人間情報学会

Academy of Human Informatics

第 12 回 人 間 情 報 学 会 ポ ス タ ー 発 表 集

2012 年 9 月 11 日

東京大学山上会館

╙ 12 ࿁ੱ㑆ᖱႎቇળ㩷 䊘䉴䉺䊷⊒⴫㓸㩷 ⋡ᰴ㩷㩷䋱䋮㪫㫉㪸㫁㪼㪺㫋㫆㫉㫐㩷㪪㪼㫅㫊㫀㫅㪾㩷㪹㫐㩷㪟㫌㫄㪸㫅㩷㪧㫉㫆㪹㪼㩷㪞㫌㪸㫅㪾㫎㪼㫅㩷㪣㫀㫌㪃㩷㪤㪸㫊㪸㫐㫌㫂㫀㩷㪠㫎㪸㫀㪃㩷㪢㪸㫆㫉㫌㩷㪪㪼㫑㪸㫂㫀xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxP1㩷㩷䋲䋮䊂䉴䉪䊪䊷䉪ᤨ䈱ਇ⦟ᆫ൓䈱ᬌ಴෸䈶⍶ᱜᚻᴺ㩷ዊ㣐㤗ᄹ⟤䇮☨Ỉᜏ㇢䇮ਛỈੳ䇮㜞᳤৻♿䇮ᓼ↰㩷 ⧷ᐘxxxxxxxxxxxxxxxxxxxxxP4㩷 㩷 䋼ቇ↢ㇱ㐷ᦨఝ⑲⾨䋾㩷
䋳䋮ㅪ⛯ⴊ࿶䊝䊆䉺䊥䊮䉫䈱䈢䉄䈱䉴䊙䊷䊃䊐䉤䊮䈻䈱ⴕേ⹺⼂ᚻᴺ䈱ታⵝᣇᴺ䈱ᬌ⸛㩷᣿᧛ᄢ⊓䇮Ꮉේ㩷 ࿻ඳ䇮㈬ㅧᱜ᮸xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxP8㩷㩷䋴䋮᫪ᨋ䈏ੱ㑆䈱ቇ⠌⢻ജ䈮෸䈿䈜ᓇ㗀䈮ଥ䉎ታ㛎⊛⎇ⓥ㩷ᄹ⦟᧻▸䇮ᷡ᳓㆐຦xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxP11㩷㩷䋵䋮䊓䊦䉴䉬䉝䉰䊷䊎䉴䈱䈢䉄䈱ᤨ♽೉䊂䊷䉺䊔䊷䉴䉲䉴䊁䊛䈫↢૕ᖱႎ䊎䊠䊷䉝䈱ታⵝ㩷᫪ᚭ㩷 ⾆䇮䊨䊕䉵㩷 䉩䊣䊷䊛䇮㋈ᧁ㩷 ⺈䇮᧘㩷 ᢯ᾔ䇮᫪Ꮉ㩷 ඳਯxxxxxxxxxxxxxxxxxxP14㩷㩷䋶䋮ᣣᧄ⺆േ⹖↥↢⺖㗴䈮䈍䈔䉎⺋╵⊒↢䈫⣖ᵴേ㩷੹੗⛗⟤ሶ䇮 ᩿␽㓷䇮Ꮉ෶ᢅ↵xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxP17㩷
㩷䋷䋮ᥤᾲⅣႺਅ䈮䈍䈔䉎㗡ㇱ಄ළ䈱㜞ᰴ⣖ᯏ⢻䈮෸䈿䈜ലᨐ㩷᩿␽㓷䇮Ꮉේ㕏ᒄ䇮ဌ၂෹⟤xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxP19㩷䋸䋮䊄䊤䉟㔚ᭂ䉕៞タ䈜䉎ᄙ䉼䊞䊮䊈䊦䊪䉟䊟䊧䉴⣖ᵄ䉶䊮䉲䊮䉫䉲䉴䊁䊛㩷᩿␽㓷xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxP21㩷㩷䋹䋮♖ᴤ䈱༦ⷡᛩਈ䈏ᵈᗧᯏ⢻䈮෸䈿䈜ᓇ㗀㩷ဌ၂ ෹⟤‫ ޔ‬᩿␽㓷‫ޔ‬ቝ૒⟤ ⋿xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxP23㪈㪇䋮ᗵᖱ⒖౉䈮䉋䉎๺㖸䈮ኻ䈜䉎㓐઻㒶ᕈᄌേ䈱ᄌൻ㩷Ꮉේ㕏ᒄ‫ ޔ‬᩿␽㓷xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxP25䋱䋱䋮㘩‛ฃኈ䈮䈍䈔䉎ળ⹤䈫๰ྨ䈱㑐ଥᕈ䈱ၮ␆ᬌ⸛㩷㈬ㅧᱜ᮸‫᧛ᧁޔ‬ᢕxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxP27
㪈㪉䋮㪜㫄㫆㫋㫀㫆㫅㩷㪪㪼㫅㫊㪼㩷㪚㫆㫄㫄㫌㫅㫀㪺㪸㫋㫀㫆㫅㩷㪽㫆㫉㩷㪮㪼㪹㩷㪿㪼㪸㫃㫋㪿㪺㪸㫉㪼㩷㫄㫆㫅㫀㫋㫆㫉㫀㫅㪾㩷㫊㪼㫉㫍㫀㪺㪼㩷㜞ᯅ೑੩‫ޔ‬㜞ᯅብዏ‫ޔ‬Dr. Khalid MahmoodxxxxxxxxxxxxxxxxxxxxxxxxxxxP29㩷䋼৻⥸ㇱ㐷ᦨఝ⑲⾨䋾

㪈㪊䋮䉥䊷䊃䉺䉨䉲䊮䊉䉾䉪䉻䉡䊮䈮䉋䉎䈏䉖⚦⢩䈱ᶐẢ䊶ォ⒖䈻䈱ᓇ㗀㩷ᓟ⮮⌀㉿䇮ౝጊ✍ሶ䇮⮮ේఝሶ䇮㩷 㪡㫌㫅㫄㫀㫅㪾㩷㪰㪬㪜㩷 䇮㪞㪸㪹㫆㫉㩷 㪫㪠㪞㪰䌉䇮ቶફ䈐䉂ሶxxxxxxP38㩷

第12回人間情報学会講演会ポスターセッション（2012.9.11）

Trajectory Sensing by Human Probe
Guangwen Liu̐, Masayuki Iwai̐ and Kaoru Sezakȋ̐Institute of Industrial Science, University of Tokyo
̑Center for Spatial Information Science, University of Tokyo
Email:̐liugw198209@mcl.iis.u-tokyo.ac.jp, ̐̑{masa, sezaki}@iis.u-tokyo.ac.jp
Keywords: Trajectory Sensing, Light Sensor, Smartphone, Human Probe, Ambient
Noise Sensor
Abstract:
With the development of pervasive sensing by smartphone, we can now learn oursociety and surroundings better. We suggest the way of trajectory sensing in which
participants with smartphone (called human probe) record the sensor data combinedwith daily-life locations in order to perceive our life and the physical environment.
In current work, we collect noise and light along trajectory during commuting, andpropose a novel trajectory simplification method for data reduction to facilitate further
data analysis. The sensed data is expected to create noise map, recommend safe route,and perceive human behavior, etc.
Future Outlook:
These sensing data especially ambient light are supposed to have further applications.
(1) Light at night has a strong link with economic activities, so we can evaluate
economic strength or status through illumination analysis. In fact, even in our
preliminary study, we also can find that light nearby station is far brighter thanresidential area and the bigger the station is, the more and stronger the illumination is,while noise data behaves reversely. (2) It is helpful for safety route recommendationaccording to light condition and noise condition at night. For female travelers, forexample, they consider a bright and not-so-quiet path as safer when walking at night.
(3) Moreover, indoor navigation or positioning is complicated due to failure of GPS,
while light condition along trajectory probably opens a new door to it.

−−第12回人間情報学会講演会ポスターセッション（2012.9.11）

Figure 1: A smartphone-based trajectory sensing tool which records trajectory withenvironmental data by using built-in GPS, microphone, light sensor, pressure sensor, etc.

Figure 2: Trajectory on Google map.

Figure 3: Ambient light along trajectory at night around Komaba campus, UT−−第12回人間情報学会講演会ポスターセッション（2012.9.11）

Figure 4: Ambient noise along trajectory at night around Komaba campus, UT−−第12回人間情報学会講演会ポスターセッション（2012.9.11）

࠺ࠬࠢࡢ࡯ࠢᤨߩਇ⦟ᆫ൓ߩᬌ಴෸߮⍶ᱜᚻᴺዊ㣐㤗ᄹ⟤ ‫☨ޔ‬Ỉᜏ㇢ ‫ޔ‬ਛỈੳ ‫ޔ‬㜞᳤৻♿ ‫ޔ‬ᓼ↰⧷ᐘ ᘮᙥ⟵ႶᄢቇⅣႺᖱႎቇㇱᘮᙥ⟵Ⴖᄢቇᄢቇ㒮᡽╷࡮ࡔ࠺ࠖࠕ⎇ⓥ⑼
email: core@ht‫ޕ‬sfc‫ޕ‬keio‫ޕ‬ac‫ޕ‬jp

Keywords: bad posture‫ ޔ‬posture estimation‫ ޔ‬ubiquitous sensingⷐᣦᧄ⎇ⓥߢߪ‫ੱޔ‬㑆ߩᆫ൓⁁ᘒࠍᛠីߒ‫ޔ‬ਇ⦟ᆫ൓߇ᬌ⍮ߐࠇߚ႐วߦ⍶ᱜߔࠆࠃ߁ଦߔࠪࠬ࠹ࡓࠍឭ᩺ߔࠆ‫◲ޕ‬නߥࠕࠢ࠴ࡘࠛ࡯࡚ࠪࡦᯏ⢻ࠍᜬߞߚ࠙ࠚࠕ࡜ ࡉ࡞࠺ࡃࠗࠬࠍ⵾૞ߒ‫ࠬࠗࡃ࠺ޕࠆߔ⌕ⵝߦᤨࠢ࡯ࡢࠢࠬ࠺ޔ‬ߪ࡙࡯ࠩߩⴕേࠍ࡝ࠕ࡞࠲ࠗࡓߢ᷹ⷰߒ‫ޔ‬₀⢛ߥߤߩਇ⦟ᆫ൓߇ᬌ⍮ߐࠇߚ႐ว‫ޔ‬⍶ᱜߔࠆࠃ߁௛߈߆ߌࠆ‫ޕ‬ᱜߒ޿ᆫ൓ߢᣣᏱ↢ᵴࠍㅍࠆߎߣߦࠃࠅ‫∋ޔ‬ഭࠍシᷫߔࠆߎߣࠍ⋡ᜰߔ‫ޕ‬੹ᓟߩዷᦸਇ⦟ᆫ൓ߪੱߦࠃߞߡ∝⁁߇᭽‫ߦࠇߙࠇߘޔࠅ޽ߢޘ‬ኻߒㆡಾߥࠕࠢ࠴
ࡘࠛ࡯࡚ࠪࡦ߇ᔅⷐߢ޽ࠆ‫੹ޔߚ߹ޕ‬࿁ߪ࠺ࠬࠢࡢ࡯ࠢᤨߣ㒢ቯࠍߒߚ߇‫ޔ‬ࠃࠅ᏷ᐢ޿ਇ⦟ᆫ൓ߩේ࿃ߦኻᔕߔࠆߚ߼‫ޔ‬ᣣᏱ↢ᵴਛߦㆡวߔࠆ࠺ࡃࠗ
ࠬߩ⸳⸘߇ᔅⷐߣߥࠆ‫ޕ‬−−

第12回人間情報学会講演会ポスターセッション（2012.9.11）

 ߪߓ߼ߦ

 ࠕࡊࡠ࡯࠴ߩ⒳㘃ੱ㑆߇ᦨ߽∋ഭߒߦߊ޿ᆫ൓ߪ‫ޔ‬⢛╭ࠍ࡝ᧄ⎇ⓥߢߪ‫ޔ‬㧟ߟߩࠕࡊࡠ࡯࠴ߦࠃߞߡ࡙

࡜࠶ࠢࠬߒߡિ߫ߒߚ⁁ᘒߢ޽ࠆ‫ޔߒ߆ߒޕ‬࡯ࠩߩᆫ൓⍶ᱜࠍߪ߆ࠆ‫ޕ‬⢛ߥߤߩ⢛㛽ߦߥࠎࠄ߆ߩᱡߺࠍᛴ߃ߡ޿ ࠬ࠲ࡦ࠼࡜ࠗ࠻

ࠆੱߦߣߞߡ‫ߚߞ޿߁ߘޔ‬ᆫ൓ࠍ⛽ᜬߔࠆߎ✭߿߆ߥਇ⦟ᆫ൓߇ᬌ⍮ߐࠇߚ႐ว‫ޔ‬ߣߪ㔍ߒ޿‫ޕ‬ਇ⦟ᆫ൓߇ਈ߃ࠆᓇ㗀ߪり૕࡮࠺ࡃࠗࠬߪࠬ࠲ࡦ࠼࡜ࠗ࠻ߦᖱႎࠍ

♖␹⊛ߥ㕙ߢᄙߊሽ࿷ߔࠆ‫ޕ‬ㅢ⍮ߔࠆ‫ߩࠩ࡯࡙ߪ࠻ࠗ࡜࠼ࡦ࠲ࠬޕ‬ߘߎߢᧄ⎇ⓥߢߪ‫࡯࡙ߡ޿↪ࠍࠬࠗࡃ࠺ޔ‬ਇ⦟ᆫ൓ߩ⒟ᐲߦᔕߓߡᾖᐲࠍ⺞శࠩߩᆫ൓⁁ᘒࠍᛠីߒ‫ޔ‬ᖡ޿ᆫ൓߇ᬌ⍮ߐࠇߒ‫ޔ‬ㅢ⍮ࠍⴕ߁‫ޕ‬ߚ႐วߦ⍶ᱜߔࠆࠃ߁ଦߔࠪࠬ࠹ࡓࠍឭ᩺ ࡃࠗࡉ࡟࡯࡚ࠪࡦ

