Upgrade to Pro — share decks privately, control downloads, hide ads and more …

情報通信技術を用いたセンシング / Real world sensing with ICT

情報通信技術を用いたセンシング / Real world sensing with ICT

九州大学高等研究院 / 九州先端科学技術研究所 研究交流会 講演資料
Material presented at Kyushu Univ - ISIT Joint Workshop

F212be062f34ad310ab5cea3da92cab6?s=128

Shigemi ISHIDA

February 07, 2019
Tweet

Transcript

  1. ৘ใ௨৴ٕज़Λ༻͍ͨ
 ηϯγϯά ੴా ൟາ <ishida@f.ait.kyushu-u.ac.jp> ۝भେֶ γεςϜ৘ใՊֶݚڀӃ ॿڭ Feb 7,

    2019 @۝भେֶ–ISITݚڀަྲྀձ
  2. ࢿྉ͸ҎԼͰऔಘͰ͖·͢ https://speakerdeck.com/ pman0214/real-world- sensing-with-ict

  3. ੴా ൟາʢ͍ͩ͠ ͛͠Έʣ ▪ ུྺ • 2006೥3݄ ࣳӜ޻ۀେֶ޻ֶ෦ిࢠ޻ֶՊ • 2008೥3݄

    ౦ژେֶେֶӃ৽ྖҬ૑੒ՊֶݚڀՊम࢜ྃ • 2008೥4݄ʙ2009೥9݄ (ג) ΞΫςΟε։ൃ৬ • 2012೥4݄ʙ2013೥9݄ ֶৼ ಛผݚڀһDC2→PD • 2012೥9݄ ౦ژେֶେֶӃ޻ֶܥݚڀՊത࢜ྃ (޻ത) • 2013೥5݄ʙ9݄ ถϛωιλେ Visi;ng Scholar • 2013೥10݄ʙ ݱ৬ ▪ ઐ໳ • ແઢηϯαωοτϫʔΫɼ࣮ۭؒηϯγϯάɼ԰಺ଌҐ 3
  4. IoT: Internet of Things ▪ ਎ͷճΓͷʮϞϊʯ͕Πϯλʔωοτʹͭͳ͕Δ ੈք • Ϟϊ͕ηϯα΍ΞΫνϡΤʔλʹͳΓɼଞͷϞϊ ͱ࿈ܞͯ͠ศརͳαʔϏεΛ࣮ݱ

    • ηϯγϯάσʔλΛղੳͯ͠ΞΫνϡΤʔγϣϯ 4
  5. IoT͸σʔλղੳʁ 5

  6. IoT͸σʔλղੳʁ 5 Ϗοάσʔλ ਓ޻஌ೳ ػցֶश ਂ૚ֶश

  7. IoT͸σʔλղੳʁ 5 σʔλͷऔಘͩͬͯେࣄ Ϗοάσʔλ ਓ޻஌ೳ ػցֶश ਂ૚ֶश

  8. Ξ΢τϥΠϯ ▪ ແઢηϯαωοτϫʔΫ ▪ ԰಺ଌҐٕज़ • ҟछແઢΛ࢖ͬͨηϯαଌҐ • νϟωϧ෼཭BLEଌҐ ▪

    ৽͍͠ηϯγϯάٕज़ • ं྆ηϯγϯά • ԻڹΞϓϩʔν • ແઢΞϓϩʔν • ԰֎ਓମηϯγϯά 6 ←ίί͕ࠓ೔ͷϝΠϯ
  9. Ξ΢τϥΠϯ ▪ ແઢηϯαωοτϫʔΫ ▪ ԰಺ଌҐٕज़ • ҟछແઢΛ࢖ͬͨηϯαଌҐ • νϟωϧ෼཭BLEଌҐ ▪

    ৽͍͠ηϯγϯάٕज़ • ं྆ηϯγϯά • ԻڹΞϓϩʔν • ແઢΞϓϩʔν • ԰֎ਓମηϯγϯά 6 ଌҐٕज़ɼηϯγϯάٕज़Λ ۦ͚଍Ͱ঺հ ←ίί͕ࠓ೔ͷϝΠϯ
  10. ແઢηϯαωοτϫʔΫ Wireless Sensor Network (WSN) 7

  11. ແઢηϯαωοτϫʔΫ (WSN) ▪ ηϯαʴແઢ௨৴ • औಘͨ͠ηϯασʔλΛແઢ௨৴Ͱऩू ▪ ྫʣεϚʔτ೶ۀɼεϚʔτϋ΢ε • WSNʹΑΔ؀ڥ৘ใͷऔಘ͕ඞਢ

