Coding infant engagement in the Face-to-Face Still-Face paradigm using deep neural networks

Background: The Face-to-Face Still-Face (FFSF) task is a validated and commonly used observational measure of mother-infant socio-emotional interactions. With the ascendence of deep learning-based facial emotion recognition, it is possible that common complex tasks, such as the coding of FFSF videos...
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Autor*in:	Faltyn, Mateusz [verfasserIn] Krzeczkowski, John E. [verfasserIn] Cummings, Mike [verfasserIn] Anwar, Samia [verfasserIn] Zeng, Tammy [verfasserIn] Zahid, Isra [verfasserIn] Ntow, Kwadjo Otu-Boateng [verfasserIn] Van Lieshout, Ryan J. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Machine learning Deep neural networks Face-to-Face Still-Face Task Developmental psychology Perinatal psychiatry

Übergeordnetes Werk:	Enthalten in: Infant behavior and development - Amsterdam [u.a.] : Elsevier Science, 1978, 71
Übergeordnetes Werk:	volume:71

DOI / URN:	10.1016/j.infbeh.2023.101827

Katalog-ID:	ELV010231390

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100	1		\|a Faltyn, Mateusz \|e verfasserin \|0 (orcid)0000-0002-7276-3029 \|4 aut
245	1	0	\|a Coding infant engagement in the Face-to-Face Still-Face paradigm using deep neural networks
264		1	\|c 2023
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520			\|a Background: The Face-to-Face Still-Face (FFSF) task is a validated and commonly used observational measure of mother-infant socio-emotional interactions. With the ascendence of deep learning-based facial emotion recognition, it is possible that common complex tasks, such as the coding of FFSF videos, could be coded with a high degree of accuracy by deep neural networks (DNNs). The primary objective of this study was to test the accuracy of four DNN image classification models against the coding of infant engagement conducted by two trained independent manual raters.Methods: 68 mother-infant dyads completed the FFSF task at three timepoints. Two trained independent raters undertook second-by-second manual coding of infant engagement into one of four classes: 1) positive affect, 2) neutral affect, 3) object/environment engagement, and 4) negative affect.Results: Training four different DNN models on 40,000 images, we achieved a maximum accuracy of 99.5% on image classification of infant frames taken from recordings of the FFSF task with a maximum inter-rater reliability (Cohen's κ-value) of 0.993.Limitations: This study inherits all sampling and experimental limitations of the original study from which the data was taken, namely a relatively small and primarily White sample.Conclusions: Based on the extremely high classification accuracy, these findings suggest that DNNs could be used to code infant engagement in FFSF recordings. DNN image classification models may also have the potential to improve the efficiency of coding all observational tasks with applications across multiple fields of human behavior research.
650		4	\|a Machine learning
650		4	\|a Deep neural networks
650		4	\|a Face-to-Face Still-Face Task
650		4	\|a Developmental psychology
650		4	\|a Perinatal psychiatry
700	1		\|a Krzeczkowski, John E. \|e verfasserin \|4 aut
700	1		\|a Cummings, Mike \|e verfasserin \|4 aut
700	1		\|a Anwar, Samia \|e verfasserin \|4 aut
700	1		\|a Zeng, Tammy \|e verfasserin \|4 aut
700	1		\|a Zahid, Isra \|e verfasserin \|4 aut
700	1		\|a Ntow, Kwadjo Otu-Boateng \|e verfasserin \|4 aut
700	1		\|a Van Lieshout, Ryan J. \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Infant behavior and development \|d Amsterdam [u.a.] : Elsevier Science, 1978 \|g 71 \|h Online-Ressource \|w (DE-627)320465349 \|w (DE-600)2007808-0 \|w (DE-576)098614940 \|x 1934-8800 \|7 nnns
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936	b	k	\|a 77.00 \|j Psychologie: Allgemeines \|q VZ