ߔࠆ‫ޕ‬ਃゲടㅦᐲ࠮ࡦࠨ‫ޔ‬࿶ജ࠮ࡦࠨ෸߮◲ᆫ൓ߩᱡߺ߇㑣୯ࠍ⿥߃ߚ႐ว‫ࡃ࠺ޔ‬නߥࠕࠢ࠴ࡘࠛ࡯࡚ࠪࡦᯏ⢻ࠍᜬߞߚ࠙ࠚࠗࠬ⥄りߩࡃࠗࡉ࡟࡯࡚ࠪࡦᯏ⢻ߦࠕ࡜ࡉ࡞ߥ࠺ࡃࠗࠬࠍ೙૞ߒ‫ߦࠩ࡯࡙ޔ‬ᣣᏱࠃߞߡㅢ⍮ࠍⴕ޿‫ޔ‬ᆫ൓ߩᡷༀࠍଦߔ‫ޕ‬↢ᵴਛߦ߅޿ߡりߦઃߌߡ߽ࠄ߁‫ࡃ࠺ߩߎޕ‬ ࠺ࠗ࡝࡯࡟ࡐ࡯࠻

ࠗࠬߪ࡙࡯ࠩߩⴕേࠍ࡝ࠕ࡞࠲ࠗࡓߢ⸘᷹

࡙࡯ࠩߩᣣਛߩᆫ൓⁁ᘒࠍ߹ߣ߼‫ࠣޔ‬ߒ‫ޔ‬₀⢛ߥߤߩਇ⦟ᆫ൓߇ᬌ⍮ߐࠇߚ႐ว‫ޔ‬࡜ࡈࠖࠞ࡞ߥᖱႎߣߒߡࠬࡑ࡯࠻ࡈ

ᒝ೙ߔࠆࠃ߁௛߈߆ߌࠆ‫ߩߘޔߚ߹ޕ‬ᣣ࡙࡯ࠜࡦߦㅢ⍮ߔࠆ‫ޕ‬೨ᣣ߿‫ࠇߘޔ‬એ೨ߩࠩ߇ߤ߁޿߁ᆫ൓⁁ᘒߢ޿ߚ߆ࠍᣣᲤߦࡈ

㓸⸘ᖱႎߣᲧセߔࠆߎߣߦࠃࠅ‫ޔ‬ᆫ൓ࠖ࡯࠼ࡃ࠶ࠢߒ‫⥄ࠩ࡯࡙ޔ‬りߩᗧ⼂ᡷༀࠍ⹜ߩᡷༀࠍ⋡ߢ⷗ߡ⹺⼂ߔࠆߎߣ߇ߢ

ߺࠆ‫߇ࠩ࡯࡙ޕ‬ᱜߒ޿ᆫ൓ߢᣣᏱ↢ᵴࠍㅍࠆ߈ࠆ‫࡯ࡌ࠴ࡕߩࠩ࡯࡙ޔࠅࠃߦࠇߎޕ‬ߎߣߦࠃࠅ‫⦟ޔ‬ᅢߥ♖␹⁁ᘒߢ޿ࠆߎߣࠍ⋡࡚ࠪࡦ⛽ᜬ෸߮࠺ࡃࠗࠬⵝ⌕ࠍᜬ⛯ᜰߔ‫ޕ‬

ߔࠆࠃ߁௛߈߆ߌࠆ‫ޕ‬ ࠺ࡃࠗࠬߩ⸳⸘

 ⋡⊛ߣᧄ⎇ⓥߩὶὐᧄ࠺ࡃࠗࠬߦߪ‫ߚߩࡦ࡚ࠪ࡯ࠛࡘ࠴ࠢࠕޔ‬ᧄ⎇ⓥߢߪ‫ࡢࠢࠬ࠺ߩߢࡦࠦ࠰ࡄ࠻࡯ࡁޔ‬߼ߩᝄേࡕ࡯࠲‫ޔ‬೙ᓮߩߚ߼ߦ㧟ゲടㅦᐲ࠮࡯ࠢᤨߦᆫ൓ߩᱡߺࠍᬌ಴ߒ‫ޔ‬ᡷༀߔࠆࠃ߁

ࡦࠨࠍ៞タߒߡ޿ࠆ‫ޕ‬ᚻߦ෼߹ࠆዊဳࠨࠗ࠭

௛߈߆ߌࠆߎߣࠍ⋡⊛ߣߔࠆ‫ޕ‬ߣߒ‫⽶ࠩ࡯࡙ޔ‬ᜂߩシᷫࠍ⹜ߺࠆ‫ޕ‬ ⦟ᆫ൓ߩቯ⟵ ࠪࠬ࠹ࡓ᭴ᚑ࿑

ᐳ૏ᤨߦ߅ߌࠆㆡಾߥᆫ൓ߣߪ‫ޔ‬㗡ㇱ߇߹࿑ߦࠪࠬ࠹ࡓ᭴ᚑ࿑ࠍ᜼ߍࠆ‫ߩࠩ࡯࡙ޕ‬ߞߔߋ߆ߟ⢷ㇱ߇೨ᣇߦ޽ࠅ‫ޔ‬ਔ⢋߇ᓟᣇߦᆫ൓ߪ࠺ࡃࠗࠬౝߩടㅦᐲ࠮ࡦࠨߦࠃߞߡ

߭߆ࠇ‫ޔ‬⣻ㇱ߇೨ߦ಴ߡ޿ߥ޿ᆫ൓ߣቯ⟵ߐ⸘᷹ߐࠇ‫ޔ‬ਇ⦟ᆫ൓߇ᬌ⍮ߐࠇߚ႐ว‫ޔ‬ᆫ൓ࠇࠆ‫⎇ᧄޕ‬ⓥߢߪ਄ඨりߦ⌕⋡ࠍߒ‫ޔ‬௑ᢳߦ࠺࡯࠲ߪ࠺࡯࠲ᆫ൓ࡕࠫࡘ࡯࡞߳ォㅍߐࠇࠃߞߡਇ⦟ᆫ൓ࠍផኤߔࠆ‫ޕ‬ࠆ‫ߩߘޕ‬ᖱႎߪࠕࠢ࠴ࡘࠛ࡯࠻ታⴕࡕࠫࡘ࡯

 ࠕࡊࡠ࡯࠴

࡞ߦવ㆐ߐࠇ‫ࠊߥⴕ߇࠴࡯ࡠࡊࠕ߳ࠩ࡯࡙ޔ‬ࠇࠆ‫ޔߚ߹ޕ‬ᆫ൓࠺࡯࠲ߪᣣ೎ᄌൻ▚಴ࡕࠫᧄ▵ߢߪ‫࠴࡯ࡠࡊࠕߩ߳ࠩ࡯࡙ޔ‬ᣇᴺߦߟ

ࡘ࡯࡞߽߳ㅍାߐࠇ‫ߣ࠲࡯࠺࡞ࠞࠖࡈ࡜ࠣޔ‬޿ߡㅀߴ‫ߚ߃߹〯ࠍࠇߘޔ‬਄ߢ࠺ࡃࠗࠬߩ⸳

ߒߡ࡙࡯ࠩߪ#PFTQKF┵ᧃ਄ߢ⏕⹺ߔࠆߎ⸘ߦߟ޿ߡㅀߴࠆ‫ޕ‬ߣ߇น⢻ߢ޽ࠆ‫ޕ‬−−

第12回人間情報学会講演会ポスターセッション（2012.9.11）

ⴕ߁‫⥄ࠩ࡯࡙ޔߚ߹ޕ‬り߇ࠣ࡜ࡈࠖࠞ࡞
࠺࡯࠲ߣߒߡ⹺⍮ߔࠆࠃ߁ㅢ⍮ߔࠆ‫ߎޕ‬ࠇࠄ߇ⵍ㛎⠪ߦኻߒߤߩ⒟ᐲ᦭↪ߥ߽ߩߛߞߚߩ߆ࠍ⏕⹺ߔࠆ‫ޕ‬ ♖␹⁁ᘒߩᄌൻ
ᣣᲤߦ࡙࡯ࠩࠕࡦࠤ࡯࠻ࠍߣࠅ‫ⵍޔ‬㛎⠪߇ߤ߁ᗵߓߚߩ߆ࠍߺࠆ‫ޔߚ߹ޕ‬ታ㛎ਛ࿑㧝㧦ࠪࠬ࠹ࡓ᭴ᚑ࿑

ߦ⣖ᵄߩ᷹ቯࠍⴕ޿‫ޔ‬ᆫ൓ߣ㜞޿㑐ㅪᕈ߇ᓧࠄࠇߚ߆ࠍ⺞ᩏߔࠆ‫ޕ‬ ታⵝ⸒⺆ ᵹ↪ᕈ
୘ੱߩ૕ᩰᏅߦኻᔕߔࠆㆡᔕᕈߦߟ޿ߡ

ታⵝ⸒⺆ߪ#TFWKPQ⸒⺆ߣߔࠆ‫ޕ‬ၮ⋚ߦ
#TFWKPQRTQOKPKࠍ↪޿‫ޔ‬ᝄേࡕ࡯࠲ߦ౞⋚⹏ଔߔࠆ‫ޕ‬ဳᝄേࡕ࡯࠲ࠍ೑↪ߔࠆ‫ߩࠬࠗࡃ࠺ޔߚ߹ޕ‬ ⹏ଔታ㛎௑߈ࠍขᓧߔࠆਃゲടㅦᐲ࠮ࡦࠨࠍ៞タߒ⹏ଔታ㛎ߪ‫ޔ‬ᰴߩᚻ㗅ߢⴕ߁‫ⵍޕ‬㛎⠪ߪ‫ޔ‬ߡ޿ࠆ㧔-:/㧕‫ޕ‬#PFTQKF෸߮ࠬ࠲ࡦ

₀⢛ߩ⥄ⷡ߇޽ࠆੱ‛߹ߚߪ⥄ઁߣ߽ߦ⦟

࠼࡜ࠗ࠻ߣߩ࠺࡯࠲ㅢାߩߚ߼‫<ޔ‬KI$GGડ

ᆫ൓ߣߩ⹺⼂߇޽ࠆੱ‛ߩ㧞ࡄ࠲࡯ࡦߦኻ

↹ߩή✢ㅢାᯏ⢻ࠍ៞タߒߚዊဳࡕࠫࡘ࡯

ߒߡⴕ߁‫⦟ߕ߹ޕ‬ᆫ൓ߛߣ⥄ⷡߩ޽ࠆੱ‛ߦ࡞ߢ޽ࠆ:$GG㧔5GTKGU㧕ࡕࠫࡘ࡯ኻߒߡߪ‫ޔ‬㧝ᣣ࠺ࡃࠗࠬࠍⵝ⌕ߒߚ߁߃ߢㅢ

࡞ࠍ૶↪ߔࠆ‫ޕ‬ᏱㅢࠅߩᣣᏱᵴേࠍߒߡ߽ࠄ޿‫ߩ࠲࡯࠺ޔ‬෼㓸෸߮ቇ⠌ࠍߐߖࠆ‫ޕ‬ᰴߦ‫ޔ‬೎ᣣߦ৻ቯᤨ㑆 ታ㛎ᣇᴺ

޽ࠆ႐ᚲߢߥࠎࠄ߆ߩേ૞ࠍߒߡ߽ࠄ޿‫ޔ‬ᱡᧄ┨ߢߪ‫↪᦭ߩࠬࠗࡃ࠺ޔ‬ᕈߦߟ޿ߡᬌ⸽

ߺߩᬌ⍮߇޽ࠆ߆‫߇ࡦ࡚ࠪ࡯ࠛࡘ࠴ࠢࠕޔ‬௛ߔࠆታ㛎ᣇᴺߦߟ޿ߡㅀߴࠆ‫ޕ‬ߊ߆ߤ߁߆ߥߤ‫ߩߘޔ‬㓙ߩ࠺ࡃࠗࠬߩ᜼േߦ

 ታ㛎ⅣႺ

⌕⋡ߔࠆ‫ޕ‬ਇ⦟ᆫ൓ߩ⥄ⷡ߇޽ࠆ࡙࡯ࠩߦኻ

ⵍ㛎⠪ߪᱦ߆ࠄᱦߩᄢቇ↢↵ᅚᷙߒߡ߽ห᭽ߦⴕ߁‫ⵍ߈ߣߩߎޔߚ߹ޕ‬㛎⠪ߦวੱߣߔࠆ‫ޕ‬ታ㛎႐ᚲߪ‫⎇ޔ‬ⓥቶౝߩ࠺ߪ౒ߦ⣖ᵄ࠮ࡦࠨࠍⵝ⌕ߒ‫ޔ‬⣖ᵄ߆ࠄ♖␹⁁ࠬࠢࠬࡍ࡯ࠬߢⴕ߁‫ޕ‬ታ㛎ᦼ㑆ߪ㧝ㅳ㑆ߣߔᘒࠍ⸃ᨆߔࠆ‫ߩߎޕ‬ታ㛎ߪ㧝ੱߦߟ߈㧝ㅳ㑆ࠆ‫ޕ‬⸘᷹ߒ‫ޔ‬ᣣᲤߩ⚿ᨐߪ࠺࡯࠲ߣߒߡ߹ߣ߼‫ޔ‬ ⹏ଔᣇ㊎࡙࡯ࠩߦឭଏߔࠆ‫ޕ‬ ᱡߺߩᬌ⍮♖ᐲࠪࠬ࠹ࡓ߇▚಴ߒߚᆫ൓ߩᱡߺࠍ‫࡯࡙ޔ‬ ߹ߣ߼

ࠩࠕࡦࠤ࡯࠻ߦࠃߞߡቯᕈ⊛ߦ⹏ଔߔࠆ‫ޕ‬࠺ࠬࠢࡢ࡯ࠢᤨߦ߅ߌࠆਇ⦟ᆫ൓ߩ⍶ᱜ߹ߚ‫ߩߎޔ‬㓙㧟ゲടㅦᐲ࠮ࡦࠨߦࠃࠆ┙ᚻᴺߦߟ޿ߡឭ᩺ࠍⴕߞߚ‫࡯࡙ߪࠬࠗࡃ࠺ޕ‬૏࡮ᐳ૏್ቯߦߩ᦭↪ᕈߦߟ޿ߡ߽ߺࠆ‫ޕ‬ࠩߩ⽶ᜂߦߥࠄߥ޿࠙ࠚࠕ࡜ࡉ࡞ߥ߽ߩߣ ࠕࠢ࠴ࡘࠛ࡯࡚ࠪࡦߩ᦭↪ᕈߒ‫࠻ࠗ࡜࠼ࡦ࠲ࠬߪࠩ࡯࡙ޔ‬෸߮ࡃࠗࡉ࡟࡯