    8
  12. ηϯαϊʔυ ▪ ௒খܕίϯϐϡʔλʴ௨৴ • ۃΊͯඇྗɼػೳ΋গͳ͍ 9 εϚʔτϑΥϯ Moto Z Play

    ηϯαϊʔυ MICAz CPU 8-core Cortex-A53, 64bit ATmega128L, 8bit ΫϩοΫप೾਺ 2.0 GHz 8 MHz ROM༰ྔ 32 GB 128 kB ϝϞϦ༰ྔ 3 GB 4 kB ແઢηϯαϊʔυMICAz
  13. ηϯαϊʔυ ▪ ௒খܕίϯϐϡʔλʴ௨৴ • ۃΊͯඇྗɼػೳ΋গͳ͍ 9 εϚʔτϑΥϯ Moto Z Play

    ηϯαϊʔυ MICAz CPU 8-core Cortex-A53, 64bit ATmega128L, 8bit ΫϩοΫप೾਺ 2.0 GHz 8 MHz ROM༰ྔ 32 GB 128 kB ϝϞϦ༰ྔ 3 GB 4 kB ແઢηϯαϊʔυMICAz
  14. ηϯαϊʔυ ▪ ௒খܕίϯϐϡʔλʴ௨৴ • ۃΊͯඇྗɼػೳ΋গͳ͍ 9 εϚʔτϑΥϯ Moto Z Play

    ηϯαϊʔυ MICAz CPU 8-core Cortex-A53, 64bit ATmega128L, 8bit ΫϩοΫप೾਺ 2.0 GHz 8 MHz ROM༰ྔ 32 GB 128 kB ϝϞϦ༰ྔ 3 GB 4 kB ͜Εʢ۝େϩΰʣ = 5,619B ແઢηϯαϊʔυMICAz
  15. ηϯαϊʔυ ▪ ௒খܕίϯϐϡʔλʴ௨৴ • ۃΊͯඇྗɼػೳ΋গͳ͍ 㱺 ͍͔ʹޮ཰ྑ͘࢖͏͔ 9 εϚʔτϑΥϯ Moto

    Z Play ηϯαϊʔυ MICAz CPU 8-core Cortex-A53, 64bit ATmega128L, 8bit ΫϩοΫप೾਺ 2.0 GHz 8 MHz ROM༰ྔ 32 GB 128 kB ϝϞϦ༰ྔ 3 GB 4 kB ͜Εʢ۝େϩΰʣ = 5,619B ແઢηϯαϊʔυMICAz
  16. WSNʹ͓͚Δ՝୊ͷྫ ▪ ηϯαϊʔυ͸Ͳ͜ʹઃஔ͞Ε͍ͯΔʁ • શηϯαϊʔυͷҐஔΛखಈͰ؅ཧ͢Δͷ͸΄΅ෆ Մೳ 㱺 ଌҐٕज़ (ಛʹ԰಺͕໰୊) ▪

    ηϯγϯά͕೉͍͠৘ใΛͲ͏΍ͬͯऔಘ͢Δʁ • ैདྷͷηϯαͰ೉͔ͬͨ͜͠ͱΛ࣮ݱ͢Δ৽͍͠η ϯγϯάٕज़ • লిྗͳηϯγϯάٕज़ ※ ηϯαϊʔυͰಈ͔͘Ͳ͏͔͸࡞͔ͬͯΒߟ͑Δ 㱺 ৽͍͠ηϯγϯάٕज़ 10
  17. ԰಺ଌҐٕज़ Indoor LocalizaHon 11

  18. AP1 = –48dBm AP2 = –70dBm : : QUERY Estimation

    Phase - ଌҐྖҬ֤ॴͰ৴߸ڧ౓Λଌఆ - ଌҐ࣌ʹ͸ଌҐ஍఺Ͱଌఆͨ͠ ৴߸ڧ౓ͱ΋ͬͱ΋ࣅ͍ͯΔ஍ ఺Λ୳ࡧͯ͠Ґஔਪఆ ԰಺ଌҐͷجຊ ▪ 2ͭͷํࣜ 12 DB AP1 = –52dBm AP2 = –62dBm : : INSERT Training Phase • ଟลଌྔ๏ • ϑΟϯΨʔϓϦϯτ๏ - Ґஔͷ෼͔͍ͬͯΔج४ہ ͔Βͷڑ཭Λਪఆ - ෳ਺ج४ہ͔Βͷڑ཭ͰҐ ஔਪఆ
  19. ηϯαϊʔυͷઃஔҐஔਪఆ ▪ WiFi APͷ৴߸Λ࢖ͬͯηϯαΛଌҐ [1,2] • ηϯαΛஔ͍͚ͨͩͰͲ͜ʹ͋Δ͔͕෼͔Δ ※ ηϯαϊʔυ͸ZigBee 㱺

