This project was funded, in part, by ConAgra Foods (Omaha, NE, USA), the Virginia Agricultural Experiment Station, the Hatch Program of the National Institute of Food and Agriculture, U.S. Department of Agriculture, and the Virginia Tech Water INTERface Interdisciplinary Graduate Education Program.

<ol>
	<li>De Wijk, R. A., Kooijman, V., Verhoeven, R. H. G., Holthuysen, N. T. E., De Graaf, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Autonomic+nervous+system+responses+on+and+facial+expressions+to+the+sight,+smell,+and+taste+of+liked+and+disliked+foods.">Autonomic nervous system responses on and facial expressions to the sight, smell, and taste of liked and disliked foods.</a> Food Qual Prefer. 26 (2), 196-203 (2012).</li><li>De Wijk, R. A., He, W., Mensink, M. G. J., Verhoeven, R. H. G., De Graaf, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ANS+responses+and+facial+expression+differentiate+between+the+taste+of+commercial+breakfast+drinks.">ANS responses and facial expression differentiate between the taste of commercial breakfast drinks.</a> PLoS ONE. 9 (4), 1-9 (2014).</li><li>He, W., Boesveldt, S., De Graaf, C., De Wijk, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Behavioural+and+physiological+responses+to+two+food+odours.">Behavioural and physiological responses to two food odours.</a> Appetite. 59 (2), 628 (2012).</li><li>He, W., Boesveldt, S., De Graaf, C., De Wijk, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamics+of+autonomic+nervous+system+responses+and+facial+expressions+to+odors.">Dynamics of autonomic nervous system responses and facial expressions to odors.</a> Front Psychol. 5 (110), 1-8 (2014).</li><li>Danner, L., Sidorkina, L., Joechl, M., Duerrschmid, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Make+a+face!+Implicit+and+explicit+measurement+of+facial+expressions+elicited+by+orange+juices+using+face+reading+technology.">Make a face! Implicit and explicit measurement of facial expressions elicited by orange juices using face reading technology.</a> Food Qual Prefer. 32 (2014), 167-172 (2013).</li><li>Danner, L., Haindl, S., Joechl, M., Duerrschmid, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Facial+expression+and+autonomous+nervous+system+responses+elicited+by+tasting+different+juices.">Facial expression and autonomous nervous system responses elicited by tasting different juices.</a> Food Res Int. 64 (2014), 81-90 (2014).</li><li>Arnade, E. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Measuring consumer emotional response to tastes and foods through facial expression analysis [thesis]. , 1-187 (2013).</li><li>Leitch, K. A., Duncan, S. E., O'Keefe, S., Rudd, R., Gallagher, D. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Characterizing+consumer+emotional+response+to+sweeteners+using+an+emotion+terminology+questionnaire+and+facial+expression+analysis.">Characterizing consumer emotional response to sweeteners using an emotion terminology questionnaire and facial expression analysis.</a> Food Res Int. 76, 283-292 (2015).</li><li>Crist, C. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Application+of+emotional+facial+analysis+technology+to+consumer+acceptability+using+a+basic+tastes+model.">Application of emotional facial analysis technology to consumer acceptability using a basic tastes model.</a> , (2014).</li><li>Garcia-Burgos, D., Zamora, M. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Facial+affective+reactions+to+bitter-tasting+foods+and+body+mass+index+in+adults.">Facial affective reactions to bitter-tasting foods and body mass index in adults.</a> Appetite. 71 (2013), 178-186 (2013).</li><li>Garcia-Burgos, D., Zamora, M. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exploring+the+hedonic+and+incentive+properties+in+preferences+for+bitter+foods+via+self-reports,+facial+expressions+and+instrumental+behaviours.">Exploring the hedonic and incentive properties in preferences for bitter foods via self-reports, facial expressions and instrumental behaviours.</a> Food Qual Prefer. 39 (2015), 73-81 (2015).</li><li>Lewinski, P., Fransen, M. L., Tan, E. S. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predicting+advertising+effectiveness+by+facial+expressions+in+response+to+amusing+persuasive+stimuli.">Predicting advertising effectiveness by facial expressions in response to amusing persuasive stimuli.</a> J. Neurosci. Psychol. Econ. 7 (1), 1-14 (2014).</li><li>Ekman, P., Friesen, W. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Facial+action+coding+system:+A+technique+for+the+measurement+of+facial+movement.">Facial action coding system: A technique for the measurement of facial movement.</a> , (1978).</li><li>Viola, P., Jones, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapid+object+detection+using+a+boosted+cascade+of+simple+features.">Rapid object detection using a boosted cascade of simple features.</a> Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern. 1, 511-518 (2001).