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allfields	10.1016/j.infbeh.2023.101827 doi (DE-627)ELV010231390 (ELSEVIER)S0163-6383(23)00019-X DE-627 ger DE-627 rda eng 150 VZ 77.00 bkl Faltyn, Mateusz verfasserin (orcid)0000-0002-7276-3029 aut Coding infant engagement in the Face-to-Face Still-Face paradigm using deep neural networks 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: The Face-to-Face Still-Face (FFSF) task is a validated and commonly used observational measure of mother-infant socio-emotional interactions. With the ascendence of deep learning-based facial emotion recognition, it is possible that common complex tasks, such as the coding of FFSF videos, could be coded with a high degree of accuracy by deep neural networks (DNNs). The primary objective of this study was to test the accuracy of four DNN image classification models against the coding of infant engagement conducted by two trained independent manual raters.Methods: 68 mother-infant dyads completed the FFSF task at three timepoints. Two trained independent raters undertook second-by-second manual coding of infant engagement into one of four classes: 1) positive affect, 2) neutral affect, 3) object/environment engagement, and 4) negative affect.Results: Training four different DNN models on 40,000 images, we achieved a maximum accuracy of 99.5% on image classification of infant frames taken from recordings of the FFSF task with a maximum inter-rater reliability (Cohen's κ-value) of 0.993.Limitations: This study inherits all sampling and experimental limitations of the original study from which the data was taken, namely a relatively small and primarily White sample.Conclusions: Based on the extremely high classification accuracy, these findings suggest that DNNs could be used to code infant engagement in FFSF recordings. DNN image classification models may also have the potential to improve the efficiency of coding all observational tasks with applications across multiple fields of human behavior research. Machine learning Deep neural networks Face-to-Face Still-Face Task Developmental psychology Perinatal psychiatry Krzeczkowski, John E. verfasserin aut Cummings, Mike verfasserin aut Anwar, Samia verfasserin aut Zeng, Tammy verfasserin aut Zahid, Isra verfasserin aut Ntow, Kwadjo Otu-Boateng verfasserin aut Van Lieshout, Ryan J. verfasserin aut Enthalten in Infant behavior and development Amsterdam [u.a.] : Elsevier Science, 1978 71 Online-Ressource (DE-627)320465349 (DE-600)2007808-0 (DE-576)098614940 1934-8800 nnns volume:71 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 77.00 Psychologie: Allgemeines VZ AR 71
spelling	10.1016/j.infbeh.2023.101827 doi (DE-627)ELV010231390 (ELSEVIER)S0163-6383(23)00019-X DE-627 ger DE-627 rda eng 150 VZ 77.00 bkl Faltyn, Mateusz verfasserin (orcid)0000-0002-7276-3029 aut Coding infant engagement in the Face-to-Face Still-Face paradigm using deep neural networks 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: The Face-to-Face Still-Face (FFSF) task is a validated and commonly used observational measure of mother-infant socio-emotional interactions. With the ascendence of deep learning-based facial emotion recognition, it is possible that common complex tasks, such as the coding of FFSF videos, could be coded with a high degree of accuracy by deep neural networks (DNNs). The primary objective of this study was to test the accuracy of four DNN image classification models against the coding of infant engagement conducted by two trained independent manual raters.Methods: 68 mother-infant dyads completed the FFSF task at three timepoints. Two trained independent raters undertook second-by-second manual coding of infant engagement into one of four classes: 1) positive affect, 2) neutral affect, 3) object/environment engagement, and 4) negative affect.Results: Training four different DNN models on 40,000 images, we achieved a maximum accuracy of 99.5% on image classification of infant frames taken from recordings of the FFSF task with a maximum inter-rater reliability (Cohen's κ-value) of 0.993.Limitations: This study inherits all sampling and experimental limitations of the original study from which the data was taken, namely a relatively small and primarily White sample.Conclusions: Based on the extremely high classification accuracy, these findings suggest that DNNs could be used to code infant engagement in FFSF recordings. DNN image classification models may also have the potential to improve the efficiency of coding all observational tasks with applications across multiple fields of human behavior research. Machine learning Deep neural networks Face-to-Face Still-Face Task Developmental psychology Perinatal psychiatry Krzeczkowski, John E. verfasserin aut Cummings, Mike verfasserin aut Anwar, Samia verfasserin aut Zeng, Tammy verfasserin aut Zahid, Isra verfasserin aut Ntow, Kwadjo Otu-Boateng verfasserin aut Van Lieshout, Ryan J. verfasserin aut Enthalten in Infant behavior and development Amsterdam [u.a.] : Elsevier Science, 1978 71 Online-Ressource (DE-627)320465349 (DE-600)2007808-0 (DE-576)098614940 1934-8800 nnns volume:71 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 77.00 Psychologie: Allgemeines VZ AR 71