    WiFi৴߸Λड৴Ͱ͖ͳ͍ 13 RSS1=–40 RSS2=–50 RSS3=–45 RSS1=–60 RSS2=–54 RSS3=–42 AP 2 AP 1 AP 3 Sensor node Localiza;on Server WiFi Fingerprint DB ZigLoc Demo [1] S. Ishida et al., “WiFi AP-RSS Monitoring using Sensor Nodes toward Anchor-Free Sensor Localiza;on”, IEEE VTC-Fall, Sep 2015. [2] T. Yamamoto et al., “Accuracy improvement in sensor localiza;on system u;lizing heterogeneous wireless technologies”, ICMU, Oct 2017.
  20. ଌҐٕज़ͷԠ༻ྫ (1) ▪ BLEʢBluetooth Low Energyʣ • iBeaconͳͲɼଌҐਫ਼౓͕ѱ͍ • Separate

    Channel Fingerprin;ng • νϟωϧ෼཭BLEଌҐ [3] 14 Receiver MacBook Pro BLE Beacon BLED112 +Mobile ba7ery [3] S. Ishida et al., “Proposal of Separate Channel Fingerprin;ng Using Bluetooth Low Energy”, IIAI AAI, Jul 2016. Beacon1 RSS Beacon2 RSS Beacon3 RSS Fingerprints in DB Fingerprint x at unknown location Find nearest neighbor k Beacon1: –52dBm@ch37 Beacon1: –62dBm@ch38 Beacon1: –60dBm@ch39 Beacon2: –61dBm@ch37 Beacon2: –70dBm@ch38 Beacon2: –58dBm@ch39 38 Beacon1 Beacon2 Channel gain Frequency Frequency ch37 39
  21. ଌҐٕज़ͷԠ༻ྫ (2) ▪ ΦϯσϚϯυҐஔ৘ใαʔϏε [4] 15 RSS=–48 RSS=–52 RSS=–58 RSS=–55

    WiFi AP Core AP WiFi Device Localization Server On-demand Loc Demo [4] S. Ishida et al., “On-Demand Indoor Loca;on-based Service using Ad-Hoc Wireless Posi;oning Network”, IEEE ICESS, Aug 2015.
  22. ଌҐܭࢉͷ෼ࢄ ▪ ΦϯσϚϯυҐஔ৘ใγεςϜ [5] • Ϣʔβ͕૿Ճ͢Δͱॏ͘ͳΔ 㱺 BBϧʔλΛ࢖ͬͯ௨৴Ͱ෼ࢄ 16 Router

    WNDR4300 WiFi Mesh Node PCWL-0100 172.17.0.0/16 192.168.0.0/16 10.0.0.1 10.0.0.2 Router Localiza-on Servers WiFi APs Shuffle Map Reduce 192.168.0.1 192.168.0.2 dest 10.0.0.0/9 DNAT to 192.168.0.1 dest 10.128.0.0/9 DNAT to 192.168.0.2 NAT Rule WiFi APs Localiza.on Servers 10.0.0.0 : 10.127.0.0 10.128.0.0 : 10.255.255.255 [5] J. Kajimura, “Design of distributed calcula;on scheme using network address transla;on for ad-hoc wireless posi;oning network”, Springer CCIS, vol.760, ISIP Post Proc., Oct 2017.
  23. ं྆ηϯγϯά Vehicle Sensing 17