ᧄታ㛎ߢߪ‫⺞ߩ࠻ࠗ࡜࠼ࡦ࠲ࠬޔ‬శ೙ᓮ࡚ࠪࡦߦࠃࠆ◲නߥࠕࠢ࠴ࡘࠛ࡯࡚ࠪࡦߦ

ߦࠃࠆࠕࠢ࠴ࡘࠛ࡯࡚ࠪࡦ෸߮࠺ࡃࠗࠬ

ࠃߞߡᆫ൓ߩ⍶ᱜࠍଦߐࠇࠆ‫ޕ‬⥄૕ߩᝄേߦࠃࠆࠕࠢ࠴ࡘࠛ࡯࡚ࠪࡦࠍ

−−੹ᓟߩ⺖㗴ߣߒߡ‫ޔ‬ਇ⦟ᆫ൓ߪੱߦࠃߞߡ

第12回人間情報学会講演会ポスターセッション（2012.9.11）∝⁁߇᭽‫ߩࠇߙࠇߘޔࠅ޽ߢޘ‬ේ࿃ߦኻߒㆡ

ᴺ߳ߩ࡙࡯ࠩㆡᔕᯏ᭴ߩዉ౉㧘ᨋᢅ᮸‫ޔ‬ዊỈಾߥࠕࠢ࠴ࡘࠛ࡯࡚ࠪࡦ߇ᔅⷐߢ޽ࠆ‫ࠇߘޕ‬᡽ඳ‫᧲ޔ‬੩ᄢቇ㧘㔚ሶᖱႎㅢାቇળ✚วᄢળ

ߦട߃‫ޔ‬ᆫ൓ߦᱡߺ߇⷗ࠄࠇߚ႐วߩㅢ⍮ᣇ$/CT

ᴺߥߤ‫࠴࡯ࡠࡊࠕޔ‬ᣇᴺߦߟ޿ߡߩᕁ⚝෸߮=?ዊỈ᡽ඳᎹේ࿻ඳᎹ⷏⋥᫪Ꮉඳ

ታⵝࠍⴕ߁‫ޔߡߒߘޕ‬Ᏹߦりߦ⌕ߌࠆߎߣ߇

ਯ㧦⋭⾗Ḯ࠺ࡃࠗࠬߦ߅ߌࠆゲടㅦᐲ࠮ࡦ

ߢ߈ࠆ࠙ࠚࠕ࡜ࡉ࡞ߥ࠺ࡃࠗࠬߣ޿߁ⷰὐ

ࠨࠍ↪޿ߚᆫ൓ផቯᚻᴺᖱႎಣℂቇળ⎇߆ࠄ‫ᦨߩࠬࠗࡃ࠺ޔ‬ㆡൻࠍ࿑ࠆᔅⷐ߇޽ࠆ‫ޕ‬ⓥႎ๔࡙ࡆࠠ࠲ࠬࠦࡦࡇࡘ࡯࠹ࠖࡦࠣࠪ

ࠬ࠹ࡓ⎇ⓥળ‫ޔ‬0Q7$+  =?4QUG,QJPUQP‫ޔ‬,CPGVXCPFGT.KPFGPෳ⠨ᢥ₂

;XQPPG4QIGTU6Q$W\\QTPQVVQ$W\\=?ᯌౝാ‫ޔ‬ᄢႦศೣ‫ޔ‬ᄢቇ↢ߦ߅ߌࠆ₀⢛‫ޔ‬+ORTQXKPICYCTGPGUUQHRQUVWTGVJTQWIJ⣶∩࡮⢋ಝࠅߩ⊒⃻₸ߣߘߩኻ╷ߦߟ޿ߡߩXKDTQVCEVKNGHGGFDCEM‫ޔ‬6JG1RGP⺞ᩏ‫ޔ‬ർᶏ㆏ᄢቇᄢቇ㒮ᢎ⢒ቇ⎇ⓥ㒮♿ⷐ

7PKXGTUKV[ %*+ ╙㧝㧜㧠ภ  =?ෘ↢ഭ௛⋭㧦਎Ꮺຬߩஜᐽ⁁ᴫ ⥄ⷡ∝=?Ⴎ↰ༀᦶ‫ޔ‬ਇㆡಾᆫ൓∝୥⟲‫ޕ‬ᣣᧄ⥃ᐥ⁁ߩ⁁ᴫᐔᚑ㧞㧞ᐕ࿖᳃↢ᵴၮ␆⺞ᩏߩ

ౝ⑼කળળ⹹㧔㧕㧦  ᭎ᴫ  =?ᱞን↱㓶‫ޔ‬ᆫ൓ߦߟ޿ߡߩᕁ⚝ℂቇ≮JVVRYYYOJNYIQLRVQWMGKUCKMKPJYᴺ㧞㧠㧔㧝㧕MV[QUCMV[QUCJVON=?,GPPKHGT*GCNG[.COC0CEJOCP=?⼾᳗ᢅብㆇേེ∔ᖚߩㅴⴕ੍㒐ࡂࡦ

5WUJOKVC5WDTCOCPKCP,WPCKVJ࠼ࡉ࠶ࠢකᱤ⮎಴

5JCJCDFGGP‫ޔ‬/CTICTGV/QTTKU1WVQHVJG

=?6JG2QUVWTG5WURGPFGTUUGPUGU.CDCPFKPVQVJG(TC[6QYCTFU/QFGNKPIRQUVWTGKPTGCNVKOGCPFXKCXKDTCVKQP'OQVKQPKP'XGT[FC[.KHG

CNGTVUVJGWUGTVQDCFRQUVWTG

2GTXCUKXG  %QORWVCVKQPCN&GUKIP.CD  =?ゲടㅦᐲ࠮ࡦࠨࠍ↪޿ߚᆫ൓ផቯᚻ

−− 

第12回人間情報学会講演会ポスターセッション（2012.9.11）

ㅪ⛯ⴊ࿶ࡕ࠾࠲࡝ࡦࠣߩߚ߼ߩ
ࠬࡑ࡯࠻ࡈࠜࡦ߳ߩⴕേ⹺⼂ᚻᴺߩታⵝᣇᴺߩᬌ⸛       ᣿᧛ ᄢ⊓ 1, Ꮉේ ࿻ඳ 1,2㈬ㅧᱜ᮸  1᧲੩ᄢቇ ᄢቇ㒮ᖱႎℂᎿቇ♽⎇ⓥ⑼㧘ᢥ੩඙ᧄㇹ 7-3-1
2School of Electrical and Computer Engineering, Georgia Institute of Technology,
85 Fifth Street NW, Atlanta, GA, USA
3␹ᄹᎹᄢቇ Ꮏቇㇱ㧘ᮮᵿᏒ␹ᄹᎹ඙౐ⷺᯅ 3-27-1
            email : akimura@akg.t.u-tokyo.ac.jp
Keywords: activity recognition, continuous BP monitoring, smartphone sensing, elderly,
lifestyle-related diseases ⷐᣦ ᧂᦦ᦭ߩ㜞㦂ൻ߇ㅴ߻ਛߢ‫ޔ‬㜞㦂⠪ߩᘟᕈ∔ᖚ▤ℂ߇໧㗴ߣߥߞߡ޿ࠆ‫߽ߢ߆ߥޕ‬㜞ⴊ࿶⟕ᖚ₸ߪ㕖Ᏹߦ㜞ߊ‫ޔ‬㜞ⴊ࿶ࠍၮ⋚ߣߔࠆ᭽‫੍ࠍ∛∔ߥޘ‬㒐ߔࠆߦߪ᥉Ბ߆ࠄߩ෩ᩰߥ▤ℂ߇ᔅⷐߣߥࠆ‫ⴊޕ‬࿶ߩ⥄േ⺞▵߇⎕✋ߒㆊᐲߩⴊ࿶ᄌേ߇⿠߈ࠇ߫┙ߜߊࠄߺ߿߼߹޿‫ߪߡ޿߭ޔ‬ォୟ࡝ࠬࠢߦ߹ߢߟߥ߇ࠆ‫ߪ⽎⃻ߩࠄࠇߎޕ‬㜞㦂⠪ᧄੱߩ↢ᵴߩ᏷߿⾰ࠍᄢ߈ߊ៊ߨࠆ߈ߞ߆ߌߦ߽ߥ
ࠅᓧࠆߚ߼‫ߊߴࠆߥޔ‬㜞㦂⠪ߩⴊ࿶ᄌേࠍㅪ⛯⊛ߦ‫⽶ߟ߆ޔ‬ᜂࠍ߆ߌߕߦ‫⥃ޔ‬ᐥ⸻≮ߦ߅ߌࠆ㜞㦂⠪ߩஜᐽ▤ℂߦᵴ↪ߔࠆᣇᴺ߇᳞߼ࠄࠇߡ޿ࠆ‫ޕ‬ ߹ߚ㧘㜞㦂⠪ߦ߅޿ߡߪዞኢᤨ‫ޔᤨ┙⿠ޔ‬㘩੐ᓟߥߤߦ഍⊛ߥⴊ࿶ᄌൻ߇⿠߈‫ߚߒ߁ߎޔ‬⍴ᦼ⊛ߥⴊ࿶ᄌേ߇਄⸥ߩࠃ߁ߥ∝⁁ࠍᒁ߈⿠ߎߒ߿ߔߊߥࠆ∝଀߇޽ࠆ‫੍ࠍ⁁∝ޕ‬㒐ߔࠆߚ߼ߦߪ‫ޔ‬୘‫ߩޘ‬ᖚ⠪ߩ⍴ᦼ⊛ߥⴊ࿶ᄌേߩࡕ࠾࠲࡝ࡦࠣࠍⴕ߁ߣหᤨߦߘߩⴕേᖱႎࠍᬌ಴ߒ‫ޔ‬㑐ଥᕈࠍតࠆߎߣ߇㊀ⷐߢ޽ࠆ‫ޕ‬ ᧲੩ᄢቇ ጊ↰৻㇢ᢎ᝼ߩ࠴࡯ࡓߢߪ‫ߣࡈࠞޔ‬๭߫ࠇࠆⴼ⁁ߩࡌ࡞࠻ࠍ↪޿ߕߦㅪ⛯⊛ߦⴊ࿶ߩࡕ࠾࠲࡝ࡦࠣ߇น⢻ߥ࠙ࠚࠕ࡜ࡉ࡞ⴊ࿶࠮ࡦࠨࠍ㐿⊒ߒߡ߈ߚ‫ޕ‬⢷ㇱߦⵝ⌕ߒߚᔃ㔚࠮ࡦࠨ߆ࠄ
ᓧࠄࠇࠆᔃ㔚ߩ R ᵄߣ‫ޔ‬⡊ߚ߱ߦⵝ⌕ߒߚ⣂ᵄ⸘߆ࠄᓧࠄࠇࠆ⣂ᵄߩ┙ߜ਄߇ࠅὐߣߩᤨ㑆Ꮕ߆ࠄ⣂ᵄવ㆐ᤨ㑆ࠍ⸘▚ߒ‫ޔ‬෼❗ᦼⴊ࿶ࠍផቯߔࠆ߽ߩߢ޽ࠆ‫ޔࠅࠃߦࠇߎޕ‬ᓥ᧪ߩࠞࡈࠍ↪޿ࠆ
ᚻᴺࠃࠅ߽ૐଚⷅ߆ߟ㜞㗫ᐲߦⴊ࿶ࠍ᷹ቯߢ߈ࠆ (࿑ 1)‫ޕ‬ ߹ߚ‫ޔ‬Human Activity Sensing Consortium (HASC) ߆ࠄឭଏߐࠇࠆടㅦᐲ࠺࡯࠲ࠦ࡯ࡄࠬ
“HASC2010corpus” ࠍ⸠✵࠺࡯࠲ߦ↪޿ߡ‫ੱޔ‬㑆ߩၮᧄ⊛ߥⴕേࠍࡠࡃࠬ࠻ߦ⹺⼂ߔࠆᚻᴺߦߟ
޿ߡᬌ⸛ࠍ㊀ߨߡ߈ߚ‫ߦࡦࠜࡈ࠻࡯ࡑࠬޕ‬ౝ⬿ߐࠇࠆ 3 ゲടㅦᐲ࠮ࡦࠨߩࡁ࡞ࡓ୯ߩߺࠍ૶↪ߒߡ‫ޔ‬㕒ᱛ‫ޔ‬ᱠⴕ‫ޔࡊ࠶ࠠࠬޔⴕ⿛ޔ‬㓏Ბ᣹㒠ߩၮᧄ 6 ⴕേߦ߅޿ߡ⚂ 84%ߩ⹺⼂₸ࠍታ⃻ߒߡ޿ࠆ (⴫ 1)‫ޕ‬ ੹࿁ߎ߁ߒߚขࠅ⚵ߺࠍ߽ߣߦ‫ޔ‬Android ┵ᧃࠍ↪޿ߡ┵ᧃౝߢⴕേߩ⹺⼂ࠍⴕ޿‫⥄ࠍࠣࡠޔ‬േ⊛ߦ⸥㍳ߔࠆࠕࡊ࡝ࠤ࡯࡚ࠪࡦࠍ⹜૞ߒߚ (࿑ 2)‫ޕ‬J48 ᳿ቯᧁࠍಽ㘃ེߣߒߡ↪޿‫ ⚂ޔ‬50Hz ߩടㅦᐲ࠺࡯࠲߆ࠄᐔဋ‫ޔ‬ಽᢔ‫ޔ‬๟ᵄᢙ㗔ၞߦ߅ߌࠆࠛࡀ࡞ࠡ࡯ߣ޿ߞߚ․ᓽ㊂ࠍ↪޿ߡ⹺⼂ࠍⴕߞߡ޿ࠆ‫ޕ‬ᯏ᪾ቇ⠌↪✚ว࠷࡯࡞ࠠ࠶࠻ “Weka” ࠍ Android ┵ᧃ↪ߦ⒖ᬀߒߚ “Weka for Android”1࡜ࠗࡉ࡜࡝ࠍ↪޿ࠆߎߣߢ‫ޔ‬ኈᤃߦ┵ᧃౝߢ⹺⼂ࠍⴕ߁ߎߣ߇น⢻ߢ޽ߞߚ‫ޕ‬ ៤Ꮺ┵ᧃࠍ↪޿ߚⴊ࿶⸘ߣടㅦᐲ࠮ࡦࠨߦࠃࠆⴕേ⹺⼂ࠍ૬↪ߒߡ㜞㦂⠪ߩ⥃ᐥ⸻≮ߦᵴ↪ߔࠆ੐ߢ‫ޔ‬㜞㦂⠪ߩᣣᏱ↢ᵴᵴേᐲߩ⛽ᜬߦߟߥ߇ࠆߎߣ߇ᦼᓙߐࠇࠆ‫ޕ‬1