allfields_unstemmed	10.1016/j.infbeh.2023.101827 doi (DE-627)ELV010231390 (ELSEVIER)S0163-6383(23)00019-X DE-627 ger DE-627 rda eng 150 VZ 77.00 bkl Faltyn, Mateusz verfasserin (orcid)0000-0002-7276-3029 aut Coding infant engagement in the Face-to-Face Still-Face paradigm using deep neural networks 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: The Face-to-Face Still-Face (FFSF) task is a validated and commonly used observational measure of mother-infant socio-emotional interactions. With the ascendence of deep learning-based facial emotion recognition, it is possible that common complex tasks, such as the coding of FFSF videos, could be coded with a high degree of accuracy by deep neural networks (DNNs). The primary objective of this study was to test the accuracy of four DNN image classification models against the coding of infant engagement conducted by two trained independent manual raters.Methods: 68 mother-infant dyads completed the FFSF task at three timepoints. Two trained independent raters undertook second-by-second manual coding of infant engagement into one of four classes: 1) positive affect, 2) neutral affect, 3) object/environment engagement, and 4) negative affect.Results: Training four different DNN models on 40,000 images, we achieved a maximum accuracy of 99.5% on image classification of infant frames taken from recordings of the FFSF task with a maximum inter-rater reliability (Cohen's κ-value) of 0.993.Limitations: This study inherits all sampling and experimental limitations of the original study from which the data was taken, namely a relatively small and primarily White sample.Conclusions: Based on the extremely high classification accuracy, these findings suggest that DNNs could be used to code infant engagement in FFSF recordings. DNN image classification models may also have the potential to improve the efficiency of coding all observational tasks with applications across multiple fields of human behavior research. Machine learning Deep neural networks Face-to-Face Still-Face Task Developmental psychology Perinatal psychiatry Krzeczkowski, John E. verfasserin aut Cummings, Mike verfasserin aut Anwar, Samia verfasserin aut Zeng, Tammy verfasserin aut Zahid, Isra verfasserin aut Ntow, Kwadjo Otu-Boateng verfasserin aut Van Lieshout, Ryan J. verfasserin aut Enthalten in Infant behavior and development Amsterdam [u.a.] : Elsevier Science, 1978 71 Online-Ressource (DE-627)320465349 (DE-600)2007808-0 (DE-576)098614940 1934-8800 nnns volume:71 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 77.00 Psychologie: Allgemeines VZ AR 71
allfieldsGer	10.1016/j.infbeh.2023.101827 doi (DE-627)ELV010231390 (ELSEVIER)S0163-6383(23)00019-X DE-627 ger DE-627 rda eng 150 VZ 77.00 bkl Faltyn, Mateusz verfasserin (orcid)0000-0002-7276-3029 aut Coding infant engagement in the Face-to-Face Still-Face paradigm using deep neural networks 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: The Face-to-Face Still-Face (FFSF) task is a validated and commonly used observational measure of mother-infant socio-emotional interactions. With the ascendence of deep learning-based facial emotion recognition, it is possible that common complex tasks, such as the coding of FFSF videos, could be coded with a high degree of accuracy by deep neural networks (DNNs). The primary objective of this study was to test the accuracy of four DNN image classification models against the coding of infant engagement conducted by two trained independent manual raters.Methods: 68 mother-infant dyads completed the FFSF task at three timepoints. Two trained independent raters undertook second-by-second manual coding of infant engagement into one of four classes: 1) positive affect, 2) neutral affect, 3) object/environment engagement, and 4) negative affect.Results: Training four