  24. എܠ ▪ ITS (Intelligent Transporta;on System) • ಓ࿏্ͷं྆ͷݕग़͸ॏཁͳج൫ٕज़ ͷ1ͭ ▪

    طଘͷं྆ݕग़ηϯα • ϧʔϓίΠϧɼޫిηϯαɼ௒Ի೾ɼ ੺֎ઢͳͲ 㱺 ઃஔɾ؅ཧʹಓ࿏޻ࣄ͕ඞཁͰߴίετ 㱺 ݕग़ൣғ͕ڱ͍ͨΊೋྠंͷݕग़͕ࠔ೉ 㱺 ௿ίετ͔ͭं྆λΠϓʹΑΒͣߴਫ਼౓ʹं྆Λ ݕग़͢ΔγεςϜ͕ٻΊΒΕ͍ͯΔ 18
  25. Իڹं྆ηϯγϯά ▪ εςϨΦϚΠΫͰं྆ݕग़ [6] • ं྆૸ߦԻͷ౸ୡ࣌ؒࠩΛඳ ͍ͨʮα΢ϯυϚοϓʯΛར༻ • ૸ߦं྆͸SࣈΧʔϒΛඳ͘ 㱺

    SࣈΧʔϒΛݕग़͢Δ 19 Microphones Recorder 2-lane Road α΢ϯυϚοϓ [6] S. Ishida et al., “SAVeD: Acous;c Vehicle Detector with Speed Es;ma;on capable of Sequen;al Vehicle Detec;on”, IEEE ITSC, Nov 2018. Δtmax t Δt –Δtmax
  26. α΢ϯυϚοϓ ▪ 2୆ͷϚΠΫͷं྆Իͷ࣌ؒࠩ • ࣌ؒࠩͷมԽ
 = α΢ϯυϚοϓ Time t t

    Sound Delay 20 L R L R
  27. α΢ϯυϚοϓ ▪ 2୆ͷϚΠΫͷं྆Իͷ࣌ؒࠩ • ࣌ؒࠩͷมԽ
 = α΢ϯυϚοϓ Time t t

    Sound Delay 20 L R L R
  28. α΢ϯυϚοϓ ▪ 2୆ͷϚΠΫͷं྆Իͷ࣌ؒࠩ • ࣌ؒࠩͷมԽ
 = α΢ϯυϚοϓ Time t t

    Sound Delay 20 L R L R
  29. α΢ϯυϚοϓ ▪ 2୆ͷϚΠΫͷं྆Իͷ࣌ؒࠩ • ࣌ؒࠩͷมԽ
 = α΢ϯυϚοϓ Time t t

    Sound Delay 20 L R L R
  30. α΢ϯυϚοϓ ▪ 2୆ͷϚΠΫͷं྆Իͷ࣌ؒࠩ • ࣌ؒࠩͷมԽ
 = α΢ϯυϚοϓ Time t t

    Sound Delay 20 L R L R
  31. α΢ϯυϚοϓ ▪ 2୆ͷϚΠΫͷं྆Իͷ࣌ؒࠩ • ࣌ؒࠩͷมԽ
 = α΢ϯυϚοϓ Time t t

    Sound Delay 20 L R L R
  32. α΢ϯυϚοϓ ▪ 2୆ͷϚΠΫͷं྆Իͷ࣌ؒࠩ • ࣌ؒࠩͷมԽ
 = α΢ϯυϚοϓ Time t t

    Sound Delay 20 L R L R
  33. α΢ϯυϚοϓ ▪ 2୆ͷϚΠΫͷं྆Իͷ࣌ؒࠩ • ࣌ؒࠩͷมԽ
 = α΢ϯυϚοϓ Time t t

    Sound Delay 20 L R L R t = 1 c 8 < : s✓ x + D 2 ◆2 + L 2 s✓ x D 2 ◆2 + L 2 9 = ;
  34. α΢ϯυϚοϓͷྫ ▪ ϚΠΫؒͷڑ཭ D = 50 [cm] • ౸ୡ࣌ؒࠩͷ࠷େ஋͸໿1.47 [ms]

    21 -1.5 -1 -0.5 0 0.5 1 1.5 30 35 40 45 50 Sound delay ∆t [ms] Time t [s]
  35. α΢ϯυϚοϓͷྫ ▪ ϚΠΫؒͷڑ཭ D = 50 [cm] • ౸ୡ࣌ؒࠩͷ࠷େ஋͸໿1.47 [ms]

    21 -1.5 -1 -0.5 0 0.5 1 1.5 30 35 40 45 50 Sound delay ∆t [ms] Time t [s] ࠨ͔Βӈ ӈ͔Βࠨ
  36. α΢ϯυϚοϓͷྫ ▪ ϚΠΫؒͷڑ཭ D = 50 [cm] • ౸ୡ࣌ؒࠩͷ࠷େ஋͸໿1.47 [ms]