Available from https://github.com/rjmarsan/Weka-for-Android−−第12回人間情報学会講演会ポスターセッション（2012.9.11）

 ੹ᓟߩዷᦸzᖚ⠪ߐ߹߳ߩ⽶ᜂߣක≮㑐ଥ⠪߳ߩࡈࠖ࡯࠼ࡃ࠶ࠢ ៤Ꮺ┵ᧃࠍ↪޿ߚ੹࿁ߩᚻᴺࠍታ㓙ߩ⥃ᐥߩ႐ߢ૶↪ߔࠆߣ߈ߩ໧㗴ߣߒߡ‫ߪᧃ┵ޔ‬㜞㦂⠪ߦߣߞߡりシߦⵝ⌕ߢ߈ࠆߎߣ߇ᦸ߹ߒ޿‫ߚߒ߁ߘޕ‬㈩ᘦ߆ࠄ੹࿁⣂ᵄ⸘ߩⵝ⌕႐ᚲߣߒߡ⡊ߚ߱ࠍㆬࠎߢ޿ࠆ߇‫ⴕޔ‬േ⹺⼂ߩߚ߼ߩടㅦᐲ࠮ࡦࠨ߽฽߼‫ޔ‬㜞㦂⠪߇ࠃࠅ㆑๺ᗵߥߊⵝ⌕ߢ߈ࠆ߆߇⺖㗴ߢ޽ࠆߣ⠨߃ࠄࠇࠆ‫ޕ‬ ߹ߚ‫ࠣࡦࠪࡦ࠮ޔ‬ᓟߦ▚಴ߒߚⴊ࿶࠺࡯࠲ߣⴕേ࠺࡯࠲ࠍක≮㑐ଥ⠪߇⷗߿ߔ޿ᒻߣߒߡ޿߆
ߦㄦㅦߦ⴫␜ߢ߈ࠆ߆߽ᄢ߈ߥ໧㗴ߢ޽ࠆ‫ⴊޕ‬࿶ߣⴕേ࠺࡯࠲ߩ㑆ߦ⇣Ᏹ߇ᬌ⍮ߐࠇߚߣ߈ߦ‫ޔ‬ᖚ⠪ߐ߹߽฽߼ߡಽ߆ࠅ߿ߔ޿ᒻߢ࠺࡯࠲ࠍ౒᦭ߔࠆࠗࡦ࠲ࡈࠚ࡯ࠬࠍ↪ᗧߔࠆᔅⷐ߇޽ࠆ‫ޕ‬z៤Ꮺ┵ᧃߩᶖ⾌㔚ജ ੹࿁ߩᚻᴺߦ㒢ࠄߕ‫ޔ‬៤Ꮺ┵ᧃࠍ↪޿ߚ࠮ࡦࠪࡦࠣಽ㊁ߦ߅޿ߡᄢ߈ߥ⺖㗴ߣߥߞߡ޿ࠆߩߪ‫ޔ‬࠮ࡦࠪࡦࠣߦ↪޿ࠆ┵ᧃߩᶖ⾌㔚ജߢ޽ࠆ‫ߩ࡯࡝࠹࠶ࡃࠆߔߣ߼ߓߪࠍࡦࠜࡈ࠻࡯ࡑࠬޕ‬㒢ࠄࠇߚዊဳߩ៤Ꮺ┵ᧃߢߪ‫ⴊޔ‬࿶࡮ⴕേࠍ࠮ࡦࠪࡦࠣߔࠆࠕࡊ࡝ࠤ࡯࡚ࠪࡦࠍ 1 ᣣ଻ߚߖࠆߎߣߪ⃻⁁ߢߪ㔍ߒ޿‫ޕ‬ ࠮ࡦࠨ࠺࡯࠲ߩ㊀޿ಣℂࠍ┵ᧃౝߢⴕࠊߕߦࠨ࡯ࡃ਄ߦォㅍߒߡⴕ߁‫ࠨࡦ࠮ߦ߼ߚߩߘߚ߹ޔ‬࠺࡯࠲ࠍシ⽶⩄ߦ࿶❗ߔࠆߎߣߢᶖ⾌㔚ജߩૐᷫߔࠆᚻᴺߦߟ޿ߡ⃻࿷ᬌ⸛ࠍㅴ߼ߡ޿ࠆ‫ޕ‬ ࿑⴫

࿑ 1. ࠙ࠚࠕ࡜ࡉ࡞ⴊ࿶࠮ࡦࠨࠍ↪޿ߚ㜞㦂⠪ߩ෼❗ᦼⴊ࿶ߩ⍴ᦼᄌേ᷹ቯ⚿ᨐߣⴕേ࡜ࡌ࡞

−−

第12回人間情報学会講演会ポスターセッション（2012.9.11）

Actual Class%

Predicted Class
Stay

Walk

Jog

Skip

StUp

StDown

Stay

98.1

1.4

0.0

0.0

0.3

0.3

Walk

2.5

77.2

0.3

0.3

12.8

6.9

Jog

0.3

1.7

89.7

6.4

0.0

1.9

Skip

0.0

0.3

3.9

93.3

1.1

1.4

StUp

0.0

11.7

0.0

0.3

73.1

15.0

StDown

0.0

6.9

2.8

1.9

15.6

72.8

⴫ 1. HASC2010corpus ࠍ↪޿ߚၮᧄ 6 ⴕേߩ⹺⼂♖ᐲ (Overall: 84.0%)

࿑ 2. ⴕേ⹺⼂ࠕࡊ࡝૶↪ਛߩ↹㕙଀−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

᫪ᨋ߇ੱ㑆ߩቇ⠌⢻ജߦ෸߷ߔᓇ㗀ߦଥࠆታ㛎⊛⎇ⓥ⺪⸰᧲੩ℂ⑼ᄢቇ ᷡ᳓㆐຦
⺪⸰᧲੩ℂ⑼ᄢቇ ᄹ⦟᧻▸ⷐᣦ ⑳ߚߜߪ᫪ᨋ߇ੱߩஜᐽ߿⢻ജߦ෸߷ߔᓇ㗀ߦ㑐ߔࠆ⎇ⓥࠍⴕߞߡ߈߹ߒߚ‫ᧄޕ‬ᢥߢߪ‫ޔ‬᫪ᨋ߆ࠄ⊒↢ߔࠆࠟࠬߢ޽ࠆࡈࠖ࠻ࡦ࠴࠶࠼߇ੱ㑆ߩ↢ℂߦ෸߷ߔᓇ㗀ߦߟ޿ߡᬌ⸛ߒ߹ߒߚ‫ߚ߹ޕ‬᫪ᨋߦሽ࿷ߔࠆ⥄ὼߩ㖸‫ޔ‬㘑ߩ㖸‫ߩ⪲ޔ‬㖸‫ߩ⯻ޔ‬ჿߥߤߩᵄ㐳․ᕈߦߪDᵄ߿Tᵄߣࠃ߫ࠇࠆ๟ᵄ
ᢙᚑಽ߇฽߹ࠇࠆߎߣ߆ࠄ‫ߩࠄࠇߎޔ‬㖸ߩ๟ᵄᢙᚑಽࠍ฽߻㖸ᭉ߇ቇ⠌⢻ജߦਈ߃ࠆᓇ㗀ߦߟ޿ߡᬌ⸛ߒ߹ߒߚ‫ੱ߇࠼࠶࠴ࡦ࠻ࠖࡈޕ‬㑆ߩ⥄ᓞ␹⚻♽ߦਈ߃ࠆᓇ㗀ߪᔃ㔚⸘ࠍ↪޿ߡ‫ޔߚ߹ޔ‬᫪ᨋߩ㖸ᚑಽ߇ੱ㑆ߩቇ⠌⢻ജߦਈ߃ࠆᓇ㗀ߪ⸥ᙘജ࠹ࠬ࠻ߣᔃ㔚⸘ࠍ↪޿ߡ⺞ߴ߹ߒߚ‫⚿ߩߘޕ‬ᨐ‫ޔ‬᫪ᨋߩ㚅ࠅߦࠃࠆ࡝࡜࠶ࠢࠬലᨐ‫ޔ‬ǩᵄ‫ޔ‬Ǒᵄࠍ฽߻㖸ߦࠃࠆ⸥ᙘജჇᒝലᨐ߇⏕⹺ߐࠇ߹ߒߚ‫ߩࠄࠇߎޕ‬ታ㛎⚿ᨐߪᭂ߼ߡࠪࡦࡊ࡞ߥౝኈߢߔ߇‫ޔ‬ᣂߒ޿ቇ⠌ⅣႺߩഃㅧߦነਈߔࠆ߽ߩ
ߣ⠨߃ߡ޿߹ߔ‫ޕ‬એ਄‫ޔ‬᫪ᨋ߇ੱߩஜᐽ߿⢻ജߦ෸߷ߔᓇ㗀ࠍቯ㊂ൻߔࠆ⹜ߺߦ߅޿ߡ‫⧯ޔ‬ᐓߩᣂߒ޿⍮⷗ࠍᓧࠆߎߣ߇ߢ߈ߚߣ⠨߃ߡ޿߹ߔ‫ޕ‬ߪߓ߼ߦ᫪ᨋߦߪੱ㑆ߦ෸߷ߔ᦭⋉ߥലᨐ߇ႎ๔ߐࠇߡ޿߹ߔ‫ޕ‬⠧ൻ㒐ᱛ߿߇ࠎ੍㒐ߥߤߩஜᐽჇㅴ‫ޔ‬߹ߚ቟ࠄ߉߿≹ߒߥߤࠬ࠻࡟ࠬૐᷫߦᓎ┙ߜ߹ߔ‫ߪࠄࠇߎޕ‬᫪ᨋߩ᥊ⷰ‫⥄ޔ‬ὼ㖸‫ߪ޿ࠆ޽ޔ‬᫪ᨋࠟࠬ㧔ࡈࠖ࠻ࡦ࠴࠶࠼ߣࠃ߱㧕ߩⶄวലᨐߢߔ‫ߪߜߚ⑳ޕ‬᫪ᨋⅣႺ߇ੱ㑆ߦਈ߃ࠆᓇ㗀ࠍቯ㊂ൻߒߡ޿߹ߔ‫੹ޕ‬࿁‫ੱ߇࠼࠶࠴ࡦ࠻ࠖࡈޔ‬㑆ߩ⥄ᓞ␹⚻♽ߦਈ߃ࠆᓇ㗀ࠍᔃ㔚⸘ࠍ↪޿ߡ‫ޔߚ߹ޔ‬᫪ᨋߩ㖸ᚑಽ߇ੱ㑆ߩቇ⠌⢻ജߦਈ߃ࠆᓇ㗀ࠍ૞ᬺ⽶⩄⹜㛎ߦࠃࠅ⺞ߴ߹ߒߚ‫ߩࠄࠇߎޕ‬ታ㛎⚿ᨐߪᣂߒ޿ቇ⠌ⅣႺࠍഃㅧߔࠆߚ߼ߦነਈߔࠆߣ⠨߃ߡ޿߹ߔ‫ޕ‬ᣇᴺ ੱ㑆߇ࡈࠖ࠻ࡦ࠴࠶࠼㧔ൻቇᚑಽ㧕ߦࠃࠅฃߌࠆ⥄ᓞ␹⚻♽ߩᓇ㗀ߦߟ޿ߡߪ‫࠶࠴ࡦ࠻ࠖࡈޔ‬࠼ߩਥᚑಽߣߐࠇࠆǩࡇࡀࡦߣG࡝ࡕࡀࡦࠍ↪޿ߡታ㛎ࠍⴕߞߚ‫ߩߎޔߦ․ޕ‬⒳㘃ߩൻቇᚑಽ
ߪ㊎⪲᮸߆ࠄ⊒↢ߒߡ޿ࠆߎߣ߇᣿ࠄ߆ߦߐࠇߡ߅ࠅ‫੹ޔ‬࿁‫ޔ‬ታ㛎ߩኻ⽎ߣߒߚ᫪ᨋ߽㊎⪲᮸߇ਥߚࠆ᮸⒳ߢ޽ߞߚ‫ޕ‬ታ㛎ᚻ㗅ߪੱ㑆߇ࡈࠖ࠻ࡦ࠴࠶࠼ߩਥᚑಽߣߐࠇࠆD-ࡇࡀࡦߣ d-࡝ࡕࡀࡦࠍๆᒁߒߚᤨߩ⥄ᓞ␹⚻♽ߩᵴേࠍᔃ㔚⸘ߦࠃࠅ᷹ቯߒߚ‫ޕ‬㖸߇ੱ㑆ߩቇ⠌⢻ജߦ෸߷ߔᓇ㗀ߦߟ޿ߡታ㛎ࠍⴕߞߚ‫ޕ‬᫪ᨋߩ㖸ߪǩᵄ߿ǰᵄᚑಽࠍᄙߊ฽
ࠎߢ޿ࠆ‫ߩࠄࠇߎޕ‬ᚑಽࠍ฽߻㖸ᭉࠍ⡞ߊߎߣߦࠃࠆቇ⠌⢻ജߩᓇ㗀ࠍ⺞ߴߚ‫ޕ‬ታ㛎ߪᢙሼ೉ࠍ
ᥧ⸥ߔࠆ⺖㗴ߢ޽ࠅ‫✢⋥ޔ‬㈩೉ߩᢙሼߣࡑ࠻࡝ࠢࠬ㈩೉ߩᢙሼߩᥧ⸥ࠍ↪޿ߚ‫ޕ‬Dᵄ߿Tᵄࠍ฽߻㖸ᭉࠍ⡬ߊߎߣߦࠃࠆੂᢙ೉ߩᥧ⸥ജߦ෸߷ߔᓇ㗀ࠍ⺞ߴࠆߎߣࠍ⋡⊛ߣߒߚ‫ޕ‬Dᵄ߿Tᵄࠍ฽߻㖸ᭉࠍㆬᛯߒߚℂ↱ߣߒߡߪ‫ޔ‬᫪ᨋߦ߅ߌࠆ㘑ߩ㖸‫ߋࠃߘߩ⪲ޔ‬㖸ߦߪߎࠇࠄߩᵄ㐳ᚑಽ߇ᄙߊ
฽߹ࠇߡ޿ࠆߣߐࠇߡ޿ࠆ߆ࠄߢ޽ࠆ‫⸥ޕ‬ᙘኻ⽎ߪ⒳㘃ߣߒߡ‫ࠬࠢ࡝࠻ࡑޔ‬㈩೉ߣ⋥✢㈩೉ߣ޿