</li><li>Sung, K. K., Poggio, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Example-based+learning+for+view-based+human+face+detection.">Example-based learning for view-based human face detection.</a> IEEE Trans. Pattern Anal. Mach. Intell. 20 (1), 39-51 (1998).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> FaceReader 5&#8482; Technical Specifications. , (2014).</li><li>Cootes, T., Taylor, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Statistical models of appearance for computer vision: Technical report. , (2000).</li><li>Bishop, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Neural networks for pattern recognition. , (1995).</li><li>Lewinski, P., den Uyl, T. M., Butler, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+facial+coding:+validation+of+basic+emotions+and+FACS+AUs+in+FaceReader.">Automated facial coding: validation of basic emotions and FACS AUs in FaceReader.</a> J. Neurosci. Psychol. Econ. 7 (4), 227-236 (2014).</li><li>Noldus Information Technology. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> FaceReader Reference Manual Version 6. , (2014).</li><li>Alves, N. T., Fukusima, S. S., Aznar-Casanova, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Models+of+brain+asymmetry+in+emotional+processing.">Models of brain asymmetry in emotional processing.</a> Psychol Neurosci. 1 (1), 63-66 (2008).</li><li>Costello, M., Clark, S., Clark, S., Costello, M., Drake, M., Bodyfelt, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Preparation+of+samples+for+instructing+students+and+staff+in+dairy+products+evaluation+(Appendix+F).">Preparation of samples for instructing students and staff in dairy products evaluation (Appendix F).</a> The sensory evaluation of dairy foods. , 551-560 (2009).</li><li>Porcherot, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+do+you+feel+when+you+smell+this?+Optimization+of+a+verbal+measurement+of+odor-elicited+emotions.">How do you feel when you smell this? Optimization of a verbal measurement of odor-elicited emotions.</a> Food Qual Prefer. 21, 938-947 (2010).</li><li>Warrenburg, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+fragrance+on+emotions:+Moods+and+physiology.">Effects of fragrance on emotions: Moods and physiology.</a> Chem. Senses. 30, i248-i249 (2005).</li><li>Bredie, W. L. P., Tan, H. S. G., Wendin, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparative+study+on+facially+expressed+emotions+in+response+to+basic+tastes.">A comparative study on facially expressed emotions in response to basic tastes.</a> Chem. Percept. 7 (1), 1-9 (2014).</li><li>Wendin, K., Allesen-Holm, B. H., Bredie, L. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Do+facial+reactions+add+new+dimensions+to+measuring+sensory+responses+to+basic+tastes?.">Do facial reactions add new dimensions to measuring sensory responses to basic tastes?.</a> Food Qual Prefer. 22, 346-354 (2011).</li><li>Rosenstein, D., Oster, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Differential+facial+responses+to+four+basic+tastes+in+newborns.">Differential facial responses to four basic tastes in newborns.</a> Child Dev. 59 (6), 1555-1568 (1988).</li><li>Rosenstein, D., Oster, H., P, E. k. m. a. n., E, R. o. s. e. n. b. e. r. g. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Differential+facial+responses+to+four+basic+tastes+in+newborns.">Differential facial responses to four basic tastes in newborns.</a> What the face reveals: Basic and applied studies of spontaneous expression using the facial action coding system (FACS). , 302-327 (1997).</li><li>Rozin, P., Fallon, A. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+perspective+on+disgust.">A perspective on disgust.</a> Psychol. Rev. 94 (1), 23-41 (1987).</li><li>Delarue, J., Blumenthal, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Temporal+aspects+of+consumer+preferences.">Temporal aspects of consumer preferences.</a> Curr. Opin. Food Sci. 3, 41-46 (2015).</li><li>Sudre, J., Pineau, N., Loret, C., Marin, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+methods+to+monitor+liking+of+food+during+consumption.">Comparison of methods to monitor liking of food during consumption.</a> Food Qual Prefer. 24 (1), 179-189 (2012).</li><li>Weiland, R., Ellgring, H., Macht, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gustofacial+and+olfactofacial+responses+in+human+adults.">Gustofacial and olfactofacial responses in human adults.</a> Chem. Senses. 35 (9), 841-853 (2010).</li><li>Ekman, P., Cole, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Universal+and+cultural+differences+in+facial+expressions+of+emotion.">Universal and cultural differences in facial expressions of emotion.</a> Nebraska symposium on motivation. , 207-283 (1971).</li><li>Griemel, E., Macht, M., Krumhuber, E., Ellgring, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Facial+and+affective+reactions+to+tastes+and+their+modulation+by+sadness+and+joy.">Facial and affective reactions to tastes and their modulation by sadness and joy.</a> Physiol Behav. 89 (2), 261-269 (2006).</li></ol>

The authors have nothing to disclose.