different DNN models on 40,000 images, we achieved a maximum accuracy of 99.5% on image classification of infant frames taken from recordings of the FFSF task with a maximum inter-rater reliability (Cohen's κ-value) of 0.993.Limitations: This study inherits all sampling and experimental limitations of the original study from which the data was taken, namely a relatively small and primarily White sample.Conclusions: Based on the extremely high classification accuracy, these findings suggest that DNNs could be used to code infant engagement in FFSF recordings. DNN image classification models may also have the potential to improve the efficiency of coding all observational tasks with applications across multiple fields of human behavior research. Machine learning Deep neural networks Face-to-Face Still-Face Task Developmental psychology Perinatal psychiatry Krzeczkowski, John E. verfasserin aut Cummings, Mike verfasserin aut Anwar, Samia verfasserin aut Zeng, Tammy verfasserin aut Zahid, Isra verfasserin aut Ntow, Kwadjo Otu-Boateng verfasserin aut Van Lieshout, Ryan J. verfasserin aut Enthalten in Infant behavior and development Amsterdam [u.a.] : Elsevier Science, 1978 71 Online-Ressource (DE-627)320465349 (DE-600)2007808-0 (DE-576)098614940 1934-8800 nnns volume:71 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 77.00 Psychologie: Allgemeines VZ AR 71
allfieldsSound	10.1016/j.infbeh.2023.101827 doi (DE-627)ELV010231390 (ELSEVIER)S0163-6383(23)00019-X DE-627 ger DE-627 rda eng 150 VZ 77.00 bkl Faltyn, Mateusz verfasserin (orcid)0000-0002-7276-3029 aut Coding infant engagement in the Face-to-Face Still-Face paradigm using deep neural networks 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: The Face-to-Face Still-Face (FFSF) task is a validated and commonly used observational measure of mother-infant socio-emotional interactions. With the ascendence of deep learning-based facial emotion recognition, it is possible that common complex tasks, such as the coding of FFSF videos, could be coded with a high degree of accuracy by deep neural networks (DNNs). The primary objective of this study was to test the accuracy of four DNN image classification models against the coding of infant engagement conducted by two trained independent manual raters.Methods: 68 mother-infant dyads completed the FFSF task at three timepoints. Two trained independent raters undertook second-by-second manual coding of infant engagement into one of four classes: 1) positive affect, 2) neutral affect, 3) object/environment engagement, and 4) negative affect.Results: Training four different DNN models on 40,000 images, we achieved a maximum accuracy of 99.5% on image classification of infant frames taken from recordings of the FFSF task with a maximum inter-rater reliability (Cohen's κ-value) of 0.993.Limitations: This study inherits all sampling and experimental limitations of the original study from which the data was taken, namely a relatively small and primarily White sample.Conclusions: Based on the extremely high classification accuracy, these findings suggest that DNNs could be used to code infant engagement in FFSF recordings. DNN image classification models may also have the potential to improve the efficiency of coding all observational tasks with applications across multiple fields of human behavior research. Machine learning Deep neural networks Face-to-Face Still-Face Task Developmental psychology Perinatal psychiatry Krzeczkowski, John E. verfasserin aut Cummings, Mike verfasserin aut Anwar, Samia verfasserin aut Zeng, Tammy verfasserin aut Zahid, Isra verfasserin aut Ntow, Kwadjo Otu-Boateng verfasserin aut Van Lieshout, Ryan J. verfasserin aut Enthalten in Infant behavior and development Amsterdam [u.a.] : Elsevier Science, 1978 71 Online-Ressource (DE-627)320465349 (DE-600)2007808-0 (DE-576)098614940 1934-8800 nnns volume:71 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 77.00 Psychologie: Allgemeines VZ AR 71
language	English
source	Enthalten in Infant behavior and development 71 volume:71
sourceStr	Enthalten in Infant behavior and development 71 volume:71
format_phy_str_mv	Article
bklname	Psychologie: Allgemeines
institution	findex.gbv.de
topic_facet	Machine learning Deep neural networks Face-to-Face Still-Face Task Developmental psychology Perinatal psychiatry