−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

߁ࠃ߁ߦ㈩೉ߩ⁁ᘒߩ⋧㆑ߦࠃࠆᓇ㗀߽⺞ߴߚ‫ޕ‬ᢙ೉ߪ‫ߩࠄ߆ޔ‬ᢙሼࠍ࡜ࡦ࠳ࡓߦ㈩⟎ߒ‫⋥ޔ‬✢㈩೉ߪ୘߹ߢᥧ⸥ߒߚᢙሼࠍ⸥౉ߒߡ߽ࠄߞߚ‫ޕ‬ᔃ㔚⸘ߪᏀ⢷ㇱਛᄩㇱߦ⾍ࠅઃߌ‫⥄ޔ‬ᓞ␹⚻♽ߩᵴേ㧔೽੤ᗵ␹⚻‫ޔ‬੤ᗵ␹⚻ߩᵴേᐲ㧕߅ࠃ߮ᔃᜉࠍ᷹ቯߒ‫ߩߘޔ‬ᵄᒻࠍ FFT(㜞ㅦࡈ࡯࡝ࠛᄌ឵)ࠍⴕ߁ߎߣߦࠃࠅ‫ߩߘޔ‬෻ᔕ․ᕈࠍ⺞ߴߚ‫ޕ‬⚿ᨐ߅ࠃ߮⠨ኤࡈࠖ࠻ࡦ࠴࠶࠼ᚑಽ߇ੱߩ⥄ᓞ␹⚻ߦ෸߷ߔᓇ㗀ࠍ⺞ߴߚታ㛎ߩ⚿ᨐࠍ࿑ߦ␜ߒߚ‫ޔࠅࠃࠇߎޕ‬ǩࡇࡀࡦࠍ༦޿ߢ޿ࠆᤨߪ૗߽༦޿ߢ޿ߥ޿ᤨࠃࠅ೽੤ᗵ␹⚻ߩᵴേᜰᮡ߇ᄢ߈޿ߎߣ߇ࠊ߆
ࠆ‫ޔߚ߹ޕ‬G࡝ࡕࡀࡦߪ੤ᗵ␹⚻ߩ௛߈ࠍᛥ߃ࠆ௑ะ߇⷗ࠄࠇࠆ‫ੱ߇࠼࠶࠴ࡦ࠻ࠖࡈޕ‬㑆ߦ቟ࠄ߉߿≹ߒࠍਈ߃ࠆߣߐࠇߡ޿ࠆℂ↱ࠍ␜ߔߎߣ߇ߢ߈ߚ‫ޕ‬࿑㧝㧚ࡈࠖ࠻ࡦ࠴࠶࠼ᚑಽߩǩࡇࡀࡦ‫ߩࡦࡀࡕ࡝ޔ‬㚅ࠅ߇⥄ᓞ␹⚻ߦ෸߷ߔᓇ㗀ࡑ࠻࡝ࠢࠬ㈩೉ߐࠇߚੂᢙߩᥧ⸥ജ࠹ࠬ࠻߅޿ߡ୘ੱ㑆ߢߩࡃ࡜ߟ߈߇ᄢ߈ߊ‫⸥ޔ‬ᙘജߩะ਄
ߪ⷗ࠄࠇߥ߆ߞߚ߇‫✢⋥ޔ‬㈩೉ߩੂᢙࠍ↪޿ߚ࠹ࠬ࠻ߢߪ‫ޔ‬㖸ᭉࠍ⡬޿ߡ޿ߥ޿ᤨࠃࠅ߽㖸ᭉࠍ⡬޿ߡ޿ࠆᤨߩ߶߁߇ᚑ❣߇⦟ߊ‫ޔ‬Dᵄߢߪ቟ቯߒߚᅢᚑ❣ࠍ‫ޔ‬Tᵄߢߪࡃ࡜ߟ߈߇޽ߞߚ߽ߩߩ
㜞޿ᥧ⸥ജࠍ␜ߒߚ‫࠲࡯࠺ߒ߆ߒޕ‬ᢙ߇ዋߥ޿ߣ޿߁໧㗴߇޽ࠅ੹ᓟߐࠄߥࠆ࠺࡯࠲ߩ㓸Ⓧߣ⸃
ᨆ߇ᔅⷐߢ޽ࠆ‫ޕ‬
FFT ߦ⥄ᓞ␹⚻♽ߩᵴേᜰᮡࠍ߆ߌߡߺߚ߇‫ߚߞ޿ߣࠇߎߦ․ޔ‬ᴺೣᕈࠍ⊒⷗ߔࠆߦߪ⥋ࠄߥ߆ߞߚ‫⥄ޕ‬ᓞ␹⚻♽ߩᵴേᜰᮡ⥄૕ߦ୘ੱᏅ߇޽ࠆߎߣ߇ᄢ߈ߥ໧㗴ߢ޽ࠆ‫ޕ‬
⴫.1 ࡑ࠻࡝ࠢࠬ㈩೉໧㗴ߩᚑ❣
ⵍ㛎⠪

ᱜᒰᢙ

䈭䈚
࿁╵ᤨ㑆㩿㫊㪀

ᱜᒰᢙ

T࿁╵ᤨ㑆㩿㫊㪀

ᱜᒰᢙD࿁╵ᤨ㑆㩿㫊㪀

㪘

㪉㪌

㪈㪊㪋㪅㪐

㪈㪐

㪈㪌㪇

㪉㪌

㪋㪋㪅㪏

㪙

㪉㪌

㪋㪋㪅㪏

㪉㪌

㪈㪈㪊㪅㪏

㪉㪉

㪈㪋㪈㪅㪈

㪚

㪈㪐

㪈㪊㪋㪅㪍

㪈㪌

㪈㪉㪉㪅㪈

㪈㪌

㪈㪇㪐㪅㪈

−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

࿑㧞 ⋥✢㈩೉ߩᚑ❣
⚿⺰ ࠊߚߒߚߜߪりㄭߥⅣႺ߆ࠄ੖ᗵࠍㅢߒߡ᭽‫ߥޘ‬ᖱႎࠍᓧߡ޿ࠆ‫ߩߎޔ‬ᖱႎߪᤨ㑆⊛‫ޔ‬ⓨ㑆⊛ߦᭂ߼ߡᄙ⒳ᄙ᭽ߢ޽ࠆ‫੹ޕ‬࿁‫⎇ᧄޔ‬ⓥߢߪ᫪ᨋߩⅣႺࠍขࠅ਄ߍ‫ޔ‬᫪ᨋߩᜬߟ․೎ߥ੖ᗵೝỗ߇ੱߦਈ߃ࠆᓇ㗀ࠍቯ㊂ൻߔࠆߎߣࠍ⹜ߺߚ‫⚿ߩߘޕ‬ᨐ‫ޔ‬᫪ᨋߩࠟࠬᚑಽߢ޽ࠅ‫࠶࠴ࡦ࠻ࠖࡈޔ‬࠼ߣࠃ߫ࠇߡ޿ࠆǩࡇࡀࡦ߿࡝ࡕࡀࡦ߇ੱߩ⥄ᓞ␹⚻ߩᵴേߦᓇ㗀ࠍਈ߃ࠆߎߣ‫ߦࠄߐޔ‬᫪ᨋߩ㖸ᚑಽߢ޽ࠆǩᵄ߿Ǒᵄ߇⸥ᙘജߦ෸߷ߔᓇ㗀ࠍቯ㊂ൻߢ߈ߚ‫⚿ߩࠄࠇߎޕ‬ᨐߪ‫ޔ‬ᭂ߼ߡ㒢ࠄࠇߚ᧦ઙ‫ޔ‬ⅣႺߢᓧࠄࠇߚ߽ߩߢ޽ࠆߩߢ‫ߦ㉼⸃ޔ‬㒢⇇ߪ޽ࠆ߇‫ੱޔ‬㑆ᖱႎߩ₪ᓧߣ޿߁ᗧ๧ߢߪ
ᣂߒ޿⍮⷗ࠍᓧࠆߎߣ߇ߢ߈ߚߣ⠨߃ߡ޿ࠆ‫ޕ‬ߒ߆ߒߥ߇ࠄ‫ޔ‬ᔃᜉᢙ‫⥄ޔ‬ᓞ␹⚻♽ߩᵴേࡄ࠲࡯ࡦ‫ޔ‬૕᷷‫ޔ‬ᔃℂ⁁ᘒ‫ޔ‬ஜᐽ⁁ᘒߥߤߩੱ㑆ᖱႎߪ‫ޔ‬୘ੱᏅ߿ᤨ㑆Ꮕ߇ሽ࿷ߔࠆߚ߼‫ޔ‬ቯ㊂ൻߦߪචಽ㈩ᘦߔࠆᔅⷐ߇޽ࠆ‫੹ޕ‬ᓟ‫ߩ࠲࡯࠺ޔ‬ା㗬ᕈߣ⸃ᨆ⚿ᨐߩ቟ቯᕈࠍ⏕଻ߔࠆߚ߼ߦ࠺࡯࠲ᢙࠍჇᄢߐߖࠆߎߣ߇ᔅⷐߢ޽ࠆߣ⠨߃ߡ޿ࠆ‫ޕ‬−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

ࡋ࡞ࠬࠤࠕࠨ࡯ࡆࠬߩߚ߼ߩ
ᤨ♽೉࠺࡯࠲ࡌ࡯ࠬࠪࠬ࠹ࡓߣ↢૕ᖱႎࡆࡘ࡯ࠕߩታⵝ
᫪ᚭ ⾆ 1, ࡠࡍ࠭ ࡛ࠡ࡯ࡓ 2㧘㋈ᧁ ⺈ 1㧘᧘᢯ᾔ 1㧘᫪Ꮉ ඳਯ 1
1᧲੩ᄢቇవ┵⑼ቇᛛⴚ⎇ⓥ࠮ࡦ࠲࡯ ᧲੩ㇺ⋡㤥඙㚤႐ 4-6-1
2᧲੩ᄢቇᄢቇ㒮ᣂ㗔ၞഃᚑ⑼ቇ⎇ⓥ⑼ ජ⪲⋵ᨰᏒᨰߩ⪲ 5-1-5
email:
morito@mlab.t.u-tokyo.ac.jp
Keywords: ࡋ࡞ࠬࠤࠕࠨ࡯ࡆࠬ ᤨ♽೉࠺࡯࠲ࡌ࡯ࠬ

