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1 SmartDesign {b09a015,ksato,madokoro}@akita-pu.ac.jp, sakura@smart-d.jp (SRS-18) Ekman 6 3, ( ) SRS-18 Stress Response Scale-18, An Analysis of Psychological Stress Factors with Spontaneous Facial Expressions using Bayesian Networks Hiroaki OTSU, Kazuhito SATO, Hirokazu MADOKORO, and Sakura KADOWAKI Faculty of Systems Science and Technology, Akita Prefectural University 84 4 Aza Ebinokuchi Tsuchiya, Yurihonjo City, Japan SmartDesign Co.,Ltd, 304 Challenge office, west tsuchizakiminato, Akita City, Japan {b09a015,ksato,madokoro}@akita-pu.ac.jp, sakura@smart-d.jp Abstract This paper presents a method to create an individual model to describe relations between facial expressions and stress patterns using Bayesisan networks. For creating this model, we extract relations between three facial expressions (happiness, anger, and sadness) for the basic six facial expressions defined by Ekman and psychological stress factors (dysphoria and anxiety, displeasure and anger, and lassitude). In the evaluation experiment, we constructed relational models of individual facial expressions from the dataset of facial images and stress vales measured using Stress Response Scale-18 (SRS-18) obtained from 10 subjects during 7-20 weeks at one-week intervals. Moreover, we demonstrate psychological stress factors on facial expressions with using to estimate probabilities using arousal levels of facial expressions. Key words Bayesian networks arousal levels Stress Response Scale-18 facial expressions. 1. [1]

2 [2], (SRS-18 : Stress Response Scale-18) 10 ( 5 ) 2. [3] / [4] POMS (Profile of Mood States) POMS 65 POMS 30 [5] SRS-18 (Stress Response Scale-18) SRS SRS-18 ス ト レ ッ サ Fig. 1 脳内 ( 表情発現パス ) 体性 固体差 質格 : 人間の反応 : 個体差で生じる 1 < ストレス反応 > 生 心 表 行 体 理 情 動 < 測定方法 > 唾液アミラーゼ 心拍 脳波 fmri SRS-18 POMS. 表情空間チャート : 本研究で扱う部分 Connection between stress response and facial expressions. 3. Blair [6] (1) (2) (3) (4) [7] 2

3 ( 表情を修飾する ) 前頭前野 島皮質 扁桃体 大脳基底核 運動関連領野 ( 感情を生む ) ( 表情を自発的に作る ) ( 表情筋を動かす ) : 意図的な表情発現パス : 自然な表情発現パス 覚醒度 Fig. 2 Facial expressions and arousal levels. Preprocessing of facial images Acquisition of facial images Smoothing of histograms Gabor wavelet filters Coarse graining Generation of FESCs SOMs Weights Fuzzy ART networks SRS-18 Beysian Network 3. Fig. 3 Whole architecture of our proposed method. X 1 X 2 X 3 X 6 X 4 X 7 X 5 4. Fig. 4 Example of Bayesian networks SOMs (Self Organizing Maps) [8] SOMs Fuzzy ART (Adaptive Resonance Theory) [9] [10] Xi Xj (1) Xi Xj 4 X2 P (X2 X1) P (X2 X1) = P (X2 X1)P (X1) (2) [11] K2 K2,, [12] K2 5. SRS-18 [13] SRS Russell [14] Russell 2 Russell 2 Gabor Wavelets Gabor Wavelets

4 1 SRS-18. Table 1 Question items of SRS-18 and stress factors. SOMs SOMs Fuzzy ART SOMs Fuzzy ART 5. 2 SRS-18 SRS , 4 1: 2: 3: 4: SRS-18 SRS-18 RSR A Happiness Angry Sadness