Yiyecek ve içecek ile ilgili literatürde AFEA uygulama 1-11 derece sınırlıdır. Gıda uygulama yöntemi ve veri yorumlama kurmak için bir fırsat yaratmak, yeni. Arnade (2013) 7 çikolatalı süt ve eğri analizi ve varyans analizi altındaki alanı kullanarak beyaz süt bireysel duygusal tepki arasında yüksek bireysel değişkenlik bulundu. Üzgün ​​ve tiksinti kısa zaman yanıtı 7 varken Ancak, hatta katılımcı değişkenliği ile katılımcılar uzun mutlu bir tepki o...

Otomatik yüz ifadesi analizi (AFEA) içecek ve yiyeceklere duygusal tepkiler karakterize etmek için bir potansiyel analitik bir araçtır. Mevcut duyusal bilim metodolojileri, gıda değerlendirme uygulamaları ve hedonik skala değerlendirmesi için fazladan bir boyut ekleyebilirsiniz Duygusal analiz genellikle araştırma ve endüstri ortamlarında ikisi de kullanılır. Duygusal analiz yiyecek ve içeceklere daha doğru bir tepki ortaya ek bir metrik sağlayabilir. Reaksiyonları 1 kayıt hatası nedeniyle Hedonik puanlama katılımcı önyargı içerebilir. AFEA araştırma bilgisayar oyunları, kullanıcı davranışı, eğitim / pedagoji ve empati ve aldatma üzerine psikoloji çalışmaları da dahil olmak üzere birçok araştırma uygulamalarında kullanılmaktadır. Çoğu gıda ile ilişkili araştırma gıda kalitesi ve gıda ile insan davranışları duygusal tepki karakterize odaklanmıştır. Gıda davranışları içgörüler kazanıyor son trend ile, edebiyat raporları büyüyen bir vücut Afea kullanımıyiyecek, içecek ve koku 1-12 ile bağlantılı insan duygusal tepki karakterize etmek için. AFEA Yüz Hareket Kodlama Sistemi (FACS) elde edilir. Yüz eylem kodlama sistemi (FACS) 5 puanlık bir yoğunluk ölçeğinde 13 eylem birimlerinin (AUS) ile karakterize yüz hareketleri ayrımcılık. FACS yaklaşım, eğitimli yorum uzmanları, manuel kodlama, önemli değerlendirme zamanı gerektirir ve sınırlı veri analizi seçenekleri sağlar. AFEA duyguları belirlemek için hızlı bir değerlendirme yöntemi olarak geliştirilmiştir. AFEA yazılım duygusal tepki 14-18 karakterize etmek yüz kas hareketi, yüz veritabanları ve algoritmalar kullanır. Bu çalışmada kullanılan AFEA yazılım Duygusal Yüz İfade Resimleri Varşova Set (WSEFEP) ve 0.70 standart bir anlaşmaya yakın (ADFES) Set Amsterdam Dinamik Yüz İfade, hem de ortalama 0.67 anlaşmanın bir &quot;FACS endeksi ulaştı manuel kodlama &quot;19 için </sup&gt;. analize dahil evrensel duygular 0 ile 1 arasında ayrı bir ölçekte (her mutlu (pozitif), (negatif) üzgün, tiksinti (negatif), sürpriz (pozitif veya negatif), kızgın (negatif), korkmuş (negatif) ve nötr 0 ifade edilmese = 1; = tamamen) 20 dile getirdi. Buna ek olarak, psikoloji literatürü korkmuş ve (uzak caydırıcı uyaranlara) 21 &quot;çekilme&quot; duyguların olarak tiksinti, mutlu şaşırttı ve öfkeli &quot;yaklaşımı&quot; duyguların olarak (uyaranlara doğru) ve üzgün içerir. gıdalar ile ilişkili duyguları tanımlamak için geçerli AFEA yazılımı biri sınırlama çiğneme ve yutma yanı sıra aşırı baş hareketleri gibi diğer kaba motor hareketleri ile ilişkili yüz hareketlerinden müdahaledir. Yazılım yüzünde 16,17 üzerinde 500&#39;den fazla kas noktaları dayalı küçük yüz kas hareketleri, ilgili pozisyon ve hareket derecesi, hedefler. Çiğneme hareketleri ifadeleri sınıflandırılması müdahale. Bu sınırtirme sıvılaştırılmış gıdalar kullanarak ele alınabilir. Ancak, diğer yöntem zorluklar da veri toplama çevre, teknoloji, araştırmacı talimatları, katılımcı davranış ve katılımcı öznitelikleri gibi video duyarlılık ve AFEA analizi azaltabilir. Standart bir metodoloji geliştirdi ve optimum video yakalama ve duyusal değerlendirme laboratuar ortamında yiyecek ve içeceklerin duygusal tepki için Afea kullanarak veri analizi için doğrulanmadı. Pek çok açıdan aydınlatma, katılımcı yön, katılımcı davranış, katılımcı yüksekliği, yanı sıra, kamera yüksekliği, kamera olta balıkçılığı ve ekipman ayarları nedeniyle gölgeleme, ışıklandırma dahil video yakalama ortamı etkileyebilir. Ayrıca, veri analizi metodolojileri tutarsız ve duygusal tepki değerlendirmek için standart bir metodoloji eksikliği. Burada, (içecekler kullanılarak anlamlı sonuçlara duygusal verileri ve işlem verileri yakalamak için bizim standart işletim prosedürü gösterecektiraromalı süt, değerlendirme için tatlandırılmamış süt ve tatlandırılmamış su). Bildiğimiz kadarıyla tek hakemli yayın için laboratuar grubundan, duygular analizi 8 veri yorumlama için zaman serileri kullanmıştır; Ancak, yöntem, bizim sunulan yöntem için güncellendi. Amacımız duyusal değerlendirme laboratuar ortamında tekrarlanabilirliği ile yardımcı olmak için geliştirilmiş ve tutarlı bir metodoloji geliştirmektir. gösteri için, çalışma modelinin amacı AFEA aromalı süt, tatlandırılmamış süt ve tatlandırılmamış su geleneksel hedonik kabul edilebilirlik değerlendirmesi ek eğer değerlendirmektir. Bu video protokolü niyeti, AFEA metodolojisi kurulmasına yardımcı bir duyusal değerlendirme laboratuvarda video yakalama kriterlerini (duyusal stand ayarı) standardize ve nüfusun zamansal duygusal veri analizi için bir yöntem göstermektir.