    21 -1.5 -1 -0.5 0 0.5 1 1.5 30 35 40 45 50 Sound delay ∆t [ms] Time t [s] ࠨ͔Βӈ ӈ͔Βࠨ RANSACͰSࣈΧʔϒΛݕग़ 㱺 Ϟσϧ͔ࣜΒ଎౓΋ਪఆ t = 1 c 8 < : s✓ x + D 2 ◆2 + L 2 s✓ x D 2 ◆2 + L 2 9 = ;
  37. α΢ϯυϚοϐϯά σϞ 22 Real Sound Mapping Demo

  38. Ԡ༻ ▪ লిྗԽ • Waveletม׵Λ࢖ͬͨলిྗं྆ݕग़ख๏ [7] ▪ ϊΠζରࡦ • ఆৗϊΠζՃࢉʹΑΔϊΠζ෼཭

    [8] • ෩ϊΠζରࡦ [9] ▪ ྻंݕग़ • ྻं૸ߦԻΛػցֶशͯ͠ݕग़ [10] • ྻं৐ंҐஔਪఆ [11] 23 [7] ٱอ ଞ, “཭ࢄ΢ΣʔϒϨοτม׵Λ༻͍ͨলϦιʔεं྆ݕग़γεςϜͷઃܭͱධՁ”, IPSJ ITSݚڀձ, Feb-Mar, 2019. (will appear) [8] ཥ ଞ, “ϚΠΫΛ༻͍ͨं྆ݕग़γεςϜʹ͓͚Δ؀ڥϊΠζ࡟ݮख๏ͷఏҊ”, IEICE ASNݚڀձ, Jan 2019. [9] M. Uchino et al., “Ini;al design of acous;c vehicle detector with wind noise suppressor”, PerVehicle, Mar 2019 (will appear) [10] K. Sato et al., “Proposal of acoustic train detection system for crowdsensing”, ITS-AP Fukuoka Post Proc, 2019 (will appear) [11] ࠤ౻ ଞ, “ϚΠΫϩϑΥϯΛ༻͍ͨమಓ৐ंҐஔਪఆख๏ͷઃܭͱධՁ”, IPSJ ITSݚڀձ, Feb-Mar 2019 (will appear)
  39. ԻڹΞϓϩʔνͷݶք ▪ ࣭ٙʹͯ • ʮࣗసं͸ݕग़Ͱ͖ͳ͍ͷʁʯ • ʮาߦऀ͸ݕग़Ͱ͖ͳ͍ͷʁʯ 24

  40. ԻڹΞϓϩʔνͷݶք ▪ ࣭ٙʹͯ • ʮࣗసं͸ݕग़Ͱ͖ͳ͍ͷʁʯ • ʮาߦऀ͸ݕग़Ͱ͖ͳ͍ͷʁʯ ▪ ͜ͷΑ͏ͳ࣭໰Λ͞ΕΔ͕ɼݪཧతʹࠔ೉ •

    ͦ΋ͦ΋ൃͤΒΕΔԻ͕খ͍͞ • ଎౓͕஗͍ͷͰα΢ϯυϚοϓ্Ͱͷղੳ͕ࠔ೉ 24
  41. طଘͷंɼࣗసंɼาߦऀݕग़ ▪ ं • ϧʔϓίΠϧɼϨʔβɼ௒Ի೾ ▪ ࣗసंɾาߦऀ [12,13] • Χϝϥ΍Ϩʔβڑ཭ܭ

    (LiDAR)ʹΑΔݕग़ ▪ ઃஔʹ͸ಓ࿏޻ࣄ͕ඞཁ • ಋೖɾ؅ཧίετ͕ߴ͍ 㱺 ௿ίετͳηϯα͕ٻΊΒΕ͍ͯΔ 25 [12] F. García et al., “Context aided pedestrian detec;on for danger es;ma;on based on laser scanner and computer vision”, Expert Systems with Applica;ons, Nov 2014. [13] P. Dollár et al., “Pedestrian detec;on: an evalua;on of the state of the art”, IEEE Trans. Parern Analysis and Machine Intelligence, Apr 2012.
  42. ▪ WiFi௨৴ͷ఻ൖ࿏ͷมԽʹΑΔηϯγϯά • ंɼࣗసंɼਓͰճંɾ൓ࣹ͕ൃੜ ৽͍͠Ξϓϩʔν 26

