A method for the assessment of facial hedonic reactions in newborns

Ayres, Caroline; Ferreira, Charles Francisco; Bernardi, Juliana Rombaldi; Marcelino, Thiago Beltram; Hirakata, Vânia Naomi; Silva, Clécio Homrich da; Goldani, Marcelo Zubarán

doi:10.1016/j.jped.2016.06.011

Cited by 5 publications

(9 citation statements)

References 29 publications

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“…Recently, however, researchers [3,4,7,[22][23][24][25][26][27][28] have used much more convenient and accurate automated facial expression recognition systems, including sophisticated artificial network systems such as FaceReader or iMotions software (iMotions, Inc., Copenhagen, Denmark), which can classify facial expressions into the following basic universal human emotions suggested by Ekman and Friesen [29], with intensity ranging from 0 to 1: happy, sad, angry, surprised, scared, disgusted, and neutral. Analyses of these emotions are utilized effectively in various experimental situations in food research, e.g., a time course of changes in each emotion after tasting breakfast drinks [7], a comparison between implicit (spontaneous) and explicit (intentional) facial expressions after tasting different juices [22], a comparison between before and after sensory-specific satiety [24], a correlation between hedonic liking and facial expressions [27], a comparison of facial expressions in response to different tastes between persons with and without depressive disorders [4], and differences in responses to various food stimuli between Asians and Western populations [3,26].…”

Section: Plos Onementioning

confidence: 99%

“…Therefore, in this study, we aimed to analyze facial expressions with the aid of artificial intelligence (AI) technology to examine whether facial expressions can be used as a method for rating food and beverage hedonics. Essentially, the same approach has recently been assessed by several researchers in different fields of food research [3,4,[22][23][24][25][26][27][28]. Most researchers have used an automated facial expression recognition system (computer software with built-in information about changes in human facial expressions to different emotions) such as FaceReader (Noldus Information Technology, Wageningen, The Netherlands) to analyze facial expressions in response to various food and beverage stimuli under different experimental conditions using basic expressions of human emotions, such as happiness, anger, sadness, and disgust.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of facial expressions in response to basic taste stimuli using artificial intelligence to predict perceived hedonic ratings

2021

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Taste stimuli can induce a variety of physiological reactions depending on the quality and/or hedonics (overall pleasure) of tastants, for which objective methods have long been desired. In this study, we used artificial intelligence (AI) technology to analyze facial expressions with the aim of assessing its utility as an objective method for the evaluation of food and beverage hedonics compared with conventional subjective (perceived) evaluation methods. The face of each participant (10 females; age range, 21–22 years) was photographed using a smartphone camera a few seconds after drinking 10 different solutions containing five basic tastes with different hedonic tones. Each image was then uploaded to an AI application to achieve outcomes for eight emotions (surprise, happiness, fear, neutral, disgust, sadness, anger, and embarrassment), with scores ranging from 0 to 100. For perceived evaluations, each participant also rated the hedonics of each solution from –10 (extremely unpleasant) to +10 (extremely pleasant). Based on these, we then conducted a multiple linear regression analysis to obtain a formula to predict perceived hedonic ratings. The applicability of the formula was examined by combining the emotion scores with another 11 taste solutions obtained from another 12 participants of both genders (age range, 22–59 years). The predicted hedonic ratings showed good correlation and concordance with the perceived ratings. To our knowledge, this is the first study to demonstrate a model that enables the prediction of hedonic ratings based on emotional facial expressions to food and beverage stimuli.