<table><tbody><tr><td>2% Reduced Fat Milk</td><td>Kroger Brand, Cincinnati, OH or DZA Brands, LLC, Salisbury, NC</td><td>na</td><td>for solutions</td></tr><tr><td>Drinking Water</td><td>Kroger Brand, Cincinnati, OH</td><td>na</td><td>for solutions</td></tr><tr><td>Imitation Clear Vanilla Flavor</td><td>Kroger Brand, Cincinnati, OH</td><td>na</td><td>for solutions</td></tr><tr><td>Iodized Salt</td><td>Kroger Brand, Cincinnati, OH</td><td>na</td><td>for solutions</td></tr><tr><td>FaceReader 6</td><td>Noldus Information Technology, Wageningen, The Netherlands</td><td>na</td><td>For Facial Analysis</td></tr><tr><td>Sensory Information Management System (SIMS) 2000</td><td>Sensory Computer Systems, Berkeley Heights, NJ</td><td>Version 6</td><td>For Sensory Data Capture</td></tr><tr><td>Rhapsody</td><td>Acuity Brands Lighting, Inc., Conyers, GA</td><td></td><td>For Environment Illumination</td></tr><tr><td>R Version&amp;nbsp;</td><td>R Core Team 2015</td><td>3.1.1</td><td>For Statistical Analysis</td></tr><tr><td>Microsoft Office</td><td>Microsoft</td><td>na</td><td>For Statistical Analysis</td></tr><tr><td>JMP</td><td>Statistical Analysis Software (SAS) Version 9.2, SAS Institute, Cary, NC</td><td>na</td><td>For Statistical Analysis</td></tr><tr><td>Media Recorder 2.5</td><td>Noldus Information Technology, Wageningen, The Netherlands</td><td>na</td><td>For capturing participants sensory evaluation</td></tr><tr><td>Axis M1054 Camera</td><td>Axis Communications, Lund, Sweden</td><td>na</td><td></td></tr><tr><td>Beverage</td><td></td><td>na</td><td>Beverage or soft food for evaluation</td></tr></tbody></table>

protocol for data collection and analysis applied to automated facial expression analysis technology and temporal analysis for sensory evaluation

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial expression analysis (AFEA) software. Additionally, we demonstrate a method for extracting relevant emotional data output and plotting the emotional response of a population over a specified time frame. By time pairing each participant's treatment response to a control stimulus (baseline), the overall emotional response over time and across multiple participants can be quantified. AFEA is a prospective analytical tool for assessing unbiased response to food and beverages. At present, most research has mainly focused on beverages. Methodologies and analyses have not yet been standardized for the application of AFEA to beverages and foods; however, a consistent standard methodology is needed. Optimizing video capture procedures and resulting video quality aids in a successful collection of emotional response to foods. Furthermore, the methodology of data analysis is novel for extracting the pertinent data relevant to the emotional response. The combinations of video capture optimization and data analysis will aid in standardizing the protocol for automated facial expression analysis and interpretation of emotional response data.

yöntem AFEA veri toplama için standart bir protokol önermektedir. Önerilen protokol adımları takip edilirse, kullanılamaz duygusal veri çıkışı zayıf veri toplama kaynaklanan (Şekil 1) (Şekil 2: A; Sol Resim) sınırlı olabilir. Bu kötü veriler (Şekil 1) olduğu gibi günlük dosyaları (.txt) ağırlıklı olarak &quot;FIND_FAILED&quot; &quot;FIT_FAILED&quot; ve içeriyorsa Zaman serileri analizi kullanılan olamaz. Ay...

