IPSJ SIG Technical Report Vol.2011-MBL-59 No.18 Vol.2011-CDS-2 No /9/6 1 1, RFID Optimization of Sensor Allocation for Detecting Human Mo
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1 1 1, RFID Optimization of Sensor Allocation for Detecting Human Movement by Passive Infrared Sensors in Home Environment Kazuya Murao, 1 Tsutomu Terada, 1, 2 Ai Yano 3 and Ryuichi Matsukura 3 Recently, smart home and smart office where sensors are installed are expected to reduce power consumption by effective home appliance control considering human positions and movements. Though the methods using RFID tags and cameras have been proposed so far, it is a burden to put the tag over a reader whenever passing through rooms and camera is costly and may invade our privacy. In this paper, we examine the relationship between sensor positions and detection accuracy for the simultaneous tracking in the home environment where passive infrared sensors have originally been installed for light control % HEMS (Home Energy Management System) BEMS (Buildig Energy Management System) 1) ON/OFF ON/OFF RFID 2 1 Graduate School of Engineering, Kobe University 2 PRESTO, Japan Science and Technology Agency 3 Fujitsu Laboratories Ltd. 1 c 2011 Information Processing Society of Japan
2 ) RFID 3) IC 4) GPS HVAC (Central heating, ventilation, and air conditioning) 5) 1 65% HVAC % 55% Wren 6) 2 Wilson 7) 1 1 8) 9) 1 7) LDK 12 1 (1 ) 2 2 ( 1 2) (2 ) c 2011 Information Processing Society of Japan
3 情報処理学会研究報告 ග㒊 86%ࢥ ࢡࢱ (a) First floor Fig. 1 (b) Second floor (b) センサ外観 図 1 実験家屋の間取りとセンサ設置箇所 Floor plan of the experimental house and sensor arrangement. 内にセンサは合計 25 個設置されている 部屋の間取りおよびセンサ設置箇所を図 1 に セ ンサの設置の様子および外観を図 2 に示す (a) センサ設置の様子 人感センサは NTT アドバンスドテクノロジ株式会社製 SN-MP13 を使用した センサの 図 2 人感センサ Fig. 2 Passive IR sensor. 仕様を表 1 に示す 各センサは図 3 に示すようにセンサコントローラを介して住宅内にあ るサーバに接続されている センサは 500 ミリ秒おきにセンシングし センサコントロー 表 1 センサの仕様 Table 1 Sensor specifications. ラに情報を通知する センサコントローラは人物の在 不在判定を行い 前回サーバに通知 した状態と異なる状態が 1 秒間 (2 サンプル) 続けばサーバに現在の状態を通知する セン 検出距離 サの検出範囲内に動いている物体が現れると Found その後範囲内から動いている物体 検出範囲 が消えると NotFound が検出時刻および反応したセンサ ID とともにサーバに通知され 最大 5m 水平方向 垂直方向 検出ゾーン数 る ただし Found および NotFound は人の有無でなく 範囲内の移動物体の有無で 価格 個 5,900 円 (税込) あり 人が存在していても動いていない場合は NotFound が出力される場合がある そ のため本研究では Found の情報のみを利用する また 検出範囲外を移動したためにセ ンサが反応しない場合や隣接するセンサの検出範囲に入ったために誤反応する可能性があ 簡単のため 住人は異なる 2 部屋間の移動のみを行うとする 便所を出て廊下で長時間静止 る さらに センサは移動物体の有無のみを検出するため 人物が誰であるかを直接検出す したり リビングを出て何かを思い出して再びリビングに戻るような移動は考えない ることはできない そのため 複数の住人が同時に移動する場合 サーバが受信したセンシ 4. 移動情報検出手法 ングデータに複数の移動から発生するデータが混在する これらの理由により 単純に反応 本章ではサーバが受信したセンサ列から移動情報を推定する手法を述べる 温度や加速度 したセンサが設置されている場所を接続して移動経路を検出することは困難である ただし 3 c 2011 Information Processing Society of Japan
