IPSJ SIG Technical Report Vol.2017-UBI-55 No.10 Vol.2017-ASD-9 No /8/25 1,a) 1,b) IoT SVM Random Forest GMM-HMM A Study on Data Analysis Aiming

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1 1,a) 1,b) IoT SVM Random Forest GMM-HMM A Study on Data Analysis Aiming at Accuracy Improvement of In-Home Living Activity Akita Hiroya 1,a) Sato Kenya 1,b) 1. IoT(Internet of Things) [1][2] [3] IoT 1 Doshisha University Graduate School of Science and Engineering a) b) 2. ECHONET Lite [4] Random Forest c 2017 Information Processing Society of Japan 1

2 6m 換気扇 机 パソコン 冷蔵庫 電子キッチンレンジ炊飯器 3m 1 ベッド 3 : iremocon ipod touch + テレビ机パソコンベッド エアコン 換気扇冷蔵庫 電子キッチンレンジ炊飯器 3m 4 : 2 iremocon[6] JSCA WG ECHONET-Lite ECHONET-Lite ECHONET-Lite ECHONET-Lite BLE(Bluetooth Low Energy) [5] 4 beacon ipod touch ipod touch ipod touch REST c 2017 Information Processing Society of Japan 2

3 1 Rest サーバー mongodb -50 wifi の電波強度の遷移 温度湿度照度 位置情報加速度情報 iremocon 温度, 湿度, 照度情報取得 ipod touch 位置情報加速度情報取得 電波 BLE 電波 電波強度 (dbm) OS ios9.3.5 ipod touch swift2.2 Xcode 7.3 2GB CPU mongodb PC TV X,Y,Z BLE Wifi 2.4GHz Wifi Wifi NaN 時間 (sec) 6 Wifi A F near middle far PC 4.3 svm random forest python sklearn SVM(Support Vector Machine) SVM SVM 9 3 SVM F % Random Forest Random Forest c 2017 Information Processing Society of Japan 3

4 4 Random Forest F % 7 SVM: 10 Rondom Forest: 8 SVM: SVM の主成分削減による正答率の遷移 正答率 (%) 主成分 ( 次元数 ) 11 Random Forest: 9 SVM: Random Forest c 2017 Information Processing Society of Japan 4

5 Random Forest の主成分削減による正答率の遷移 正答率 (%) 主成分 ( 次元数 ) 12 Random Forest: 14 Random Forest: 6 GMM-HMM % % 1.269% % % % % 13 SVM: PC SVM Random Forest SVM % Random Forest % GMM-HMM GMM-HMM GMM-HMM Wifi 10 Wifi 20 SVM 97% c 2017 Information Processing Society of Japan 5

6 Random Forest SVM SVM Random Forest Random Forest PC 3 SVM 84% Random Forest 93% SVM Random Forest SVM Random Forest IoT GMM-HMM GMM-HMM GMM-HMM JSPS [1] S. Hongeng, R. Nevatia, and F. Bremond: Video-based event recognition: activity representation and probabilistic recognition methods, Computer Vision and Image Understanding, pp , ( ). [2] L. Ballan, M. Bertini, A. Del Bimbo, L. Seidenari, and G. Serra: Event detection and recognition for semantic annotation of video, Multimedia tools and applications, pp , (2011-1). [3] Ming-Je Tsai, Chao-Lin Wu, Sipun Kumar Pradhan, Yifei Xie, Ting-Ying Li, Li-Chen Fu, and Yi-Chong Zeng: Context-aware Activity Prediction using Human Behavior Pattern inreal Smart Home Environments, IEEE International Conference on Automation Science and Engineering (CASE), pp , (2016-8). [4],,,,,,, : ECHONET Lite, (DICOMO2016), pp ,(2016-7). [5] Aplix: MyBeacon, ). [6] GLAMO INC: iremocon ( ). 6. SVM Random Forest GMM-HMM c 2017 Information Processing Society of Japan 6

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1 Kinect for Windows M = [X Y Z] T M = [X Y Z ] T f (u,v) w 3.2 [11] [7] u = f X +u Z 0 δ u (X,Y,Z ) (5) v = f Y Z +v 0 δ v (X,Y,Z ) (6) w = Z + 3 3D 1,a) 1 1 Kinect (X, Y) 3D 3D 1. 2010 Microsoft Kinect for Windows SDK( (Kinect) SDK ) 3D [1], [2] [3] [4] [5] [10] 30fps [10] 3 Kinect 3 Kinect Kinect for Windows SDK 3 Microsoft 3 Kinect for Windows

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