Watch this Scientific Journal Video about Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation at JoVE.com

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Otomatik yüz ifadesi analizi yazılımı kullanılarak bir duyusal değerlendirme laboratuvarda içecekler ve sıvılaştırılmış gıdalar bir nüfusun duygusal tepki yakalama ve istatistiksel analiz etmek için bir protokol tarif edilmektedir.

Duyusal için Veri Toplama ve Otomatik Yüz İfade Analizi Teknoloji Analizi Uygulamalı ve Geçici Analiz Protokolü

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial ...

protocol-for-data-collection-analysis-applied-to-automated-facial

Universidad San Sebastian

Research

JoVE Journal

Behavior

9.3K Görüntüleme.  Virginia Tech.  Otomatik yüz ifadesi analizi yazılımı kullanılarak bir duyusal değerlendirme laboratuvarda içecekler ve sıvılaştırılmış gıdalar bir nüfusun duygusal tepki yakalama ve istatistiksel analiz etmek için bir protokol tarif edilmektedir. 

Video: Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis (ALS)

Improved methods for assessing bulbar impairment are necessary for expediting diagnosis of bulbar dysfunction in ALS, for predicting disease progression across speech subsystems, and for addressing the critical need for sensitive outcome measures for ongoing experimental treatment trials. To address this need, we are obtaining longitudinal profiles of bulbar impairment in 100 individuals based on a comprehensive instrumentation-based assessment that yield objective measures. Using instrumental approaches to quantify speech-related behaviors is very important in a field that has primarily relied on subjective, auditory-perceptual forms of speech assessment1. Our assessment protocol measures performance across all of the speech subsystems, which include respiratory, phonatory (laryngeal), resonatory (velopharyngeal), and articulatory. The articulatory subsystem is divided into the facial components (jaw and lip), and the tongue. Prior research has suggested that each speech subsystem responds differently to neurological diseases such as ALS. The current protocol is designed to test the performance of each speech subsystem as independently from other subsystems as possible. The speech subsystems are evaluated in the context of more global changes to speech performance. These speech system level variables include speaking rate and intelligibility of speech.
The protocol requires specialized instrumentation, and commercial and custom software. The respiratory, phonatory, and resonatory subsystems are evaluated using pressure-flow (aerodynamic) and acoustic methods. The articulatory subsystem is assessed using 3D motion tracking techniques. The objective measures that are used to quantify bulbar impairment have been well established in the speech literature and show sensitivity to changes in bulbar function with disease progression. The result of the assessment is a comprehensive, across-subsystem performance profile for each participant. The profile, when compared to the same measures obtained from healthy controls, is used for diagnostic purposes. Currently, we are testing the sensitivity and specificity of these measures for diagnosis of ALS and for predicting the rate of disease progression. In the long term, the more refined endophenotype of bulbar ALS derived from this work is expected to strengthen future efforts to identify the genetic loci of ALS and improve diagnostic and treatment specificity of the disease as a whole. The objective assessment that is demonstrated in this video may be used to assess a broad range of speech motor impairments, including those related to stroke, traumatic brain injury, multiple sclerosis, and Parkinson disease.

A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis ALS

Objective assessments of the physiological mechanisms that support speech are needed to monitor disease onset and progression in persons with ALS and to quantify treatment effects in clinical trials. In this video, we present a comprehensive, instrumentation-based protocol for quantifying speech motor performance in clinical populations.

Amyotrophic Lateral Sclerosis bulber Disfonksiyon Kapsamlı Değerlendirmesi (ALS) için Protokol

Improved methods for assessing bulbar impairment are necessary for expediting diagnosis of bulbar dysfunction in ALS, for predicting disease progression ...

Tıp

Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach

Mouse is the preferred model organism for testing drugs designed to increase sociability. We present a method to quantify mouse sociability in which the test mouse is placed in a standardized apparatus and relevant behaviors are assessed in three different sessions (called session I, II, and III).
The apparatus has three compartments (see Figure 1), the left and right compartments contain an inverted cup which can house a mouse (called &#34;stimulus mouse&#34;).
In session I, the test mouse is placed in the cage and its mobility is characterized by the number of transitions made between compartments. In session II, a stimulus mouse is placed under one of the inverted cups and the sociability of the test mouse is quantified by the amounts of time it spends near the cup containing the enclosed stimulus mouse vs. the empty inverted cup. In session III, the inverted cups are removed and both mice interact freely. The sociability of the test mouse in session III is quantified by the number of social approaches it makes toward the stimulus mouse and by the number of times it avoids a social approach by the stimulus mouse.
The automated evaluation of the movie detects the nose of the test mouse, which allows the determination of all described sociability measures in session I and II (in session III, approaches are identified automatically but classified manually). To find the nose, the image of an empty cage is digitally subtracted from each frame of the movie and the resulting image is binarized to identify the mouse pixels. The mouse tail is automatically removed and the two most distant points of the remaining mouse are determined; these are close to nose and base of tail. By analyzing the motion of the mouse and using continuity arguments, the nose is identified.
<img alt="Figure 1" src="/files/ftp_upload/52508/52508fig1.jpg" /> 
Figure 1. Assessment of Sociability During 3 sessions. Session I (top): Acclimation of test mouse to the cage. Session II (middle): Test mouse moving freely in the cage while the stimulus mouse is enclosed in an inverted cup. Session III (bottom): Both test mouse and stimulus mouse are allowed to move freely and interact with each other.