ⷐᣦ ࠮ࡦࠨߦ↪޿ࠄࠇࠆඨዉ૕ᛛⴚߩㅴዷ߿ࡃ࠶࠹࡝ኈ㊂ߩჇടߦ઻޿‫ߩࠬࠗࡃ࠺ࠨࡦ࠮ޔ‬ዊဳൻ߿ૐࠦࠬ࠻ൻ߇ㅴߺ‫ⴊޔ‬࿶࡮⣂ᜉ࡮ᔃ㔚ߣ޿ߞߚ↢૕ᖱႎࠍ⥄↱ⴕേਅߦ߅޿ߡ㐳ᤨ㑆‫ޔ‬ㅪ⛯⊛ߦ᷹ቯߔࠆߎߣ߇น⢻ߦߥߞߚ‫↢ޕ‬૕ᖱႎࠍ㐳ᤨ㑆‫ޔ‬ㅪ⛯⊛ߦ᷹ቯߒ‫⫾ޔ‬Ⓧ࡮ಽᨆߔࠆߎߣߢ‫ޔ‬ᘟᕈ∔ᖚᖚ⠪ߩ⁁ᘒࠍᛠីߒߚࠅ‫ޔ‬ஜᏱ⠪ߩஜᐽ⁁ᘒࠍ▤ℂߒߚࠅߔࠆࡋ࡞ࠬࠤࠕࠨ࡯ࡆࠬ߇ታ
⃻ߐࠇࠆ‫ޕ‬ ᗐቯߔࠆࡋ࡞ࠬࠤࠕࠨ࡯ࡆࠬߢߪ‫ޔ‬ㅪ⛯⊛ߦ᷹ቯߐࠇࠆ↢૕ᖱႎ߆ࠄߥࠆᤨ♽೉࠺࡯࠲ࠍ⫾Ⓧ
ߔࠆᤨ♽೉࠺࡯࠲ࡌ࡯ࠬࠪࠬ࠹ࡓߣߘߩ↢૕ᖱႎࠍ⴫␜ߔࠆ↢૕ᖱႎࡆࡘ࡯ࠕ߇ᔅⷐߣߥࠆ‫ޕ‬ ࡋ࡞ࠬࠤࠕࠨ࡯ࡆࠬߩߚ߼ߩᤨ♽೉࠺࡯࠲ࡌ࡯ࠬࠪࠬ࠹ࡓߣ↢૕ᖱႎࡆࡘ࡯ࠕߦߪ᭽‫ߥޘ‬ᛛⴚ⺖㗴߇ሽ࿷ߔࠆ߽ߩߩ‫ޔ‬ዋߥߊߣ߽ᰴߩ 2 ߟߩⷐ᳞߇⠨߃ࠄࠇࠆ‫ޕ‬z ࠮ࡦࠨ߆ࠄขᓧߒߚ↢૕ᖱႎࠍⴕേጁᱧߣ㑐ㅪߠߌߡ⫾Ⓧߒ㧘ߘࠇࠄࠍࠕࡊ࡝ࠤ࡯࡚ࠪࡦ߇
೑↪ߢ߈ࠆᯏ⢻z ↢૕ᖱႎߣⴕേጁᱧߩࠝ࡯ࡃ࡟ࠗ⴫␜㧘↢૕ᖱႎࠣ࡜ࡈߩ᜛ᄢ❗ዊ㧘⛔⸘㊂ߩ⴫␜ߥߤක≮ᓥ੐⠪ࠍᡰេߔࠆᯏ⢻ ߎࠇࠄߩⷐ᳞ߦၮߠ߈‫♽ᤨޔ‬೉࠺࡯࠲ࡌ࡯ࠬࠪࠬ࠹ࡓߣ↢૕ᖱႎࡆࡘ࡯ࠕࠍታⵝߒߚ‫ޕ‬ ߹ߕᤨ♽೉࠺࡯࠲ࡌ࡯ࠬࠪࠬ࠹ࡓߩ᭴ᚑࠍ␜ߔ㧔࿑ 1㧕‫♽ᤨޕ‬೉࠺࡯࠲ࡌ࡯ࠬࠪࠬ࠹ࡓߪ໧޿ว
ࠊߖࡑࡀ࡯ࠫࡖ‫ࠄ߆ࠬ࡯ࡌ࠲࡯࠺࠲ࡔޔࠬ࡯ࡌ࠲࡯࠺࠻ࡦࡌࠗޔࠬ࡯ࡌ࠲࡯࠺ࠨࡦ࠮ޔ‬᭴ᚑߐࠇࠆ‫ޕ‬໧޿วࠊߖࡑࡀ࡯ࠫࡖߪ‫ޔ‬ฦ⒳࠮ࡦࠨ߿ࠕࡊ࡝ࠤ࡯࡚ࠪࡦ߆ࠄߩⷐ᳞ࠍฦ࠺࡯࠲ࡌ࡯ࠬߦഀ
ࠅᝄࠆᯏ⢻ߣ Web ࠗࡦ࠲ࡈࠚ࡯ࠬࠍ஻߃ࠆ‫ޕ‬Web ࠗࡦ࠲ࡈࠚ࡯ࠬߪ‫ࡦ࡚ࠪ࡯ࠤ࡝ࡊࠕޔ‬ኻᔕߩ᳢↪ᕈࠍ㊀ⷞߒ࡝ࠢࠛࠬ࠻ߣߒߡ HTTP ߩ GET/POST‫ ߦࠬࡦࡐࠬ࡟ޔ‬XML/JSON ࠍ↪޿ߚࠗࡦ࠲ࡈ
ࠚ࡯ࠬߣߒߚ‫↢ߪߦࠬ࡯ࡌ࠲࡯࠺ࠨࡦ࠮ޕ‬૕ᖱႎߩᤨ♽೉࠺࡯࠲߇଻ሽߐࠇࠆ‫ ߦࠄߐޕ‬Tiva ࠺࡯࠲ࡌ࡯ࠬࡑࡀ࡯ࠫࡖࠍ↪޿ߡ‫♽ᤨޔ‬೉࠺࡯࠲ߩ࿶❗ߣ໧޿วࠊߖಣℂߩ㜞ㅦൻࠍหᤨᦨㆡൻߔࠆ‫ޕ‬ࠗࡌࡦ࠻࠺࡯࠲ࡌ࡯ࠬߦߪⴕേጁᱧߥߤߩᤨ♽೉ߢߪߥ޿࠮ࡦࠨ࠺࡯࠲ࠍ଻ሽߐࠇࠆ‫࡯࠺࠲ࡔޕ‬࠲ࡌ࡯ࠬߦߪ࡙࡯ࠩߩ૶↪ߒߡ޿ࠆ࠮ࡦࠨߩᖱႎ߿‫ޔ‬᷹ቯᤨ㑆ߥߤ߇଻ሽߐࠇࠆ‫ޕ‬ ᰴߦ↢૕ᖱႎࡆࡘ࡯ࠕߩ᭎ⷐࠍ␜ߔ㧔࿑ 2㧕‫↢ޕ‬૕ᖱႎࡆࡘ࡯ࠕߪ‫ޔ‬කᏧ߇⎇ⓥ࡮⸻≮ߦ↪޿ࠆߎߣࠍᗐቯߒߚࡊࡠ࠻࠲ࠗࡊߣߒߡታⵝߒߚ‫ߩ࠲࡯࠺ޕ‬㑛ⷩߪ․ቯߩࡊ࡜࠶࠻ࡈࠜ࡯ࡓଐሽߒߥ
޿ߎߣ߇ᦸ߹ߒ޿ߎߣ߆ࠄ‫ޔ‬Web ࡉ࡜࠙ࠩࠍ↪޿ߚ⴫␜ߣߒߚ‫ౕޕ‬૕⊛ߦߪ‫⸘⛔ޔ‬㊂ߩ⴫␜‫ⴕޔ‬േጁᱧߩࠝ࡯ࡃ࡟ࠗ⴫␜‫ޔ‬ㆬᛯࠛ࡝ࠕߩ᜛ᄢ❗ዊ‫ޔ‬ᬌᩏ࠺࡯࠲ߩ౉ജ࡮࡝ࡦࠢߩᯏ⢻ࠍᜬߟ‫ޕ‬ ೨ㅀߩᤨ♽೉࠺࡯࠲ࡌ࡯ࠬߣ↢૕ᖱႎࡆࡘ࡯ࠕࠍ↪޿ߡ‫᧲ޔ‬ᄢ∛㒮ߦ߅޿ߡታ⸽ታ㛎ࠍⴕߞߚ‫ޕ‬
࿑ 3 ߦታ⸽ታ㛎ߦදജ޿ߚߛ޿ߚᖚ⠪ߩⴊ࿶߅ࠃ߮ Double Product㧔ⴊ࿶ߦᔃᜉᢙࠍਸ਼ߓߚ߽ߩߢ
޽ࠅ‫ޔ‬ᔃ⽶⩄ߩ⋡቟ߣߥࠆ㧕ߩࠣ࡜ࡈࠍ␜ߔ‫ᧄޕ‬᷹ቯߪ‫ޔ‬77 ᱦߩ↵ᕈߦ⸻ኤ೨ᓟߩ⚂ 2 ᤨ㑆‫ޔ‬ᢔᱠ‫ޔ‬ભᙑߥߤߩ⥄↱ⴕേࠍߒ‫ߩߘޔ‬㓙ߩⴊ࿶‫ޔ‬⣂ᜉ‫ⴕޔ‬േጁᱧࠍ᷹ቯߒߚ‫ⴕޔ߅ߥޕ‬േጁᱧߪ‫ޔ‬ᖚ⠪ᧄੱ߇ᤨೞߣⴕേࠍ⸥㍳ߒߚ߽ߩߢ޽ࠆ‫⚿ߩߘޕ‬ᨐ‫⴩⊕ޔ‬ᕈ㜞ⴊ࿶ߣᕁࠊࠇࠆ⸻ኤᤨߩⴊ࿶
਄᣹‫߮ࠃ߅ޔ‬㓏Ბ᣹㒠ᤨ࡮ભᙑᤨߩⴊ࿶‫ޔ‬Double Product ߩᕆỗߥᄌൻ߇᷹ⷰߢ߈ߚ‫ޕ‬੹ᓟߩዷᦸ ⃻࿷‫᧲ޔ‬ᄢ∛㒮ߢߩታ⸽ታ㛎ࠍ࿾㆏ߦ⛮⛯ߒ‫♽ᤨޔ‬೉࠺࡯࠲ࡌ࡯ࠬࠪࠬ࠹ࡓ߿↢૕ᖱႎࡆࡘ࡯

−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

ࠕߩⷐઙߩ♖✺ൻࠍㅴ߼ߡ޿ࠆ‫੹ޕ‬ᓟߪ‫ޔ‬ታ⸽ታ㛎ࠍ⛮⛯ߔࠆߣߣ߽ߦ‫ޔ‬ක≮ᓥ੐⠪ߦ㒢ࠄߕᖚ⠪ᧄੱ߿ᖚ⠪ߩኅᣖߦߣߞߡߩ೑ᵴ↪ߩน⢻ᕈࠍᬌ⸽ߔࠆ‫ߩࠬ࡯ࡌ࠲࡯࠺ޔߚ߹ޕ‬ၮ⋚ᛛⴚߪ‫ޔ‬࠮ࡦࠨ߿ࠕࡊ࡝ࠤ࡯࡚ࠪࡦߩ⒳㘃ࠍ໧ࠊߥ޿᭴ᚑߣߥߞߡ߅ࠅ‫ޔ‬ක≮એᄖߩᔕ↪น⢻ᕈࠍᬌ⸛ߒ
ᤨ♽೉࠺࡯࠲ࡌ࡯ࠬࠪࠬ࠹ࡓߩᮮᣇะዷ㐿ࠍ࿑ࠆ‫ޕ‬⻢ㄉᧄ⎇ⓥߪ⑼ቇᛛⴚᝄ⥝ᯏ᭴㧔JST㧕ߩᚢ⇛⊛ഃㅧ⎇ⓥផㅴ੐ᬺ㧔CREST㧕ߩᡰេߦࠃߞߡⴕࠊࠇߚ‫ޕ‬

࿑ 1 ↢૕ᖱႎߩ⫾Ⓧ࡮ಽᨆߦะߌߚᤨ♽೉࠺࡯࠲ࡌ࡯ࠬࠪࠬ࠹ࡓ

−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

࿑ 2 Web ࡉ࡜࠙ࠩࠍ↪޿ߚ↢૕ᖱႎࡆࡘ࡯ࠕߩࡊࡠ࠻࠲ࠗࡊ

77ᱦ ↵ᕈ ⥄↱ⴕേਅ䈪⚂2ᤨ㑆䈱᷹ቯἯ⦡✢䋺෼❗ᦼⴊ࿶㤥⦡✢䋺Double Product䋨ⴊ࿶䈫⣂ᜉᢙ䉕ਸ਼䈛䈢䉅䈱䋩⊕⴩ᕈ㜞ⴊ࿶䈱น⢻ᕈ䈅䉍

ᔃᜉ䊶Doble Product䈱ᕆỗ䈭ᄌൻ

࿑ 3 ᧲ᄢ∛㒮ߢߩታ⸽ታ㛎⚿ᨐ−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

ᣣᧄ⺆േ⹖↥↢⺖㗴ߦ߅ߌࠆ⺋╵⊒↢ߣ⣖ᵴേ

An EEG study of error-related fundamental brain activities
of oral Japanese-verb processing੹੗⛗⟤ሶ 1,᩿␽㓷 2㧘Ꮉ෶ᢅ↵ 1