  43. ▪ WiFi௨৴ͷ఻ൖ࿏ͷมԽʹΑΔηϯγϯά • ंɼࣗసंɼਓͰճંɾ൓ࣹ͕ൃੜ ճંɾ൓ࣹͯ͠౸ୡ ৽͍͠Ξϓϩʔν 26

  44. ▪ WiFi௨৴ͷ఻ൖ࿏ͷมԽʹΑΔηϯγϯά • ंɼࣗసंɼਓͰճંɾ൓ࣹ͕ൃੜ ճંɾ൓ࣹͯ͠౸ୡ ৽͍͠Ξϓϩʔν 26 ো֐෺ͷҐஔ΍େ͖͞ ʹΑͬͯճંɼ൓ࣹঢ় ଶ͸มԽ

  45. ؔ࿈ݚڀ: ԰಺ແઢηϯγϯά ▪ E-eyes [14] • ԰಺ʹ͓͍ͯ఻ൖ࿏ͷมԽͰਓؒͷߦಈΛਪఆ ▪ Smokey [15]

    • NLOS (Non Line-of-Sight)؀ڥͰ٤Ԏಈ࡞Λݕग़ ▪ RF-Pose [16] • นӽ͠ʹෳ਺ਓͷ࢟੎Λਪఆ (ઐ༻ແઢػΛ࢖༻) ▪ SignFi [17] • WiFiͷ఻ൖ࿏มԽͰख࿩Λೝࣝ 27 [14] Y. Wang et al., “E-eyes: In-home device-free ac;vity iden;fica;on using fine-grained WiFi signatures”, ACM MobiCom, Sep 2014. [15] X. Zheng et al., “Smokey: Ubiquitous smoking detec;on with commercial WiFi infrastructures”, IEEE INFOCOM, Jul 2016. [16] M. Zhao et al., “Through-wall human pose es;ma;on using radio signals”, CVPR, Jun 2018. [17] Y. Ma et al., “SignFi: sign language recogni;on using WiFi”, ACM IMWUT, Mar 2018.
  46. ▪ ACT-Iʮ৘ใͱະདྷʯʹ࠾୒ʢ2018೥10݄ʙʣ • ແઢ௨৴Λ༻͍ͨं྆ɾࣗసंɾาߦऀݕग़ٕज़ • ʮʙʢલུʣʙຊݚڀͷ໨త͸ɺࢢൢ ͷແઢ௨৴ػثΛվ଄͢Δ͜ͱͳ͘༻ ͍ͯं྆΍ࣗసंɺาߦऀΛݕग़͢Δ γεςϜΛ։ൃ͢Δ͜ͱͰ͢ɻແઢ௨ ৴͸पғͷ؀ڥมԽͷӨڹΛड͚Δ͜

    ͱ͔Βɺແઢ௨৴͕ड͚ͨӨڹΛղੳ ͢Δ͜ͱͰं྆ɺࣗసंɺาߦऀͷݕ ग़Λ࣮ݱ͠·͢ɻʯ 㱺 ݚڀΛ։࢝ͨ͠͹͔ΓͷͨΊ੒Ռͷ͘͘͝͝Ұ෦Λ ঺հ ઓུత૑଄ݚڀਪਐࣄۀ 28 ग़ల: ACT-I 平成30年度採択課題
  47. WiFiΛ༻͍ͨं྆ݕग़ [18] ▪ ۝େ಺ͷಓ࿏Ͱ࣮ݧ • ಓ࿏ͷ྆ଆʹૹड৴ػΛઃஔ • ఻ൖ࿏৘ใΛղੳͯ͠௨աं྆Λݕग़ 29 TX

    RX [18] M. Cong et al., “Proposal of On-road Vehicle Detection Method Using WiFi Signal”, IPSJ ITSݚڀձ, Feb-Mar 2019 (will appear)
  48. WiFiʹΑΔं྆ݕग़ͷ֓ཁ 1. WiFi (802.11n)୺຤ؒͰ50msִؒͰ௨৴ 2. ఻ൖ࿏৘ใͷҐ૬৘ใΛநग़ 3. ิਖ਼΍ϊΠζআڈͳͲ্ͨ͠Ͱಛ௃ྔΛநग़ • Window಺ͷ࠷େ஋ɼ࠷খ஋ɼ෼ࢄͳͲ