dewey-raw	150
isfreeaccess_bool	false
container_title	Infant behavior and development
authorswithroles_txt_mv	Faltyn, Mateusz @@aut@@ Krzeczkowski, John E. @@aut@@ Cummings, Mike @@aut@@ Anwar, Samia @@aut@@ Zeng, Tammy @@aut@@ Zahid, Isra @@aut@@ Ntow, Kwadjo Otu-Boateng @@aut@@ Van Lieshout, Ryan J. @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320465349
dewey-sort	3150
id	ELV010231390
language_de	englisch
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Two trained independent raters undertook second-by-second manual coding of infant engagement into one of four classes: 1) positive affect, 2) neutral affect, 3) object/environment engagement, and 4) negative affect.Results: Training four different DNN models on 40,000 images, we achieved a maximum accuracy of 99.5% on image classification of infant frames taken from recordings of the FFSF task with a maximum inter-rater reliability (Cohen's κ-value) of 0.993.Limitations: This study inherits all sampling and experimental limitations of the original study from which the data was taken, namely a relatively small and primarily White sample.Conclusions: Based on the extremely high classification accuracy, these findings suggest that DNNs could be used to code infant engagement in FFSF recordings. 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spellingShingle	Faltyn, Mateusz ddc 150 bkl 77.00 misc Machine learning misc Deep neural networks misc Face-to-Face Still-Face Task misc Developmental psychology misc Perinatal psychiatry Coding infant engagement in the Face-to-Face Still-Face paradigm using deep neural networks
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topic_title	150 VZ 77.00 bkl Coding infant engagement in the Face-to-Face Still-Face paradigm using deep neural networks Machine learning Deep neural networks Face-to-Face Still-Face Task Developmental psychology Perinatal psychiatry
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title	Coding infant engagement in the Face-to-Face Still-Face paradigm using deep neural networks
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title_full	Coding infant engagement in the Face-to-Face Still-Face paradigm using deep neural networks
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author_browse	Faltyn, Mateusz Krzeczkowski, John E. Cummings, Mike Anwar, Samia Zeng, Tammy Zahid, Isra Ntow, Kwadjo Otu-Boateng Van Lieshout, Ryan J.
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title_sort	coding infant engagement in the face-to-face still-face paradigm using deep neural networks
title_auth	Coding infant engagement in the Face-to-Face Still-Face paradigm using deep neural networks
abstract	Background: The Face-to-Face Still-Face (FFSF) task is a validated and commonly used observational measure of mother-infant socio-emotional interactions. With the ascendence of deep learning-based facial emotion recognition, it is possible that common complex tasks, such as the coding of FFSF videos, could be coded with a high degree of accuracy by deep neural networks (DNNs). The primary objective of this study was to test the accuracy of four DNN image classification models against the coding of infant engagement conducted by two trained independent manual raters.Methods: 68 mother-infant dyads completed the FFSF task at three timepoints. Two trained independent raters undertook second-by-second manual coding of infant engagement into one of four classes: 1) positive affect, 2) neutral affect, 3) object/environment engagement, and 4) negative affect.Results: Training four different DNN models on 40,000 images, we achieved a maximum accuracy of 99.5% on image classification of infant frames taken from recordings of the FFSF task with a maximum inter-rater reliability (Cohen's κ-value) of 0.993.Limitations: This study inherits all sampling and experimental limitations of the original study from which the data was taken, namely a relatively small and primarily White sample.Conclusions: Based on the extremely high classification accuracy, these findings suggest that DNNs could be used to code infant engagement in FFSF recordings. DNN image classification models may also have the potential to improve the efficiency of coding all observational tasks with applications across multiple fields of human behavior research.