5 ストレス要因ノード群 全くちがう : なぐさめて欲しい その通りだ : 気持ちが沈んでいる 全くちがう : 全くちがう : 泣きたい気持ちだ まあそうだ : 悲しい気分だ その通りだ : 何もかもいやだと思う 全くちがう : くやしい思いがする 全くちがう : 感情を抑えられない その通りだ : その通りだ : 不愉快だ 全くちがう : 怒りを感じる 全くちがう : その通りだ : 話や行動がまとまらない その通りだ : よくないことを考える 全くちがう : 根気がない 全くちがう : ひとりでいたい気分だ 全くちがう :0.462 何かに集中できない その通りだ : 何となく心配だ いらいらする 怒りっぽくなる いろいろなことに自信がない ストレス因子ノード群 抑うつ 不安 高い : 不機嫌 怒り やや高い : 無気力 高い : 覚醒度 :7 覚醒度 :8 ストレス要因評語 ストレス因子及び合計点レベルの評語 Angry( 全体 ) 覚醒度 :9 Happiness( 全体 ) Sadness( 全体 ) 合計ストレスレベル 表情 ( 覚醒度 ) (SRS-18) ストレス 合計ストレスレベル : 高い 全くちがういくらかそうだまあそうだその通りだ 弱い 普通 やや高い 高い 5 A. Fig. 5 Result of relational model created by Bayesian networks (Subject A). 2 Table 2 ( A). Stress factors and probabilities in the case of the maximum arousal levels (Sbuject A) (0.2909) (0.3390) (0.3038) (0.4375) (0.4814) (0.4104) (0.3750) (0.3766) (0.3070) (0.3524) (0.3042) (0.2942) 3 ( A). Table 3 Stress elements and probabilities in the case of the maximum arousal level in angry (Sbuject A). () (0.8617) (0.8617) (0.4722) (0.6853) (0.2646) (0.5210) 3 A 6. 2, (10 ) 6 4 ( (10 )). Table 4 Stress factors and probabilities in the case of the maximum arousal levels (all ten subjects) (0.4461) (0.5641) (0.2927) ( ) (0.6080) (0.4048) ( ) (0.4016) (0.3482) (0.2994) (0.4422) (0.4017)

6 ストレス要因ノード群 気持ちが沈んでいる 全くちがう : 全くちがう : いくらかそうだ : 何となく心配だ 何もかもいやだと思う 全くちがう : 泣きたい気持ちだ 全くちがう : 悲しい気分だ 全くちがう : なぐさめて欲しい 全くちがう : くやしい思いがする 全くちがう : 全くちがう : 怒りっぽくなる 全くちがう : 怒りを感じる いらいらする 全くちがう : 感情を抑えられない 全くちがう : 不愉快だ 全くちがう : 根気がない 全くちがう : ひとりでいたい気分だ 全くちがう : いろいろなことに自信がない いくらかそうだ : 何かに集中できない いくらかそうだ : よくないことを考える 全くちがう : 話や行動がまとまらない ストレス因子ノード群 抑うつ 不安 弱い : 不機嫌 怒り 弱い : 無気力 弱い : 覚醒度 :7 覚醒度 :8 ストレス要因評語 ストレス因子及び合計点レベルの評語 Angry( 全体 ) Sadness( 全体 ) 全くちがう 弱い 覚醒度 :9 Happiness( 全体 ) 合計ストレスレベル 表情 ( 覚醒度 ) ストレス (SRS-18) 合計ストレスレベル : 高い いくらかそうだまあそうだその通りだ 普通 やや高い 高い Fig Result of relational model created by Bayesian networks (all ten subjects). 5 ( (10 )). Table 5 Stress elements and probabilities in the case of the maximum arousal level in sadness (all ten subjects). ( ) (0.2949) (0.3277) (0.5674) (0.6602) (0.8625) (0.3741) 6 Table 6 (). Stress factors and probabilities in the case of the maximum arousal levels (Male) (0.4538) (0.4137) (0.5207) (0.5135) (0.5752) (0.5731) (0.5420) (0.3280) (0.4269) (0.4595) (0.5304) (0.4872) A

