自閉症と感情コンピューティング
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ハーバード社会起業大会ツアー2012のメンバー同窓会にて発表した資料です。 感情コンピューティング（Affective Computing）と自閉症への応用についての簡単な解説をしました。
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自閉症と感情コンピューティング
- 1. 自閉症と 感情コンピューティング 大阪大学大学院 竹村研究室 M2 藤原 由来 June 8, 2013
- 2. 論文紹介 R.W. Picard,“Future affective technology for autism and emotion communication,” Philosophical transactions of the Royal Society of London. Series B, Biological sciences, vol. 364, no. 1535, pp. 3575–84, Dec. 2009. Future affective technology for autism and emotion communication Rosalind W. Picard* MIT Media Laboratory, Cambridge, MA 02139, USA People on the autism spectrum often experience states of emotional or cognitive overload that pose challenges to their interests in learning and communicating. Measurements taken from home and school environments show that extreme overload experienced internally, measured as autonomic nervous system (ANS) activation, may not be visible externally: a person can have a resting heart rate twice the level of non-autistic peers, while outwardly appearing calm and relaxed. The chasm between what is happening on the inside and what is seen on the outside, coupled with chal- lenges in speaking and being pushed to perform, is a recipe for a meltdown that may seem to come ‘out of the blue’, but in fact may have been steadily building. Because ANS activation both influ- ences and is influenced by efforts to process sensory information, interact socially, initiate motor activity, produce meaningful speech and more, deciphering the dynamics of ANS states is important for understanding and helping people on the autism spectrum. This paper highlights advances in technology that can comfortably sense and communicate ANS arousal in daily life, allowing new kinds of investigations to inform the science of autism while also providing personalized feedback to help individuals who participate in the research. Keywords: affective computing; autism; autonomic nervous system; wearable sensors; Phil. Trans. R. Soc. B (2009) 364, 3575–3584 doi:10.1098/rstb.2009.0143 on March 16, 2013rstb.royalsocietypublishing.orgDownloaded from
- 4. 自閉症スペクトラム障害 AUTISM SPECTRUM DISORDER • 非定型なコミュニケー ション • 社会性の障害 • 制限や繰り返しのある行 動、興味、活動
- 7. VALENCE（情動価） と AROUSAL（覚醒度） Panagiotis C. Petrantonakis, Leontios J. Hadjileontiadis, "Emotion Recognition from Brain Signals Using Hybrid Adaptive Filtering and Higher Order Crossings Analysis," IEEETransactions on Affective Computing, vol. 1, no. 2, pp. 81-97, July-December, 2010 ↑ 情動価 覚醒度 → 「快・不快」 「ポジティブ・ネガティブ」 「活動的・非活動的」 「興奮・冷静」
- 8. 最も良い測定方法 覚醒度： EDA (Electrodermal Activity; 皮膚電気活動) 情動価：顔 • 眉間のしわ • ほお骨
- 10. 感情と自律神経系
- 11. 自律神経系 (Autonomic Nervous System;ANS) 生理学的な覚醒を調整する 交感神経 副交感神経 •外界の脅威に備えて、代 謝を促す •‘fight and flight’ 戦いと躍動 •心拍数・血圧が上がり、 汗をかく •体のエネルギーの回復・ 保存 •‘rest and digest’ 休息と消化
- 12. EDAで交感神経の覚醒を 評価する making eye contact (he asked me, in a part that didn’t get aired, why I didn’t just look at him, and he was, I outcomes in general; in short, the ‘group analysis’ can still be done. Thus, an individualized measure- ment approach, scaled to include anybody who wants to participate, can contribute to deepening the understanding of the science of autism. The science of individualized measurements—the idiographic approach—is not a new idea, and Molenaar and others have elsewhere argued for the benefits of this methodology (Molenaar 2004). The idea is to measure and characterize patterns over time (not just minutes, but also days and weeks) for an individual, and then repeat this methodology for many individuals. The immense scientific value of this approach has long been known, yet it has been impractical. Today’s technological advances make the idiographic science newly practical. Most of all, with the individualized data-intensive approach based on measurement in a person’s natural environment, it is not just the science that benefits: each participant can now benefit with information specific to his or her needs and situation. Participants need not wait a year or more to see if a publication shows a finding, and wonder if their data were an outlier or a core part of that finding. Instead, if you participate in a study, you see your own data, and you can see if your patterns connect to those that the scientists find across the group of individuals. 5. DESIGN PRINCIPLES AND CHALLENGES: COMFORT, CLARITY AND CONTROL The challenge is to design a device that can accurately and comfortably measure data outside the laboratory as well as it does inside the laboratory. As shown in (a) (b) (c) Figure 1. Wearable EDA sensor. (a) Sensor is inside stretchy breathable wristband. (b) Disposable Ag/AgCl electrodes ttached to the underside of the wristband. (c) The EDA ensor can be worn comfortably for long periods of time.EDAセンサ EDA（皮膚表面のコンダクタンス） ＝交感神経の覚醒を示す便利な指標 交感神経の 覚醒度
- 16. FRIDLUND & IZARD (1983) • 線形判別による生理学的特徴量からの感情の自動分類 を行ってきた • 被験者に依存した、与えられた4つの顔の筋電位信号に よる、4つの表情（happy, sad, anger, fear）の分類 • 38-51%の精度 (via cross-validation)
- 17. PICARD ET AL. (2001) • パターン認識技術と4つのmodalityを用いた • 皮膚コンダクタンス • 心拍数 • 呼吸 • 顔の筋電位 • 被験者1人の6週間分の測定により、nertralを含めた8つの感情を81%の精度 で自動分類した
- 18. LIU ET AL. (2008) • 12種類の生理学的指標に対してパターン分析 • liking, anxiety, engagementの分類を、ロボットと交流した 自閉症児に対して行った
- 20. 自律神経系の データは日々 明らかに 変化している 必要なのは、絶え間なく変わる ベースラインを認識すること M. Poh, N. C. Swenson, and R.W. Picard,“A wearable sensor for unobtrusive, long-term assessment of electrodermal activity.,” IEEE transactions on bio-medical engineering, vol. 57, no. 5, pp. 1243–52, May 2010.