abstractGer	Background: The Face-to-Face Still-Face (FFSF) task is a validated and commonly used observational measure of mother-infant socio-emotional interactions. With the ascendence of deep learning-based facial emotion recognition, it is possible that common complex tasks, such as the coding of FFSF videos, could be coded with a high degree of accuracy by deep neural networks (DNNs). The primary objective of this study was to test the accuracy of four DNN image classification models against the coding of infant engagement conducted by two trained independent manual raters.Methods: 68 mother-infant dyads completed the FFSF task at three timepoints. Two trained independent raters undertook second-by-second manual coding of infant engagement into one of four classes: 1) positive affect, 2) neutral affect, 3) object/environment engagement, and 4) negative affect.Results: Training four different DNN models on 40,000 images, we achieved a maximum accuracy of 99.5% on image classification of infant frames taken from recordings of the FFSF task with a maximum inter-rater reliability (Cohen's κ-value) of 0.993.Limitations: This study inherits all sampling and experimental limitations of the original study from which the data was taken, namely a relatively small and primarily White sample.Conclusions: Based on the extremely high classification accuracy, these findings suggest that DNNs could be used to code infant engagement in FFSF recordings. DNN image classification models may also have the potential to improve the efficiency of coding all observational tasks with applications across multiple fields of human behavior research.
abstract_unstemmed	Background: The Face-to-Face Still-Face (FFSF) task is a validated and commonly used observational measure of mother-infant socio-emotional interactions. With the ascendence of deep learning-based facial emotion recognition, it is possible that common complex tasks, such as the coding of FFSF videos, could be coded with a high degree of accuracy by deep neural networks (DNNs). The primary objective of this study was to test the accuracy of four DNN image classification models against the coding of infant engagement conducted by two trained independent manual raters.Methods: 68 mother-infant dyads completed the FFSF task at three timepoints. Two trained independent raters undertook second-by-second manual coding of infant engagement into one of four classes: 1) positive affect, 2) neutral affect, 3) object/environment engagement, and 4) negative affect.Results: Training four different DNN models on 40,000 images, we achieved a maximum accuracy of 99.5% on image classification of infant frames taken from recordings of the FFSF task with a maximum inter-rater reliability (Cohen's κ-value) of 0.993.Limitations: This study inherits all sampling and experimental limitations of the original study from which the data was taken, namely a relatively small and primarily White sample.Conclusions: Based on the extremely high classification accuracy, these findings suggest that DNNs could be used to code infant engagement in FFSF recordings. DNN image classification models may also have the potential to improve the efficiency of coding all observational tasks with applications across multiple fields of human behavior research.
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title_short	Coding infant engagement in the Face-to-Face Still-Face paradigm using deep neural networks
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author2	Krzeczkowski, John E. Cummings, Mike Anwar, Samia Zeng, Tammy Zahid, Isra Ntow, Kwadjo Otu-Boateng Van Lieshout, Ryan J.
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up_date	2024-07-06T17:16:43.889Z
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With the ascendence of deep learning-based facial emotion recognition, it is possible that common complex tasks, such as the coding of FFSF videos, could be coded with a high degree of accuracy by deep neural networks (DNNs). The primary objective of this study was to test the accuracy of four DNN image classification models against the coding of infant engagement conducted by two trained independent manual raters.Methods: 68 mother-infant dyads completed the FFSF task at three timepoints. Two trained independent raters undertook second-by-second manual coding of infant engagement into one of four classes: 1) positive affect, 2) neutral affect, 3) object/environment engagement, and 4) negative affect.Results: Training four different DNN models on 40,000 images, we achieved a maximum accuracy of 99.5% on image classification of infant frames taken from recordings of the FFSF task with a maximum inter-rater reliability (Cohen's κ-value) of 0.993.Limitations: This study inherits all sampling and experimental limitations of the original study from which the data was taken, namely a relatively small and primarily White sample.Conclusions: Based on the extremely high classification accuracy, these findings suggest that DNNs could be used to code infant engagement in FFSF recordings. 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Coding infant engagement in the Face-to-Face Still-Face paradigm using deep neural networks

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