0.4212 less than mBMI (p < 0.0007). Contingency table analysis found 109 participants in the ‘obese’ category of mBMI were positioned into a lower category for fBMI. Facial imagery is a viable measure for dissemination of human research; however, further testing to sensitize fBMI measures for underweight and obese individuals are necessary. |
abstract_unstemmed |
This study aimed to identify whether a research participant’s body-mass index (BMI) can be correctly identified from their facial image (photograph) in order to improve data capturing in dissemination and implementation research. Facial BMI (fBMI) was measured using an algorithm formulated to identify points on each enrolled participant’s face from a photograph. Once facial landmarks were detected, distances and ratios between them were computed to characterize facial fatness. A regression function was then used to represent the relationship between facial measures and BMI values to then calculate fBMI from each photo image. Simultaneously, BMI was physically measured (mBMI) by trained researchers, calculated as weight in kilograms divided by height in meters squared (adult BMI). Correlation analysis of fBMI to mBMI (n = 1210) showed significant correlation between fBMI and BMIs in normal and overweight categories (p < 0.0001). Further analysis indicated fBMI to be less accurate in underweight and obese participants. Matched pair data for each individual indicated that fBMI identified participant BMI an average of 0.4212 less than mBMI (p < 0.0007). Contingency table analysis found 109 participants in the ‘obese’ category of mBMI were positioned into a lower category for fBMI. Facial imagery is a viable measure for dissemination of human research; however, further testing to sensitize fBMI measures for underweight and obese individuals are necessary. |
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