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Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of facial expressions in response to basic taste stimuli using artificial intelligence to predict perceived hedonic ratings

2021

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“…A previous study has analyzed videotaped facial reactions in human infants and non-human infant/adult primates during the ingestion of liquids of various tastes, and found that tongue protrusions and gapes to sucrose and quinine, respectively, were elicited universally across species [21]. Studies consistently observed mouth motions in response to sucrose solution in human infants [22][23][24][25][26] and adults [27,28]. These data suggest that mouth movements related to consumption could be modulated by hedonic experiences during tasting.…”

Section: Introductionmentioning

confidence: 88%

Brow and Masticatory Muscle Activity Senses Subjective Hedonic Experiences during Food Consumption

et al. 2021

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Sensing subjective hedonic or emotional experiences during eating using physiological activity is practically and theoretically important. A recent psychophysiological study has reported that facial electromyography (EMG) measured from the corrugator supercilii muscles was negatively associated with hedonic ratings, including liking, wanting, and valence, during the consumption of solid foods. However, the study protocol prevented participants from natural mastication (crushing of food between the teeth) during physiological data acquisition, which could hide associations between hedonic experiences and masticatory muscle activity during natural eating. We investigated this issue by assessing participants’ subjective ratings (liking, wanting, valence, and arousal) and recording physiological measures, including EMG of the corrugator supercilii, zygomatic major, masseter, and suprahyoid muscles while they consumed gel-type solid foods (water-based gellan gum jellies) of diverse flavors. Ratings of liking, wanting, and valence were negatively correlated with corrugator supercilii EMG and positively correlated with masseter and suprahyoid EMG. These findings imply that subjective hedonic experiences during food consumption can be sensed using EMG signals from the brow and masticatory muscles.

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“…However, due to the absence of thresholds in the literature for a technical error of measurement in orofacial measurements, we chose to use the Bland-Altman graph (27) to evaluate possible discrepancies. However, it was observed that of the analyzed variables, the discards were justified on 1 or 2 occasions in each variable, resulting in at least 95.6% of the observations with reliability.…”

Section: Methodsmentioning

confidence: 99%

Medidas antropométricas orofaciais em recém-nascidos a termo

Medeiros

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Santi

et al. 2019

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RESUMO Objetivo Descrever e comparar as medidas antropométricas e as proporções orofaciais de recém-nascidos (RNs) a termo saudáveis, segundo o sexo, de uma maternidade pública do estado de Sergipe, Nordeste do Brasil. Método Estudo randomizado descritivo e analítico. Participaram 46 RNs a termo e saudáveis, de ambos os sexos, selecionados aleatoriamente. Foi utilizado paquímetro digital para tomadas das medidas (em milímetros), com os RNs em posição supina e lábios ocluídos. Quatro pesquisadores foram previamente treinados, com cada RN sendo medido duas vezes por uma mesma dupla destes. Os dados foram descritos por meio de frequências simples e percentuais. As diferenças de média foram avaliadas através do Teste de Mann-Whitney, com significância de 5%. Associações foram avaliadas através do teste Exato de Fisher. Foram calculados tamanhos de efeitos D de Cohen. Resultados Foram obtidas diferenças entre os grupos para as medidas antropométricas: terços da face médio (glabela-subnasal ou g-sn) e inferior (subnasal-gnátio ou sn-gn); e alturas do filtro (subnasal-labial superior ou sn-ls), dos lábios superior (subnasal-estômio ou sn-sto) e inferior (estômio-gnátio ou sto-gn), sempre maiores no sexo masculino. As proporções orofaciais estudadas não apresentaram diferenças entre sexos. Conclusão O estudo apontou presença de disformismo sexual para as medidas da face ao nascimento na população nascida em Aracaju, Sergipe.

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A method for the assessment of facial hedonic reactions in newborns

Abstract: These results confirm that the proposed method has an efficient power to detect significant differences between neutral and sucrose stimuli. In conclusion, this evaluation method of hedonic facial reactions in newborns reflects the response to a specific taste.

Cited by 5 publications

References 29 publications

Analysis of facial expressions in response to basic taste stimuli using artificial intelligence to predict perceived hedonic ratings

Analysis of facial expressions in response to basic taste stimuli using artificial intelligence to predict perceived hedonic ratings

Brow and Masticatory Muscle Activity Senses Subjective Hedonic Experiences during Food Consumption

Medidas antropométricas orofaciais em recém-nascidos a termo

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