This protocol describes a method to quantify mouse sociability. Mice are videotaped as they move and interact in a special cage. Movie processing allows for the automated quantification of sociability with excellent accuracy and reliability.

Bir Otomatik Görüntü İşleme Yaklaşımı ile Fare Sosyallik Deneysel Değerlendirilmesi

Mouse is the preferred model organism for testing drugs designed to increase sociability. We present a method to quantify mouse sociability in which the ...

Davranış

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

In a recent theoretical synthesis on the concept of engagement, Fredricks, Blumenfeld and Paris1 defined engagement by its multiple dimensions: behavioral, emotional and cognitive. They observed that individual types of engagement had not been studied in conjunction, and little information was available about interactions or synergy between the dimensions; consequently, more studies would contribute to creating finely tuned teaching interventions. Benefiting from the recent technological advances in neurosciences, this paper presents a recently developed methodology to gather and synchronize data on multidimensional engagement during learning tasks. The technique involves the collection of (a) electroencephalography, (b) electrodermal, (c) eye-tracking, and (d) facial emotion recognition data on four different computers. This led to synchronization issues for data collected from multiple sources. Post synchronization in specialized integration software gives researchers a better understanding of the dynamics between the multiple dimensions of engagement. For curriculum developers, these data could provide informed guidelines for achieving better instruction/learning efficiency. This technique also opens up possibilities in the field of brain-computer interactions, where adaptive learning or assessment environments could be developed.

This paper aims to describe the techniques involved in the collection and synchronization of the multiple dimensions (behavioral, affective and cognitive) of learners&#8217; engagement during a task.

Nişan Çoklu Boyutları değerlendirilmesi Öğrenme karakterize etmek: Bir Nörofizyolojik Perspektif

In a recent theoretical synthesis on the concept of engagement, Fredricks, Blumenfeld and Paris1 defined engagement by its multiple dimensions: ...

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

The current study presented 60 people with traumatic brain injury (TBI) and 60 controls with isolated facial emotion expressions, isolated vocal emotion expressions, and multimodal (i.e., film clips) stimuli that included contextual cues. All stimuli were presented via computer. Participants were required to indicate how the person in each stimulus was feeling using a forced-choice format. Additionally, for the film clips, participants had to indicate how they felt in response to the stimulus, and the level of intensity with which they experienced that emotion.

This paper describes how to implement a battery of behavioral tasks to examine emotion recognition of isolated facial and vocal emotion expressions, and a novel task using commercial television and film clips to assess multimodal emotion recognition that includes contextual cues.

Travmatik Beyin Hasarı ile insanlar tarafından Emotion Tanıma değerlendirin İzole Expressions ve Film Klipler Kullanımı keşfetmek

The current study presented 60 people with traumatic brain injury (TBI) and 60 controls with isolated facial emotion expressions, isolated vocal emotion ...

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Several neuroimaging studies have suggested that the low spatial frequency content in an emotional face mainly activates the amygdala, pulvinar, and superior colliculus especially with fearful faces1-3. These regions constitute the limbic structure in non-conscious perception of emotions and modulate cortical activity either directly or indirectly2. In contrast, the conscious representation of emotions is more pronounced in the anterior cingulate, prefrontal cortex, and somatosensory cortex for directing voluntary attention to details in faces3,4. Asperger&#39;s syndrome (AS)5,6 represents an atypical mental disturbance that affects sensory, affective and communicative abilities, without interfering with normal linguistic skills and intellectual ability. Several studies have found that functional deficits in the neural circuitry important for facial emotion recognition can partly explain social communication failure in patients with AS7-9. In order to clarify the interplay between conscious and non-conscious representations of emotional faces in AS, an EEG experimental protocol is designed with two tasks involving emotionality evaluation of either photograph or line-drawing faces. A pilot study is introduced for selecting face stimuli that minimize the differences in reaction times and scores assigned to facial emotions between the pretested patients with AS and IQ/gender-matched healthy controls. Information from the pretested patients was used to develop the scoring system used for the emotionality evaluation. Research into facial emotions and visual stimuli with different spatial frequency contents has reached discrepant findings depending on the demographic characteristics of participants and task demands2. The experimental protocol is intended to clarify deficits in patients with AS in processing emotional faces when compared with healthy controls by controlling for factors unrelated to recognition of facial emotions, such as task difficulty, IQ and gender.