- 21. 問題：ベースラインは いつ測定すべきか？ 通常の実験：実験室にて短い時間で 被験者の自己申告により 「リラックスしている時間」で取る しかし、この状況は作為的であり 人物を特徴付けるには短すぎる
- 22. 24時間・毎日測定する ことが重要 睡眠時間などに'low stable' な 時間帯が自然に現れる M. Poh, N. C. Swenson, and R.W. Picard,“A wearable sensor for unobtrusive, long-term assessment of electrodermal activity.,” IEEE transactions on bio-medical engineering, vol. 57, no. 5, pp. 1243–52, May 2010.
- 23. もう一つの大きな問題： 慣れない環境での測定 • 非日常的な環境 • 慣れないクリニックや研究室で • 初対面の人の前でテストを受け • 不快な電極、ファスナー、ワイ ヤーなどを付けたりさせられる
- 25. ウェアラブルEDAセンサ making eye contact (he asked me, in a part that didn’t get aired, why I didn’t just look at him, and he was, I an individual, and the many individuals. Th this approach has long impractical. Today’s te idiographic science ne the individualized dat measurement in a pers not just the science can now benefit with her needs and situatio year or more to see if and wonder if their d part of that finding. I study, you see your o your patterns connect across the group of ind 5. DESIGN PRINCIPL COMFORT, CLARITY The challenge is to des and comfortably meas c) gure 1. Wearable EDA sensor. (a) Sensor is inside stretchy eathable wristband. (b) Disposable Ag/AgCl electrodes ached to the underside of the wristband. (c) The EDA nsor can be worn comfortably for long periods of time. (a) (b) (c) 3580 R. W. Picard Future affective technology for autis rstb.royalsociDownloaded from
- 29. 人が多い部屋 覚醒度 UP
- 37. EDAセンサ：デザインの原則
- 38. making eye contact (he asked me, in a part that didn’t get aired, why I didn’t just look at him, and he was, I think in an attempt at friendliness, leaning into me the whole time in a way that was making me very stressy can still be done. Thus, an individualized measure- ment approach, scaled to include anybody who wants to participate, can contribute to deepening the understanding of the science of autism. The science of individualized measurements—the idiographic approach—is not a new idea, and Molenaar and others have elsewhere argued for the benefits of this methodology (Molenaar 2004). The idea is to measure and characterize patterns over time (not just minutes, but also days and weeks) for an individual, and then repeat this methodology for many individuals. The immense scientific value of this approach has long been known, yet it has been impractical. Today’s technological advances make the idiographic science newly practical. Most of all, with the individualized data-intensive approach based on measurement in a person’s natural environment, it is not just the science that benefits: each participant can now benefit with information specific to his or her needs and situation. Participants need not wait a year or more to see if a publication shows a finding, and wonder if their data were an outlier or a core part of that finding. Instead, if you participate in a study, you see your own data, and you can see if your patterns connect to those that the scientists find across the group of individuals. 5. DESIGN PRINCIPLES AND CHALLENGES: COMFORT, CLARITY AND CONTROL The challenge is to design a device that can accurately and comfortably measure data outside the laboratory as well as it does inside the laboratory. As shown in figure 1, researchers at MIT have developed an EDA (c) Figure 1. Wearable EDA sensor. (a) Sensor is inside stretchy breathable wristband. (b) Disposable Ag/AgCl electrodes attached to the underside of the wristband. (c) The EDA sensor can be worn comfortably for long periods of time. EDAは情動価(VALENCE)に 関与しない EDA 好き 嫌い 本当 嘘 楽しい 怒り 快 不快 覚醒度 情動価
- 43. ウェアラブルEDAセンサの ‘OPT-IN’ポリシー ユーザが許可しない限り、 EDAの情報は送信されない outcomes in general; in short, the ‘group analysis’ can still be done. Thus, an individualized measure- ment approach, scaled to include anybody who wants to participate, can contribute to deepening the understanding of the science of autism. The science of individualized measurements—the idiographic approach—is not a new idea, and Molenaar and others have elsewhere argued for the benefits of this methodology (Molenaar 2004). The idea is to measure and characterize patterns over time (not just minutes, but also days and weeks) for an individual, and then repeat this methodology for many individuals. The immense scientific value of this approach has long been known, yet it has been impractical. Today’s technological advances make the idiographic science newly practical. Most of all, with the individualized data-intensive approach based on measurement in a person’s natural environment, it is not just the science that benefits: each participant can now benefit with information specific to his or her needs and situation. Participants need not wait a year or more to see if a publication shows a finding, and wonder if their data were an outlier or a core part of that finding. Instead, if you participate in a study, you see your own data, and you can see if your patterns connect to those that the scientists find across the group of individuals. (a) (b) (c) Figure 1. Wearable EDA sensor. (a) Sensor is inside stretchy 3580 R. W. Picard Future affective technology for autism on March 16, 2013rstb.royalsocietypublishing.orgDownloaded from EDA ユーザ：不許可 研究者 データ解析者
- 44. 結論
- 48. まとめ • 自閉症スペクトラム障害と感情 • 感情と自律神経系 • 学習とコミュニケーションのためのパーソナライズド感情計 測 • EDAセンサ：デザインの原則 • 結論