12

␹ᚭᄢቇᄢቇ㒮 ଻ஜቇ⎇ⓥ⑼ ౓ᐶ⋵␹ᚭᏒ㗇⏴඙෹߇ਐ 7-10-2⁛㧕ᖱႎㅢା⎇ⓥᯏ᭴ ⣖ᖱႎㅢାⲢว⎇ⓥ࠮ࡦ࠲࡯౓ᐶ⋵␹ᚭᏒ⷏඙ጤጟ↸ጤጟ588-2
email:e-imai@stu.kobe-u.ac.jp
Keywords: ⣖ᵄ㧘 ǩ2㧘㧘 ⊒⹤㧘 㖿ᓞ㧘 ᷓㇱ⣖ᯏ⢻ⷐᣦ㖸ჿ⸒⺆ࠍ↪޿ߚࠦࡒࡘ࠾ࠤ࡯࡚ࠪࡦߪ‫⺆ޔ‬ᒵ߿᭴ᢥߣ޿ߞߚ⸒⺆ቇ⊛ߥ⊒⹤ౝኈߣ‫ޔ‬ᛥ឴߿࡝࠭
ࡓߣ޿ߞߚ㕖⸒⺆ቇ⊛ߥ㖸ჿߣߢᚑࠅ┙ߟ‫ઁޕ‬⠪ߣߩ౒ᗵࠍᚑ┙ߐߖࠆߚ߼ߦߪ‫ޔ‬ᓟ⠪ߩ㖿ᓞ㧔ࡊࡠ࠰࠺ࠖ㧕߇ਇนᰳߢ޽ࠆ‫⇣ߩ↢↥ࠖ࠺࠰ࡠࡊޕ‬Ᏹ㧔⊒⹤ߦ⹣߹ߞߚࠅ‫ޔ‬ᛥ឴߇ᐔ᧼ߦߥߞߚࠅ‫⹤⊒ޔ‬ㅦᐲ߇ㅦߊ࡮ㆃߊߥߞߚࠅ㧕ߪ‫ޔ‬⣖තਛ╬ߦࠃࠆ⣖ᯏ⢻㓚ኂߢ↢ߓࠆ߇‫✕ޔ‬ᒛ߿ὶࠅࠍᗵߓࠆ⁁ᴫਅߢ߽ߒ߫ߒ߫⊒↢ߔࠆ‫ޕ‬ᚒ‫↢↥ࠖ࠺࠰ࡠࡊޔߪޘ‬㓚ኂߩ␹⚻ࡔࠞ࠾࠭ࡓߩ⸃᣿ࠍ⋡⊛ߣߒ‫ޔ‬ᔃℂ⊛⽶ᜂࠍਈ߃ࠆ⊒⹤࠲ࠬࠢࠍ⺖ߔታ㛎ࠍ‫ⵍޔ‬㛎⠪ 2 ฬߦኻߒߡ⣖ᵄ⸘᷹ࠍⴕ޿ߥ߇ࠄታᣉߒߚ‫ޕ‬ೝỗߦߪ㜞ⷫኒᐲߢ 4 ࡕ࡯࡜㧔㖸㧕ߩฬ⹖න⺆ࠍ૶↪ߒ‫ޔ‬ฬ⹖๒␜ᓟߦ 1kHz ࡆ࡯ࡊ㖸㧔๮઎ೝỗ㧕߇㡆ࠆߣ‫ޔ‬ฬ⹖ߦᴪ߁േ⹖ࠍญ㗡⴫಴ߔࠆߣ޿߁‫ޟ‬േ⹖↥↢࠲ࠬࠢ‫ޔࠍޠ‬ೝỗᔕ╵㑆㓒߇ 2.5 ⑽ߣ⍴޿᧦ઙ㧔Rapid
task㧕ߣ 5 ⑽ߣ㐳޿᧦ઙ㧔Slow task㧕ߩ 2 ᧦ઙߢⴕߞߚ‫ޕ‬ᷓㇱ⣖ᵴേߩᜰᮡߣߒߡ‫ޔ‬࿖㓙 10-20 ᴺߢቯ߼ࠄࠇߚ 21 ࠴ࡖࡦࡀ࡞ࠠࡖ࠶ࡊߩᓟ㗡ㇱ㔚ᭂ㧔O1‫ޔ‬O2㧕ߦ⺃⊒ߐࠇࠆǩ2㧔10-13Hz㧕ߩᒝᐲࠍ⸘᷹
ߒߚ‫ⵍޕ‬㛎⠪߇ฃߌߚᔃℂ⊛⽶ᜂߩᜰᮡߣߒߡ⊹⤏ࠦࡦ࠳ࠢ࠲ࡦࠬ᳓Ḱ㧔SCL㧕ࠍ⸘᷹ߒߚ‫ޕ‬ታ㛎ਛߩ㖸ჿࠍ㍳㖸ߒ‫ޔ‬⡬ⷡශ⽎ߦࠃࠅ࠲ࠬࠢᲤߩⵍ㛎⠪ߩ෻ᔕߩ⹦⚦ࠍಽᨆߒߚ‫ޕ‬ታ㛎ߩ⚿ᨐ‫ⵍޔ‬㛎⠪ߦࠃߞߡ㗫ᐲ߅ࠃ߮࠲ࠗࡊߩᏅߪ޽ࠆ߽ߩߩ‫ޔ‬ᱜߒߊേ⹖ࠍ↥↢ߢ߈ߥ޿‫⺋ޟ‬෻ᔕ㧔ࠛ࡜࡯㧕‫ߚࠇࠄ߼⹺߇↢⊒ߩޠ‬㧔࿑ 1‫ޔ‬2㧕‫ⵍޔߪࡊࠗ࠲ߩ࡯࡜ࠛޕ‬㛎⠪ 1 ߢߪή෻ᔕ㧔ోߊᔕ╵ߒߥ޿㧕߇߶ߣࠎߤࠍභ߼ࠆߩߦኻߒߡ‫ⵍޔ‬㛎⠪ 2 ߢߪή෻ᔕߪ߶߷ߥߊ‫᧦ޔ‬ઙߦࠃߞߡࠛ࡜࡯࠲ࠗ
ࡊߦᏅ߇↢ߓߚ‫ޕ‬Slow task ߢ⊒↢ߒߚᐔ᧼ൻ㧔ᐔမߥᛥ឴㧕ߣዊჿ㧔ჿ㊂ߩૐਅ㧕ߪ‫ޔ‬Rapid task ߢߪዋߥߊ‫ޔ‬Ⴧᒝ㧔ჿ㊂ߩჇᄢ㧕‫ޔ‬ടㅦ㧔⊒⹤ㅦᐲߩടㅦ㧕‫ޔ‬ઍఘ㧔
‫߁޿ߣޠ߃ޟ‬ઍఘ⊛෻ᔕߩవⴕ㧕߇ᄙ߆ߞߚ㧔࿑ 2㧕‫ޕ‬SCL ߪ࠲ࠬࠢߏߣߦ਄ਅߦᄌേࠍ⷗ߖߚ߇‫ޔ‬ㅪ⛯ࠛ࡜࡯⊒↢ߦ઻ߞߡᕆ਄᣹ߔࠆ․ᓽ
⊛෻ᔕ߇⷗ࠄࠇߚ㧔࿑ 3㧕
‫ޕ‬ǩ2 ߩᒝᐲߪฬ⹖߅ࠃ߮ࡆ࡯ࡊ㖸๒␜೨ߦਅ߇ࠅ‫ޔ‬ೝỗ⺆ߦ਄߇ࠆߣ޿߁
ࡄ࠲࡯ࡦ߇⷗ࠄࠇߚ‫ޔߪᤨࠆߔ↢⊒߇࡯࡜ࠛޔߒ߆ߒޕ‬ᒝᐲᄌേߦੂࠇ߇↢ߓߚ‫ޕ‬‫ޟ‬ᐔ᧼‫ޠ‬‫ޔ‬‫ޟ‬ዊჿ‫ߩޠ‬ᤨߦߪ๮઎ೝỗᓟߩǩ2 ᒝᐲߩ਄᣹ᐲว޿߇ૐߊ‫ޔ‬‫ޟ‬᜛ᒛ‫ޠ‬‫ޔ‬‫ޟ‬ᒝ⺞‫ޠ‬‫ޔ‬‫ޟ‬ઍఘ‫ޠ‬‫ޔ‬‫ޟ‬ടㅦ‫ޠ‬෻ᔕᤨߪฬ⹖๒
␜೨ߩǩ2 ᒝᐲߩਅ㒠ߩ࠲ࠗࡒࡦࠣߩߕࠇ߿ਅ㒠ᐲว޿ߩਇ⿷߇⹺߼ࠄࠇߚ㧔࿑ 4㧕‫ޔߦࠄߐޕ‬ᓟ㗡ㇱᏀฝ㔚ᭂ㧔O1‫ޔ‬O2㧕ߩᏅಽ㧔O1-O2㧕ߢߪ‫ߦᤨ↢⊒࡯࡜ࠛޔ‬ฝඨ⃿ߩǩ㧞ᒝᐲ߇ᒝߊߥࠆ௑ะ߇⹺߼
ࠄࠇߚ‫ޕ‬
ࡊࡠ࠰࠺ࠖ↥↢㓚ኂ߅ࠃ߮⊒⹤ᵹᥰᕈ߇㒖ኂߐࠇࠆ∝⁁ࠍ๒ߒߚᤨ‫ޔ‬
ǩ2 ᒝᐲߩᄌേߩੂࠇ߇⹺߼ࠄ
ࠇߚᧄታ㛎⚿ᨐࠃࠅ‫⺖ߪߦ⹤⊒ޔ‬㗴೨ߩ⊹⾰ߩᛥ೙㧔ࠕࠗ࠼࡝ࡦࠢ㧕߇ᔅⷐߢ޽ࠆߎߣ߇␜ໂߐࠇ‫ޔ‬ߎߩࠕࠗ࠼࡝ࡦࠢߦߪࡕࡁࠕࡒࡦ␹⚻ᵴേ߇㑐ਈߒߡ޿ࠆߣߎߣ߇ផኤߐࠇߚ‫⎇ᧄޕ‬ⓥߩ৻ㇱߪ‫ޔ‬
JST-CREST ߩᡰេࠍฃߌߡታᣉߒߚ‫ޕ‬−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

࿑ 1㧚࠲ࠬࠢ᧦ઙ೎ߩ⺋෻ᔕ㧔ࠛ࡜࡯㧕₸ೝỗ㧔ฬ⹖๒␜㧕㧙ᔕ╵㧔ࡆ࡯ࡊ㖸㧕㑆㓒߇ 5 ⑽ߩ Slow task㧔S㧕ߣ 2.5 ⑽ߩ Rapid task㧔R㧕ࠍⴕߞߚ‫ޕ‬Rapid task ߢߪേ⹖ࠍᗐ⿠ߔࠆᤨ㑆ߦ૛⵨߇ߥ޿ߚ߼‫ޔ‬ᔃℂ⊛⽶ᜂ߇Ⴧߒߡ‫ࠅࠃޔ‬ᄙ޿ࠛ࡜࡯߇
⊒↢ߔࠆߣᗐቯߒߚ߇‫⚿ޔ‬ᨐߪⵍ㛎⠪ߦࠃߞߡ⇣ߥߞߚ‫ⵍޔߪ₸↢⊒࡯࡜ࠛޕ‬㛎⠪ 1 ߢ S>R‫ⵍޔ‬㛎⠪ 2
ߢ S<R ߢ޽ߞߚ‫ޕ‬ㅪ⛯䉣䊤䊷⊒↢SCL 䋨1/ɏ䋩

EEG10䋭13Hz Bandpass power 䋨ʅV䋩

SCL䈱ᕆ਄᣹

㤥ᄥ✢߇ SCL‫ޕ‬Ἧ⦡ߩ✢ߪǩ2 ࡄࡢ࡯ࠍ␜ߔ㧔⭯޿✢߇Ꮐ‫ޔ‬Ớ޿✢߇ฝߩᓟ㗡ㇱ㧕‫ޕ‬࿑ 2㧚࠲ࠗࡊ೎ߩࠛ࡜࡯₸    ࿑ 3㧚⊹⤏ࠦࡦ࠳ࠢ࠲ࡦࠬ࡟ࡌ࡞ߩᄌേ

࿑ 4㧚ࠛ࡜࡯⊒↢೨ᓟߩǩ2 ࡄࡢ࡯ߩᄌേ

−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

ᥤᾲⅣႺਅߦ߅ߌࠆ㗡ㇱ಄ළߩ㜞ᰴ⣖ᯏ⢻ߦ෸߷ߔലᨐ
'HHGEVUQHJGCFEQQNKPIQPJKIJGTQTFGTDTCKPHWPEVKQPUWPFGTVJGKPVGPUGJGCVEQPFKVKQP᩿␽㓷 㧘Ꮉේ㕏ᒄ ဌ၂෹⟤ ᖱႎㅢା⎇ⓥᯏ᭴⣖ᖱႎㅢାⲢว⎇ⓥ࠮ࡦ࠲࡯ ␹ᚭᏒ⷏඙ጤጟ↸ጤጟ 
᡼ㅍᄢቇᄢቇ㒮ᢥൻ⑼ቇ⎇ⓥ ජ⪲⋵ජ⪲Ꮢ⟤ᵿ⧯⪲ 
ᗲ⍮ක⑼ᄢቇ⋴⼔ቇㇱ ᗲ⍮⋵㐳ਭᚻᏒጤ૞㓵෶  ⇟࿾ e-mail:ykatagiri@nict.go.jp
Keywords: head cooling, higher-order brain function, heat, continuous performance testⷐᣦ  ᥤᾲⅣႺ᥸㔺ߦࠃࠆᷓㇱ૕᷷਄᣹ߪ૕᷷⺞ᢛਛᨔ㧔ⷞᐥਅㇱ56J㧕ߩ⠴ᥤ෻ᔕࠍ⿠േߒ⊒᳨‫▤ⴊޔ‬᜛ᒛ‫ޔ‬ᔃᜉᢙჇᄢߥߤߩ⥄ᓞ␹⚻෻ᔕࠍ⺃⊒ߔࠆߣหᤨߦᚳ᩶
૕ߣߣ߽ߦᾲ↥↢ᛥ೙ߩߚ߼ઍ⻢෸߮ⷡ㉕ᐲࠍૐᷫߐߖࠆ‫ޕ‬⣻஥ⵍ⬄㊁ߩ FQRCOKPG␹⚻ # ߪ 56J ߦ㓸ਛߒ‫ޔ‬஥ထᩭ߅ࠃ߮೨ㇱᏪ⁁࿁ߢ FQRCOKPG Ớᐲߪૐਅߒ‫ޔ‬ୱᕃᗵ߇⊒↢ߔࠆ‫ߚ߹ޕ‬⣖ᐙߩࠦ࡝ࡦ૞േᕈ࠾ࡘ࡯ࡠࡦ߽ᛥ೙ߐࠇࠆߚ߼⌁᳇߇↢ߓࠆ‫ޕ‬ߎߩ⁁ᴫߢ♖␹⊛ഭ૞ࠍ⽶⩄ߔࠆߣ⣖ᐙߩࡁ࡞ࠕ࠼࡟࠽࡝ࡦ␹⚻♽߇⾮ᵴߔࠆߚ߼‫ޔ‬ࠦ࡝ࡦ૞േᕈ࠾ࡘ࡯ࡠࡦ߇ᛥ೙ࠍฃߌ‫⊒ޔ‬૞⊛⌁᳇ࠍ⺃ዉߔࠆ‫ޔߒ߆ߒޕ‬㗪㗡ㇱ಄ළߦࠃࠆ᷷ᐲਛᨔߦኻߔࠆኙ಄ೝỗߪ‫ⷞޔ‬ᐥਅㇱߩ⠴ᥤ෻ᔕࠍ✭๺ߒ‫ޔ‬೨ㇱᏪ⁁࿁‫ޔ‬஥ထᩭ࡮✢᧦૕ߢߩ FQRCOKPG Ớᐲࠍ਄ߐߖߡᗧ᰼ࠍჇᄢߐߖᓧࠆ‫࠽࡟࠼ࠕ࡞ࡁߚ߹ޕ‬࡝ࡦ␹⚻ # ෸߮㤥⾰ FQRCOKPG ૞േᕈ࠾ࡘ࡯ࡠࡦ # ࠍᵴᕈߐߖߡㆇേ಴ജࠍჇᄢ
ߐߖᓧࠆ‫ޕ‬ ߘߎߢ‫ޔ‬ᥤᾲⅣႺਅߢᮡḰᵈᗧ෻ᔕᤨ㑆᷹ቯ(Continuous Performance Test; CPT)㧛ᮡḰᵈᗧᬌᩏ(CAT㧕ࠍታᣉߒ‫ޔ‬head cooling ߇㜞ᰴ⣖ᯏ⢻ߦ෸߷ߔലᨐࠍ᣿ࠄ߆ߦߒߚ‫ޕ‬
CPT ߪ Rosvold ࠄ߇㐿⊒(1956)ߒߚ␹⚻ᔃℂቇ⊛ᜬ⛯ᕈᵈᗧᬌᩏᴺߢ‫ޔ‬೨㗡⪲ᯏ⢻ࠍ⹏ଔߔࠆߎߣ߇น⢻ߢ޽ࠆ‫ޕ‬25͠33%ߩᮡḰⅣႺ߅ࠃ߮ 35͠80%ߩᥤᾲⅣႺߩੑߟߩ
᧦ઙਅߢᜬ⛯ᕈᵈᗧᬌᩏࠍⴕߞߚ⚿ᨐ‫ޔ‬ᥤᾲⅣႺਅߢᐔဋ෻ᔕᤨ㑆ߪ㧞㧜㧑⒟ᐲჇᄢ
㧔50ms ⒟ᐲ㧕ߔࠆ߇‫ޔ‬㗪㗡ㇱ಄ළߢߪᮡḰⅣႺਅߣห⒟ᐲ߹ߢ࿁ᓳߔࠆߎߣ߇᣿ࠄ
߆ߣߥߞߚ(Fig. 1)‫ ߚ߹ޕ‬80 ࿁ߩ⹜ⴕㆊ⒟ߢߪ‫ޔ‬೨ඨߢߪ޿ߕࠇߩ᧦ઙਅߢ߽ห⒟ᐲ
ߩ෻ᔕᤨ㑆ࠍ␜ߔ߇ᓟඨߦ޿ߊ߶ߤ෻ᔕᤨ㑆߇Ⴧᄢߒ‫ߩߎߦࠄߐޔ‬Ⴧᄢ߇ᥤᾲⅣႺਅߢ㗼⪺ߣߥࠅᜬ⛯ᕈᵈᗧ⛽ᜬࠍ࿎㔍ߦߒߡ޿ࠆߎߣ߇ࠊ߆ߞߚ‫৻ޕ‬ᣇ‫ޔ‬㗪ㇱ಄ළߦࠃࠆ಄ೝỗߪ෻ᔕᤨ㑆ߩჇᄢࠍ᦭ᗧߦ㒖ᱛߒ‫ޔ‬ᵈᗧ⛽ᜬߦነਈߒߡ޿ࠆߎߣ߇␜ߐࠇߚ
(Fig. 2)‫ޕ‬ ߐࠄߦ෻ᔕᤨ㑆ߩࡅࠬ࠻ࠣ࡜ࡓࠍ⺞ߴߚߣߎࠈ‫ޔ‬ᮡḰⅣႺਅ߅ࠃ߮ᥤᾲⅣႺਅߢߩ㗪㗡ㇱ಄ළߢߪࡐࠕ࠰ࡦಽᏓࠍ๒ߔࠆ߇‫ޔ‬ᥤᾲⅣႺਅ᥸㔺ߢߪⶄวࡐࠕ࠰ࡦಽᏓࠍ␜
ߔߎߣ߇ታ㛎ߢ᣿ࠄ߆ߣߥߞߚ(Fig. 3)‫ޕ‬෻ᔕᤨ㑆ߩࡅࠬ࠻ࠣ࡜ࡓ߇ࡐࠕ࠰ࡦಽᏓߢ޽
ࠆߎߣ߆ࠄ‫ޔ‬᛼ߒࡏ࠲ࡦㆇേߩࠗࡦࡄ࡞ࠬߩ࠲ࠗࡒࡦࠣ߇ᾲ㔀㖸⁁ߦ޽ࠆߎߣ߇␜ໂ
ߐࠇߚ‫ߩ⟲⚻␹ޔߪ⽎⃻ߩߎޕ‬ᛥ೙⁁ᘒߢ⺑᣿ߢ߈ࠆ‫ޕ‬හߜ‫ోޔ‬ᛥ೙ߢหᦼ⊒኿น⢻ߥ࠾ࡘ࡯ࡠࡦߩಽᭂ⁁ᘒ߇ᾲំࠄ߉ߦࠃࠅ࡜ࡦ࠳ࡓൻߐࠇࠆߎߣߢࠕ࡚ࠢࠪࡦࡐ࠹
ࡦࠪࡖ࡞ߩᒻ⁁߇ᄌൻߒ‫ޔ‬ኻᔕߔࠆ෻ᔕᤨ㑆߽ᾲ㔀㖸⁁ߩំࠄ߉߇↢ߓࠆߣ⠨߃ࠄࠇࠆ‫ޔߦࠄߐޕ‬ㆊಽᭂ⁁ᘒ㧔㒶ᕈ㧕ࠍߟߊࠅߛߔߩߪࡁ࡞ࠕ࠼࡟࠽࡝ࡦ㧔NE㧕ߦࠃࠆ−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