An EEG experimental protocol is designed to clarify the interplay between conscious and non-conscious representations of emotional faces in patients with Asperger&#39;s syndrome. The technique suggests that patients with Asperger&#39;s syndrome have deficits in non-conscious representation of emotional faces, but have comparable performance in conscious representation with healthy controls.

Asperger Sendromu Duygusal Yüzler Bilinçli ve Non-bilinçli Gösterimler

Several neuroimaging studies have suggested that the low spatial frequency content in an emotional face mainly activates the amygdala, pulvinar, and ...

Using Facial Electromyography to Assess Facial Muscle Reactions to Experienced and Observed Affective Touch in Humans

"Affective" touch is believed to be processed in a manner distinct from discriminatory touch and to involve activation of C-tactile (CT) afferent fibers. Touch that optimally activates CT fibers is consistently rated as hedonically pleasant. Patient groups with impaired social-emotional functioning also show disordered affective touch ratings. However, relying on self-reported ratings of touch has many limitations, including recall bias and communication barriers. Here, we describe a methodological approach to study affective responses to touch via facial electromyography (EMG) that circumvents the reliance on self-report ratings. Facial EMG is an objective, quantitative, and non-invasive method to measure facial muscle activity indicative of affective responses. Responses can be assessed across healthy and patient populations without the need for verbal communication. Here, we provide two separate datasets demonstrating that CT-optimal and non-optimal touch elicit distinct facial muscle reactions. Moreover, facial EMG responses are consistent across stimulus modalities, e.g. tactile (experienced touch) and visual (observed touch). Finally, the temporal resolution of facial EMG can detect responses on timescales that supersede that of verbal reporting. Together, our data suggest that facial EMG is a suitable methodology for use in affective tactile research that can be used to supplement, or in some cases, supplant, existing measures.

We describe a protocol to assess facial muscle activity in response to experienced and observed tactile stimulation using facial electromyography.

Deneyimli ve gözlenen duygusal Touch insanlarda yüz kas tepkileri değerlendirmek için yüz Elektromiyografi kullanma

"Affective" touch is believed to be processed in a manner distinct from discriminatory touch and to involve activation of C-tactile (CT) afferent fibers. ...

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

As Parkinson&#8217;s disease (PD) is a heterogeneous disorder, personalized medicine is truly required to optimize care. In their current form, standard scores from paper and pencil symptom- measures traditionally used to track disease progression are too coarse (discrete) to capture the granularity of the clinical phenomena under consideration, in the face of tremendous symptom diversity. For this reason, sensors, wearables, and mobile devices are increasingly incorporated into PD research and routine care. These digital measures, while more precise, yield data that are less standardized and interpretable than traditional measures, and consequently, the two types of data remain largely siloed. Both of these issues present barriers to the broad clinical application of the field&#8217;s most precise assessment tools. This protocol addresses both problems. Using traditional tasks to measure cognition and motor control, we test the participant, while co-registering biophysical signals unobtrusively using wearables. We then integrate the scores from traditional paper-and-pencil methods with the digital data that we continuously register. We offer a new standardized data type and unifying statistical platform that enables the dynamic tracking of change in the person&#8217;s stochastic signatures under different conditions that probe different functional levels of neuromotor control, ranging from voluntary to autonomic. The protocol and standardized statistical framework offer dynamic digital biomarkers of physical and cognitive function in PD that correspond to validated clinical scales, while significantly improving their precision.&#160;

This protocol offers a digitization of portions of traditional clinical tasks commonly used to measure cognition and motor control in Parkinson&#8217;s disease. Clinical tasks are digitized while biophysical rhythms are co-registered from different functional levels of the nervous systems, ranging from voluntary, spontaneous, automatic to autonomic.

Parkinson hastalığında motor ve bilişsel fonksiyonun dinamik dijital Biomarkers

As Parkinson&#8217;s disease (PD) is a heterogeneous disorder, personalized medicine is truly required to optimize care. In their current form, standard ...

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Behavioral analysis of adults engaged in learning tasks is a major challenge in the field of adult education. Nowadays, in a world of continuous technological changes and scientific advances, there is a need for life-long learning and education within both formal and non-formal educational environments. In response to this challenge, the use of eye-tracking technology and data-mining techniques, respectively, for supervised (mainly prediction) and unsupervised (specifically cluster analysis) learning, provide methods for the detection of forms of learning among users and/or the classification of their learning styles. In this study, a protocol is proposed for the study of learning styles among adults with and without previous knowledge at different ages (18 to 69-year-old) and at different points throughout the learning process (start and end). Statistical analysis-of-variance techniques mean that differences may be detected between the participants by type of learner and previous knowledge of the task. Likewise, the use of&#160;unsupervised learning clustering techniques throws light on similar forms of learning among the participants across different groups. All these data will facilitate personalized proposals from the teacher for the presentation of each task at different points in the chain of information processing. It will likewise be easier for the teacher to adapt teaching materials to the learning needs of each student or group of students with similar characteristics.