    4. ػցֶश 㱺 ं྆छผ͝ͱʹݕग़Ͱ͖Δ͔Λࢼߦ • େܕंɼόϯɼී௨ंɼখܕंͷ4छผ 30 Phase Extraction Phase Calibration PCA Feature Extraction Machine Learning Windowing
  49. ▪ ं྆छผ͝ͱͷݕग़ (ͨͩ͠unbalanced data) • Precision = 54.4%, Recall =

    48.5%, Accuracy = 55.3% 㱺 ୯७ͳݕग़ํ๏Ͱ͸શવμϝ (Work in Progress) ं྆ݕग़݁Ռ 31 EsHmated Actual େܕं όϯ ී௨ं খܕं େܕं 9 0 2 0 όϯ 2 3 5 0 ී௨ं 1 0 14 2 খܕं 1 0 8 0
  50. ԰֎ਓମηϯγϯά Outdoor Human Sensing 32

  51. ԰֎ਓମηϯγϯά [19] ▪ WiFi (802.11ac)Λ࢖ͬͨ԰֎ਓମηϯγϯά • ηϯγϯάྖҬ಺ʹਓ͕͍Δ͔Λਪఆ • ं྆ݕग़ͱ΄΅ಉ͡࢓૊Έ (ͨͩ͠802.11acΛ࢖༻)

    33 Training Phase Estimation Phase Data Acquisition Block Pre-Process Block Machine Learning Block Machine Learning Block AP CSI monitoring station CSI measuring station Target area Human Location Actual human location Angles ij, ij <latexit sha1_base64="0k9YN6B6zmwyhU3Us9qdUcDPFKI=">AAAB/nicdVDLSsNAFL2pr1pf8bFzM1gKLkpJ3Oiy4MZlBfuAJoTJdNKOnUzCzESooeCnuBIUxK3/4cq/cfpQWsUDFw7n3Mu5nDDlTGnH+bQKK6tr6xvFzdLW9s7unr1/0FJJJgltkoQnshNiRTkTtKmZ5rSTSorjkNN2OLyc+O07KhVLxI0epdSPcV+wiBGsjRTYR146YEHObsdV5KVqRgO77NacKZDzh3xbZZijEdgfXi8hWUyFJhwr1XWdVPs5lpoRTsclL1M0xWSI+7RrqMAxVX4+/X6MKkbpoSiRZoRGU3XxIsexUqM4NJsx1gP125uIP15lKUpHF37ORJppKsgsKco40gmadIF6TFKi+cgQTCQzzyIywBITbRorLbbwP2md1Vyn5l475Xp13kcRjuEETsGFc6jDFTSgCQTu4RGe4cV6sJ6sV+tttlqw5jeHsATr/Qsol5Vq</latexit> <latexit sha1_base64="0k9YN6B6zmwyhU3Us9qdUcDPFKI=">AAAB/nicdVDLSsNAFL2pr1pf8bFzM1gKLkpJ3Oiy4MZlBfuAJoTJdNKOnUzCzESooeCnuBIUxK3/4cq/cfpQWsUDFw7n3Mu5nDDlTGnH+bQKK6tr6xvFzdLW9s7unr1/0FJJJgltkoQnshNiRTkTtKmZ5rSTSorjkNN2OLyc+O07KhVLxI0epdSPcV+wiBGsjRTYR146YEHObsdV5KVqRgO77NacKZDzh3xbZZijEdgfXi8hWUyFJhwr1XWdVPs5lpoRTsclL1M0xWSI+7RrqMAxVX4+/X6MKkbpoSiRZoRGU3XxIsexUqM4NJsx1gP125uIP15lKUpHF37ORJppKsgsKco40gmadIF6TFKi+cgQTCQzzyIywBITbRorLbbwP2md1Vyn5l475Xp13kcRjuEETsGFc6jDFTSgCQTu4RGe4cV6sJ6sV+tttlqw5jeHsATr/Qsol5Vq</latexit> <latexit sha1_base64="0k9YN6B6zmwyhU3Us9qdUcDPFKI=">AAAB/nicdVDLSsNAFL2pr1pf8bFzM1gKLkpJ3Oiy4MZlBfuAJoTJdNKOnUzCzESooeCnuBIUxK3/4cq/cfpQWsUDFw7n3Mu5nDDlTGnH+bQKK6tr6xvFzdLW9s7unr1/0FJJJgltkoQnshNiRTkTtKmZ5rSTSorjkNN2OLyc+O07KhVLxI0epdSPcV+wiBGsjRTYR146YEHObsdV5KVqRgO77NacKZDzh3xbZZijEdgfXi8hWUyFJhwr1XWdVPs5lpoRTsclL1M0xWSI+7RrqMAxVX4+/X6MKkbpoSiRZoRGU3XxIsexUqM4NJsx1gP125uIP15lKUpHF37ORJppKsgsKco40gmadIF6TFKi+cgQTCQzzyIywBITbRorLbbwP2md1Vyn5l475Xp13kcRjuEETsGFc6jDFTSgCQTu4RGe4cV6sJ6sV+tttlqw5jeHsATr/Qsol5Vq</latexit> <latexit sha1_base64="0k9YN6B6zmwyhU3Us9qdUcDPFKI=">AAAB/nicdVDLSsNAFL2pr1pf8bFzM1gKLkpJ3Oiy4MZlBfuAJoTJdNKOnUzCzESooeCnuBIUxK3/4cq/cfpQWsUDFw7n3Mu5nDDlTGnH+bQKK6tr6xvFzdLW9s7unr1/0FJJJgltkoQnshNiRTkTtKmZ5rSTSorjkNN2OLyc+O07KhVLxI0epdSPcV+wiBGsjRTYR146YEHObsdV5KVqRgO77NacKZDzh3xbZZijEdgfXi8hWUyFJhwr1XWdVPs5lpoRTsclL1M0xWSI+7RrqMAxVX4+/X6MKkbpoSiRZoRGU3XxIsexUqM4NJsx1gP125uIP15lKUpHF37ORJppKsgsKco40gmadIF6TFKi+cgQTCQzzyIywBITbRorLbbwP2md1Vyn5l475Xp13kcRjuEETsGFc6jDFTSgCQTu4RGe4cV6sJ6sV+tttlqw5jeHsATr/Qsol5Vq</latexit> [19] M. Miyazaki et al., “Ini;al arempt on outdoor human detec;on using IEEE 802.11ac WLAN signal”, IEEE SAS, Mar 2019. (will appear)
  52. ࣮ݧ؀ڥ ▪ 9ͭͷΤϦΞ + ਓͳ͠ (ϥϕϧ0)Λ
 ଟΫϥε෼ྨ໰୊ͱͯ͠ػցֶश 34 CSI measuring