4 3 Fig. 3 Sensor configuration. Fig. 4 4 Movement detection. 2 ID SVM 10) K-NN ID ID 11) 4 ID 1 ID W ID T = t T = t W T = t 2 ID ( ) DP m n X = (x 1,, x m ) Y = (y 1,, y n ) m n d ij x i = y i d ij = 0 x i y i d ij = 1 X Y P = (p 1,, p k ) ( 1 ) Initialization: Cost(0, 0) = 0 Cost(i, 0) = for i = 1,, m Cost(0, j) = for j = 1,, n ( 2 ) Cost calculation: 4 c 2011 Information Processing Society of Japan
5 Do for i = 1, 2,, m Do for j = 1, 2,, n Cost(i 1, j 1) Cost(i, j) = d(x i, y j ) + min Cost(i 1, j ) Cost(i, j 1) ( 3 ) Lowest-cost path search: k = 0, i = m, j = n, p k = (i, j) While i 1&&j 1: if Cost(i 1, j 1) < Cost(i 1, j)&&cost(i 1, j 1) < Cost(i, j 1) i, j, k + +, p k = (i, j) else if Cost(i 1, j) < Cost(i, j 1) i, k + +, p k = (i, j) else j, k + +, p k = (i, j) ( 4 ) Output: Return P (2) (3) Cost(m, n) Cost(1, 1) X Y x i y i ID 4 ID ID = 15 W Recall P recision F 5 F = Recall+P recision c 2011 Information Processing Society of Japan
6 2 Table 2 Scenario F F F F 1F 2 1F 1 2F F F 2F 2F 2 2F F F 1F 1F 2F 1F 2F 2F 1F A B 3 2 Table 3 Scenario for two persons 15:46: :47:05 15:47:54 15:48: :49: :50: :50: :51:04 15:51:34 15:51:46 15:52:14 15:52:31 15:52:54 15:53:07 15:53:34 15:53: :54: :54: :54: :55:05 15:55:29 15:55:52 15:56:29 15:56:51 15:47:54 15:48:05 15:48:54 15:49:15 15:50:24 15:50:34 15:51:24 15:51: :52: :52: :53: :53: :54: :54:39 15:55:24 15:55:31 15:56:24 15:56: F c 2011 Information Processing Society of Japan
7 r e 0.85 s u a 0.8 e - m 0.75 F 人 2 人 居間東奥居間東前居間西奥居間西前台所奥台所前和室奥和室前寝室奥寝室前洋室 1 奥洋室 1 前洋室 2 奥洋室 2 前予備奥予備前 Fig Movement detection accuracy when one sensor in the room is used r e s u a 0.8 e - m F 人 2 人 全センサあり玄関前なし 1F ホールなし 1F 階段なし 2F 階段なし 2F ホールなし Fig Movement detection accuracy when one sensor in the corridor is unused F LDK c 2011 Information Processing Society of Japan
8 ( ) (A)( , ) 10) Vapnik, V., The Nature of Statistical Learning Theory, Springer (1995). 11) (DICOMO 2011) pp (2006) 1), IT IT,, Vol. 90, No. 3, pp (2007). 2),,, Vol. 54, No. 4, pp (2009). 3),,,,,,, Vol. 48, No. 3, pp (2007). 4),,,,,, Vol. 52, No. 2, pp (2011). 5) Patel, S. N., Reynolds, M. S., and Abowd, G. D., Detecting Human Movement by Differential Air Pressure Sensing in HVAC System Ductwork: An Exploration in Infrastructure Mediated Sensing, in Proceedings of International Conference on Pervasive Computing (Pervasive 2008), pp (2008). 6) Wren, C. R. and Munguia-Tapia, E., Toward Scalable Activity Recognition for Sensor Networks, in Proceedings of the International Workshop in Location and Context-Awareness (LoCA 2006), pp (2006). 7) Wilson, D. H. and Atkeson, C. G., Simultaneous Tracking and Activity Recognition (STAR) Using Many Anonymous, Binary Sensors, in Proceedings of International Conference on Pervasive Computing (Pervasive 2005), pp (2005). 8) 20 CD-ROM (2006). 9) Doucet, A., Freitas de, N., and Gordon, N., Sequential Monte Carlo Methods in Practice, Springer (2001). 8 c 2011 Information Processing Society of Japan
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