7 ストレス要因ノード群 全くちがう : いくらかそうだ : いくらかそうだ : 全くちがう : なぐさめて欲しい 全くちがう : 泣きたい気持ちだ全くちがう : 怒りっぽくなる 全くちがう : 全くちがう : 感情を抑えられない 根気がない いくらかそうだ : ひとりでいたい気分だ いくらかそうだ : 全くちがう : 悲しい気分だ いらいらする 全くちがう : 何かに集中できない 話や行動がまとまらない 何もかもいやだと思う その通りだ : 不愉快だ 全くちがう : その通りだ : 気持ちが沈んでいる 全くちがう : 全くちがう : よくないことを考える 全くちがう : 何となく心配だ 怒りを感じる くやしい思いがする いろいろなことに自信がない ストレス因子ノード群抑うつ 不安 高い : 不機嫌 怒り 弱い : 無気力 高い : 覚醒度 :5 覚醒度 :5 ストレス要因評語 ストレス因子及び合計点レベルの評語 Angry( 全体 ) 覚醒度 :6 Happiness( 全体 ) Sadness( 全体 ) 合計ストレスレベル 表情 ( 覚醒度 ) (SRS-18) ストレス 合計ストレスレベル : 高い 全くちがういくらかそうだまあそうだその通りだ 弱い 普通 やや高い 高い 7. Fig. 7 Result of relational model created by Bayesian networks (Male). ストレス要因ノード群 全くちがう : 全くちがう : 泣きたい気持ちだ 全くちがう : なぐさめて欲しい 何もかもいやだと思う 気持ちが沈んでいる 何となく心配だ いくらかそうだ : 全くちがう : 悲しい気分だ 全くちがう : 全くちがう : 怒りっぽくなる 感情を抑えられない 怒りを感じる 全くちがう : 全くちがう : 全くちがう : くやしい思いがする 全くちがう : 不愉快だ 全くちがう : いらいらする いくら k そうだ : 全くちがう : 根気がない よくないことを考える 何かに集中できない 全くちがう : ひとりでいたい気分だ 全くちがう : 話や行動がまとまらない いろいろなことに自信がない いくらかそうだ : いくらかそうだ : ストレス因子ノード群抑うつ 不安 高い : 不機嫌 怒り 弱い : 高い : 無気力 覚醒度 :7 覚醒度 :8 ストレス要因評語 ストレス因子及び合計点レベルの評語 Angry( 全体 ) Sadness( 全体 ) 全くちがう 弱い 覚醒度 :8 Happiness( 全体 ) 合計ストレスレベル 表情 ( 覚醒度 ) (SRS-18) ストレス 合計ストレスレベル : 高い いくらかそうだまあそうだその通りだ 普通 やや高い 高い 8. Fig. 8 Result of relational model created by Bayesian networks (Female). 9

8 7 Table 7 8 Table 8 9 Table 9 (). Stress elements and probabilities in the case of the maximum arousal level in angry (Male). () (0.5081) (0.2857) (0.4161) (0.3081) (0.6149) (0.5727) (). Stress factors and probabilities in the case of the maximum arousal levels (Female) (0.3010) (0.3588) (0.3171) (0.8044) (0.7418) (0.6631) (0.5539) (0.4599) (0.3142) (0.5382) (0.4229) (0.3374) (). Stress elements and probabilities in the case of the maximum arousal level in happiness (Female). ( ) (0.3450) (0.3618) (0.6536) (0.7021) (0.8786) (0.4260) A 19 A 7. SRS-18 Acknowledgment 10 [1],,, [2],, Folia Pharmacol. Jpn., vol.126, no.3, pp Sep [3],,,,, vol.33, no.3, [4] Medicore Co., Ltd., Body Checker(Cardio Monitor), http : // core.com/ [5], STRESS SCALE GUIDEBOOK,, [6] R.J.R. Blair, Facial expressions, their communicatory functions and euro-cognitive substates, Philos. Trans. R. Soc. Lond., vol.358, pp , Mar [7], :, pp , [8] T. Kohonen, Self-organizing maps, Springer Series in Information Sciences, [9] G. A. Carpenter, S. Grossberg, and D. B. Rosen, Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System, Neural Networks, vol.4, pp , [10], MIRU2010 Kushiro Jul [11],,, pp [12] G. F. Cooper, The Computational Complexity of Probabilistic Inference using Bayesian Belief Networks, Artificial Intelligence, vol.42, pp , Mar [13],,,,, Stress Response Scale-18, Jul [14] J.A. Russell and M. Bullock, Multidimensional Scaling of Emotional Facial Expressions: Similarity From Preschoolers to Adults, Journal of Personality and Social Psychology, vol.48, pp , 1985.

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