    station CSI monitoring station AP 4 1 7 5 2 8 6 3 9 30 meters 30 meters 6 meters 6 meters 0 : no human STA1 STA2 ΤϦΞ ൪߸
  53. ▪ ਫ਼౓ (ਖ਼͍͠ݕग़ͷׂ߹) • 99.86% (STA2ͷΈ࢖ͬͨ৔߹) • 56.01% (STA1ͷ Έ࢖ͬͨ৔߹)

    ݕग़݁Ռ 35 0 1 2 3 4 5 6 7 8 9 Estimated Area 0 1 2 3 4 5 6 7 8 9 Actual Area 547623 50 1 11 15 0 0 0 0 0 7 551917 27 0 330 0 19 0 0 0 0 69 550299 368 1552 4 0 8 0 0 3 0 520 546006 18 297 42 114 0 0 0 30 1711 36 550511 0 112 0 0 0 3 0 6 566 0 547076 0 577 47 25 3 0 5 45 221 0 546626 0 0 0 0 0 1 92 0 703 4 546500 0 0 0 0 0 17 0 1 5 11 546966 0 0 0 0 0 0 5 0 0 2 553393
  54. ·ͱΊ 36

  55. ·ͱΊ ▪ IoT࣮ݱʹ޲͚ͯ͸ηϯγϯά΋େ੾ ▪ ࣮ۭؒ৘ใΛऔಘ͢ΔແઢηϯαωοτϫʔΫ • Ͳ͜ʹஔ͍ͨͷ͔஌Βͳ͍ͱ͍͚ͳ͍ • ηϯγϯάର৅Λ֦େ͍ͨ͠ ▪

    ԰಺ଌҐٕज़ • ҟछແઢؒ௨৴Λ༻͍ͨηϯαଌҐ • νϟωϧؒಛੑࠩΛར༻ͨ͠BLEଌҐ ▪ ৽͍͠ηϯγϯάٕज़ • ं྆ηϯγϯά (Ի, ແઢ [Work in Progress]) • ਓମηϯγϯά (ແઢ) 37
  56. Thank you!! QuesHons & Answers 38

  57. © 2019 Shigemi ISHIDA licensed under CC BY-NC 4.0