IPSJ SIG Technical Report Vol.2014-HCI-157 No.5 Vol.2014-GN-91 No.5 Vol.2014-EC-31 No /3/13 1,2,a) Mobile Sensing for Capturing Emotional Contex
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1 1,2,a) Mobile Sensing for Capturing Emotional Context by Using Location-Based Services Kenro Aihara 1,2,a) Abstract: This paper proposes a new methodology for capturing visitors emotional context including their emotions in town by using Location-Based Services on smartphones. To capture situations of town, such as events what happens there or how people feel, the author believes that it s not enough to collect tweets and behavior logs of locations in the town, because in fact the number of geotagged tweets is limited. The author has developed and supplies an LBS ios application called Nicott since November Nicott has the function of collecting behavior logs and facial expressions. The service is designed for strollers who visit Futako-tamagawa area, which is being redeveloped as a smart city in Tokyo and consists of complexes including shopping malls, supermarkets, offices, and residential areas around the Futako-tamagawa station. The user can post his/her geotagged microblogs with photos and publish them to other users. He/she can also get event information. The function senses his/her facial expressions where emotions are reflected while he/she is using the application. The paper also shows some experimental results. 1. ICT Information Communication Technology Hitotsubashi, Chiyoda-ku, Tokyo , Japan 2 The Graduate University for Advanced Studies (SOK- ENDAI) a) kenro.aihara@nii.ac.jp c 2014 Information Processing Society of Japan 1
2 ICT Location-Based Services LBS Twitter *1 Facebook *2 Social Networking Services SNS Augmented Reality AR [1] [2], [3] *1 * ICT AIDMA AISCEAS [4], [5]AISCEAS TMO [6] 3. LBS *3 *3 c 2014 Information Processing Society of Japan 2
3 情報処理学会研究報告 (a) イベントリスト (b) イベント情報 (c) 投稿コンテンツ一覧 (d) 投稿コンテンツ詳細 (e) メニュー (f) 地図 図 1 ニコットアプリケーション 画面例 Fig. 1 Snapshot images of the Nicott application トは Apple 社 iphone 等の ios 端末用のアプリケーショ リケーションをダウンロードインストールして利用する ンによって利用可能になるサービスである これは 街に ユーザは 街なかで見かけた一コマを端末で写真撮影し 訪れた来街者らが その街で起こっているできごとや 他 その写真にコメント等を付けて投稿することができる 投 者の発見などを閲覧 共有できるようにすることで 街歩 稿コンテンツには自動的に撮影場所の位置情報が付与され きをサポートするサービスとして仕立てられている 東 る 同時に ユーザらは 二子玉川で開催もしくは予定さ 京 二子玉川エリアを対象地域として 地域の事業者らの れているイベントの情報を取得することができる 協力の下で実施されている 二子玉川は 多摩川を臨む緑 ユーザによるアプリケーションの利用を通じて サービ 豊かな住宅地として発展してきたが 二子玉川駅周辺の従 スは ユーザの位置 コンテンツへのアクセスログ 投稿コ 来からの商業施設に加え 2011 年には商業 オフィス 住 ンテンツ および 利用時の顔表情等のデータを収集する 居等の複合的な施設を中核としたスマートシティとしての 以下に サービス機能の詳細について述べる 再開発が進められ発展してきている地域である ニコットのユーザは 自らの ios 端末にニコットアプ c 2014 Information Processing Society of Japan 3
4 (a) 1(b) (c) 1(d) (e) 2 Twitter Facebook 9! a! b! c! d e... f g h i 2 Fig. 2 Posting content Lang 2 [7] 2 Valence - Arousal (f) c 2014 Information Processing Society of Japan 4
![2.3 3(c) R-R [9] LF 0.05 0.15Hz HF 0.15 0.](/docs-images/94/120689788/images/5-1.jpg "45Hz HF LF LF HF L/H Wahoo Fitness Blue HR *6 ( 1 ) Bluetooth 4.")
5 (a) (b) 3 Fig. 3 (c) Sensing features of the Nicott application Fig. 4 4 Facial feature points (b) Saragih [8] Open CV *4 Face Tracker * (c) R-R [9] LF Hz HF Hz HF LF LF HF L/H Wahoo Fitness Blue HR *6 ( 1 ) Bluetooth 4.0 ( 2 ) API ( 3 ) ( 4 ) *4 *5 *6 c 2014 Information Processing Society of Japan 5
6 Table 1 1 Subjects of a Nicott Experiment Table 3 3 Result of Classification of Facial Expression % Table 2 Result of Question Please answer apps that you use often with your smartphone. (multi answer) % SNS Twitter Facebook SNS a b c d e f g h i a b c d e f g h i SVM SVM livsvm * % 82.1% 3 a a 278 d 65 1 d! Arousal Wahoo Fitness API beattime *7 cjlin/libsvm/ c 2014 Information Processing Society of Japan 6
7 1 Hz % LBS 5 Table 5 Influences of Dicision Making While Strolling in Town SNS SNS KDDI c 2014 Information Processing Society of Japan 7
8 4 Table 4 Conditions of Providing Personal Data 47.3% 49.1% 54.5% 34.5% 43.6% 5.5% 38.2% 21.8% 5.5% 12.7% 41.8% 10.9% 20.0% 52.7% 34.5% 5.5% 1.8% 3.6% 7.3% 9.1% IT [1] 1 : AR: AR AR Vol. 51, No. 4, pp (2010). [2] Zheng, V. W., Cao, B., Zheng, Y., Xie, X. and Yang, Q.: Collaborative Filtering Meets Mobile Recommendation: A User-Centered Approach, Proceedings of the 24th AAAI Conference on Artificial Intelligence, pp (2010). [3] Ducheneaut, N., Partridge, K., Huang, Q., Price, B., Roberts, M., Chi, H., Bellotti, V. and Begole, B.: Collaborative Filtering Is Not Enough? Experiments with a Mixed-Model Recommender for Leisure Activities, User Modeling, Adaptation, and Personalization, Lecture Notes in Computer Science, Springer Berlin / Heidelberg, pp (2009). [4] Vol. 51, No. 12, pp (2006). [5] (2007). [6] No. 605, pp (2006). [7] Lang, P. J.: The Emotion Probe: Studies of Motivation and Attention, American Psychologist, Vol. 50, No. 5, pp (1995). [8] Saragih, J. M., Lucey, S. and Cohn, J. F.: Deformable Model Fitting by Regularized Landmark Mean-Shift, International Journal of Computer Vision, Vol. 91, No. 2, pp (2011). [9] MBE, ME pp (1996). [10] Aihara, K., Koshiba, H. and Takeda, H.: Behavioral Cost Based Recommendation Model for Wanderers in Town, Human-Computer Interaction. Towards Mobile and Intelligent Interaction Environments, Lecture Notes in Computer Science, Vol. 6763, Springer Berlin Heidelberg, pp (2011). [11] 26 No. 2P1-OS9b-2 (2012). [12] Aihara, K.: Do Strollers in Town Needs Recommendation?: on Preferences of Recommender in Location- Based Services, Distributed, Ambient, and Pervasive Interactions (Streitz, N. and Stephanidis, C., eds.), Lecture Notes in Computer Science, Vol. 8028, Springer Berlin Heidelberg, pp (2013). c 2014 Information Processing Society of Japan 8
1 UD Fig. 1 Concept of UD tourist information system. 1 ()KDDI UD 7) ) UD c 2010 Information Processing S
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