We present a protocol for a behavioral analysis of adults (ages 18 to 70-year-old) engaged in learning processes, undertaking tasks designed for Self-Regulated Learning (SRL). The participants, university teachers and students, and adults from the University of Experience, were monitored with eye-tracking devices and the data were analyzed with data-mining techniques.

Öğrenme Süreçlerine Dahil Olan Yetişkinlerin Davranış Analizi için Kullanılan Göz İzleme Teknolojisi ve Veri Madenciliği Teknikleri

Behavioral analysis of adults engaged in learning tasks is a major challenge in the field of adult education. Nowadays, in a world of continuous ...

Sınıf Katılımını Artırma: Öğrenci Dikkat Düzeylerinin Yapay Zeka Tabanlı Analizi

Artificial Intelligence-Based System for Detecting Attention Levels in Students

The attention level of students in a classroom can be improved through the use of Artificial Intelligence (AI) techniques. By automatically identifying the attention level, teachers can employ strategies to regain students' focus. This can be achieved through various sources of information.
One source is to analyze the emotions reflected on students' faces. AI can detect emotions, such as neutral, disgust, surprise, sadness, fear, happiness, and anger. Additionally, the direction of the students' gaze can also potentially indicate their level of attention. Another source is to observe the students' body posture. By using cameras and deep learning techniques, posture can be analyzed to determine the level of attention. For example, students who are slouching or resting their heads on their desks may have a lower level of attention. Smartwatches distributed to the students can provide biometric and other data, including heart rate and inertial measurements, which can also be used as indicators of attention. By combining these sources of information, an AI system can be trained to identify the level of attention in the classroom. However, integrating the different types of data poses a challenge that requires creating a labeled dataset. Expert input and existing studies are consulted for accurate labeling. In this paper, we propose the integration of such measurements and the creation of a dataset and a potential attention classifier. To provide feedback to the teacher, we explore various methods, such as smartwatches or direct computers. Once the teacher becomes aware of attention issues, they can adjust their teaching approach to re-engage and motivate the students. In summary, AI techniques can automatically identify the students' attention level by analyzing their emotions, gaze direction, body posture, and biometric data. This information can assist teachers in optimizing the teaching-learning process.

Yazar Spotlight: Sınıf Dikkatini Ölçmede Teknik ve Öznel Zorlukların Ele Alınması

This paper proposes an artificial intelligence-based system to automatically detect whether students are paying attention to the class or are distracted. This system is designed to help teachers maintain students' attention, optimize their lessons, and dynamically introduce modifications in order for them to be more engaging.

Öğrencilerdeki Dikkat Düzeylerini Tespit Etmek İçin Yapay Zeka Tabanlı Sistem

The attention level of students in a classroom can be improved through the use of Artificial Intelligence (AI) techniques. By automatically identifying ...

Duyusal için Veri Toplama ve Otomatik Yüz İfade Analizi Teknoloji Analizi Uygulamalı ve Geçici Analiz Protokolü

Özet

Daha Fazla Video Keşfet

Bu videodaki bölümler

Amyotrophic Lateral Sclerosis bulber Disfonksiyon Kapsamlı Değerlendirmesi (ALS) için Protokol

Bir Otomatik Görüntü İşleme Yaklaşımı ile Fare Sosyallik Deneysel Değerlendirilmesi

Nişan Çoklu Boyutları değerlendirilmesi Öğrenme karakterize etmek: Bir Nörofizyolojik Perspektif

Travmatik Beyin Hasarı ile insanlar tarafından Emotion Tanıma değerlendirin İzole Expressions ve Film Klipler Kullanımı keşfetmek

Asperger Sendromu Duygusal Yüzler Bilinçli ve Non-bilinçli Gösterimler

Deneyimli ve gözlenen duygusal Touch insanlarda yüz kas tepkileri değerlendirmek için yüz Elektromiyografi kullanma

Parkinson hastalığında motor ve bilişsel fonksiyonun dinamik dijital Biomarkers

Öğrenme Süreçlerine Dahil Olan Yetişkinlerin Davranış Analizi için Kullanılan Göz İzleme Teknolojisi ve Veri Madenciliği Teknikleri

Öğrencilerdeki Dikkat Düzeylerini Tespit Etmek İçin Yapay Zeka Tabanlı Sistem

Öğrencilerdeki Dikkat Düzeylerini Tespit Etmek İçin Yapay Zeka Tabanlı Sistem