߽ߩߢ޽ࠅ‫ޔ‬⠴ᥤ෻ᔕߢ NE ߇ᛥ೙ߐࠇࠆߣ㒶ᕈ⁁ᘒࠍ଻ᜬߔࠆᯏ⢻߇ૐਅߒߡ෻ᔕ
ᤨ㑆߇Ⴧᄢߔࠆߎߣ߇␜ໂߐࠇࠆ‫⌁ޕ‬᳇ߪ‫ޔ‬⠴ᥤ෻ᔕߦኻ᛫ߒߡ NE ␹⚻ᵴേࠍ੣ㅴ
ߒߡࠦ࡝ࡦ૞േᕈ࠾ࡘ࡯ࡠࡦࠍᛥ೙ߔࠆߚ߼ߦ↢ߓࠆߣ⠨߃ߡ޿ࠆ‫ޕ‬ᧄ⎇ⓥߪ‫ޔ‬NEDO ⋭ࠛࡀ࡞ࠡ࡯㕟ᣂᛛⴚ㐿⊒੐ᬺ‫ޟ‬ᔟㆡ࡮⋭ࠛࡀࡅࡘ࡯ࡑࡦࡈࠔࠢ
࠲࡯ߦၮߠߊ୘೎ㆡวဳ಄ᥦᚱࠪࠬ࠹ࡓߩ⎇ⓥ㐿⊒‫ࠍ࠻ࡦ࡜ࠣߩޠ‬ฃߌߡታᣉߒߚ‫ޕ‬㪙㪸㫊㪼㫃㫀㫅㪼
㪫㪼㫄㫇㩷㪉㪌㩷㪻㪼㪾㫉㪼㪼㫊㪟㫌㫄㫀㪻㫀㫋㫐㩷㪊㪊㩼㪍㪇㪇

baseline
Without cooler
With cooler

㪌㪇㪇Response time(ms)㫂㪸㫋㪸㪈㪫㪼㫄㫇㩷㪊㪌㩷㪻㪼㪾㫉㪼㪼㫊㪟㫌㫄㫀㪻㫀㫋㫐㩷㪏㪇㩼

㪋㪇㪇

㪊㪇㪇

㪉㪇㪇

㪈㪇㪇
㪇

㪈㪇

㪉㪇

㪊㪇

㪋㪇

㪌㪇

㪍㪇

㪎㪇

㫀㫅㪻㪼㫏

Trial index

Fig. 2 Response time change at every trial
㩷

㩷

㪉㪌

㪉㪇

㩷㫂㪸㫋㪸㪈

㩷㪿㪼㪸㫋㩷㫋㫆㫋㪸㫃㩿㪿㫆㫋㩷㪈㩷㪉㪀
㪉㪇

ᮡḰⅣႺ

ో䊂䊷䉺

㪈㪌

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㪏㪇第12回人間情報学会講演会ポスターセッション（2012.9.11）

࠼࡜ࠗ㔚ᭂࠍ៞タߔࠆᄙ࠴ࡖࡦࡀ࡞ࡢࠗࡗ࡟ࠬ
⣖ᵄ࠮ࡦࠪࡦࠣࠪࠬ࠹ࡓ
9KTGNGUUOWNVKEJCPPGN'')U[UVGOUYKVJFT[GNGEVTQFGU᩿␽㓷ᖱႎㅢା⎇ⓥᯏ᭴⣖ᖱႎㅢାⲢว⎇ⓥ࠮ࡦ࠲࡯ ␹ᚭᏒ⷏඙ጤጟ↸ጤጟ 

e-mail:ykatagiri@nict.go.jp
Keywords: wearable sensor, wireless EEG, dry electrodeⷐᣦ  ◲ଢߦ⣖ᵄࡕ࠾࠲߇น⢻ߥࡢࠗࡗ࡟ࠬ⣖ᵄ⸘᷹ࠪࠬ࠹ࡓߪᣣᏱ↢ᵴⓨ㑆ߩߺߥࠄߕ
㒮ౝᄖක≮ߦ߅ߌࠆ↢૕▤ℂߦ߽᦭ᦸߢ޽ࠆ‫⥃ޕ‬ᐥߩ႐ߢታ↪ൻߔࠆߚ߼ߦߪ‫ޔ‬᷹ቯ♖ᐲࠍഠൻߐߖߕⵝ⌕◲ᤃᕈࠍᄢ᏷ߦะ਄ߔࠆߎߣ߇ᔅⷐߢ޽ࠆ‫⥃߼ߚߩߎޕ‬ᐥ↪⣖ᵄ⸘᷹ࠪࠬ࠹ࡓߩ⿥ዊဳ࡮ࡢࠗࡗ࡟ࠬൻߣߣ߽ߦ‫ߦߕࠊ૶ࠍ࡞ࠚࠫޔ‬⣖ᵄ⸘᷹߇น⢻ߥ࠼࡜ࠗ㔚ᭂߩᬌ⸛ࠍⴕߞߚ‫ޕ‬ዉ㔚ᕈࠫࠚ࡞ߦࠃࠆ㗡⊹ߣߩ⦟ᅢߥ㔚᳇⊛ធ⛯ߪૐ㔀㖸⣖ᵄ⸘᷹਄㊀ⷐߢ޽ࠆ߇‫ޔ‬৻ᣇ‫◲ޔ‬ଢߥⵝ⌕ߩ㓚ኂߣߥࠅᓧࠆ‫߼ߚߩߎޕ‬᭽‫ࠍ࡞ࠚࠫߥޘ‬ᔅⷐߣߒߥ޿㔚ᭂ㧔࠼࡜ࠗ㔚ᭂ㧕ߩ㐿⊒߇ㅴ߼ࠄࠇߡ߈ߚ‫⦟ޕ‬ᅢߥ㔚᳇⊛ធ⸅ࠍታ⃻ߔࠆߚ߼‫ߦ⊹⌀ޔ‬㆐ߔࠆ㔚ᭂ᭴ㅧ߇⠨᩺ߐࠇߚ‫ߦ▤ࡄࡦ࡝ޔ▤ⴊޕ‬ធ⸅ߒߥ޿ࠃ߁ߦ㜞ߐࠍ෩ኒߦ೙ᓮߒߡ
޿ࠆ߇‫⥃ޔ‬ᐥߢ૶߁ߚ߼ߦߪ቟ోᕈࠍ⏕଻߇⺖㗴ߢ޽ࠆ‫⎇ᧄߢߎߘޕ‬ⓥߢߪ㗡㜬ߩᓇ㗀ࠍฃߌߦߊ޿ࡑ࡞࠴ࡇࡦ࠲ࠗࡊࠍᬌ⸛ߒߚ㧔(KI㧕‫⚿ߩߘޕ‬ᨐ‫࡞࠲ࠫ࠺ޔ‬ାภಣℂߩߺߢ߽ OGVCFCVC ߆ࠄ⣖ᵄ߇᛽಴น⢻ߢ޽ࠆߎߣࠍታ㛎ߦࠃࠅ⏕⹺ߒߚ㧔(KI㧕‫ޕ‬ߐࠄߦ‫ޔ‬EJCPPGNUWPKV ㅛ୚ࠪࠬ࠹ࡓߦࠃࠆᄙ࠴ࡖࡦࡀ࡞ൻߩᬌ⸛ࠍⴕ޿‫ޔ‬EJCPPPGNU ߢ HGCUKDKNKV[ࠍᬌ⸽ߒߚ‫⚿ߩߘޕ‬ᨐ‫( )'' ࡞ࡀࡦࡖ࠴ ޔ‬KI ߩ࠮࠶࠻ࠕ࠶ࡊᤨ㑆ߪ⚂  ⑽⒟ᐲ㧔ࠠࡖ࠶ࡊࠍ߆߱ߖߡ㔚ᭂߩ૏⟎ࠍᓸୃᱜߔࠆᤨ㑆㧕ߢ޽ࠅᓥ᧪ߩ '') ߩ࠮࠶࠻ࠕ࠶ࡊᤨ㑆ࠍᄢ᏷ߦ⍴❗ߢ߈ࠆߎߣࠍ⏕⹺ߒ (KI ‫ޔ‬ᢇᕆක≮ߦ߅ߌࠆㆡ↪ᕈࠍ᣿ࠄ߆ߦߒߚ‫ޔߚ߹ߦࠄߐޕ‬ᣣᏱ↢ᵴⓨ㑆ߢߩㆡ↪▸࿐ࠍ᜛ᄢߔࠆߚ߼‫⌁⌧ޔ‬⣖ᵄขᓧࠍᬌ⸛ߒߚ‫ߩᤨ⌁⌧ޔࠄ߇ߥߒ߆ߒޕ‬૕൓ߦࠃࠅ㔚ᭂߦᯏ᪾ᄌേ߇ടࠊࠆߣᄢ߈ߥࠕ࡯࠴ࡈࠔࠢ࠻߇಴⃻ߒ᷹ቯ߇࿎㔍ߣߥࠆ߶߆‫ࡦࡇ࠴࡞ࡑޔ‬࠲ࠗࡊߩ㔚ᭂߪઔะߌᆫ൓ߢ∩ߊታ↪ߦଏߐߥ޿ߎߣ߇᣿ࠄ߆ߣߥߞߚ‫ޕ‬ାภಣℂࠕ࡞ࠧ࡝࠭ࡓߩᵞ✵ߦࠃࠅࠕ࡯࠴ࡈࠔࠢ࠻ߩ㒰෰ߪน⢻ (KI ߢ޽ࠆߣ⠨߃ࠄࠇࠆ߇‫ޔ‬࠼࡜ࠗ㔚ᭂߦߟ޿ߡߪ−∩ᗵࠍᄢ᏷ߦᡷༀߔࠆߎߣ߇ᔅⷐߢ޽ࠆ‫ޕ‬ᧄ⎇ⓥߪ‫ޔ‬JST-CREST ߩࠣ࡜ࡦ࠻ࠍฃߌߡታᣉߒߚ‫ޕ‬−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

Meta data

7 mm

Bandpass: 䋲䋭䋭59Hz

Fig. 2 EEG extraction from noisy signals ofmulti-pin-type dry electrodes by using anappropriate band-pass filters.
Fig. 1 Wireless 4-channel EEG sensor systemwith dry multi-pin-type electrodes

Fig. 3 Wireless, 8-channel EEG system withdry electrodes.
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Fig. 5 An EEG diagram measured
under the sleep record by using an
EEG system with dry electrodes

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Fig. 4 An EEG diagram measured by using
a wireless,8-channel EEG system with dryelectrodes.

−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）♖ᴤߩ༦ⷡᛩਈ߇ᵈᗧᯏ⢻ߦ෸߷ߔᓇ㗀


ဌ၂෹⟤

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᩿␽㓷 㧘ቝቝ૒⟤⋿ 

1ᗲ⍮ක⑼ᄢቇ⋴⼔ቇㇱ ᗲ⍮⋵㐳ਭᚻᏒጤ૞㓵෶ 1 ⇟࿾ 1ᖱႎㅢା⎇ⓥᯏ᭴ ⣖ᖱႎㅢାⲢว⎇ⓥ࠮ࡦ࠲࡯ ␹ᚭᏒ⷏඙ጤጟ↸ጤጟ 588-2
3␹ᚭᄢቇᄢቇ㒮 ଻ஜቇ⎇ⓥ⑼ ␹ᚭᏒ㗇⏴඙෹߇ਐ 7-10-2
2email: bohgaki @aichi-med-u.ac.jp
Keywords: pain control, essential oil, electroencephalogram, dopaminergic systemⷐᣦ⧐㚅ᕈើ⊒ᕈ᦭ᯏൻว‛ߢ޽ࠆ♖ᴤࠍ↪޿ߚ≮ᴺߪ‫ޔ‬ᗵⷡฃኈེࠍ੺ߒߚ⵬ቢઍᦧක≮ߩ৻ߟߢ޽ࠆ‫♖ޕ‬ᴤߪẜ࿷⊛ߥᯏ⢻ߣߒߡ↢૕ߩᕡᏱᕈߦነਈߒ‫ߩߘޔ‬ᔕ↪▸࿐ߪ⻉∝⁁ߩ✭๺ߦ෸߱ߣ⠨߃ࠄࠇߡ޿ࠆ‫ޕ‬ᚒ‫♖ߩ♽㉕ⷡߪޘ‬ᴤ߇༦ⷡೝỗߦࠃࠅਛ⣖⣻஥ⵍ⬄㊁ 86# ߩ࠼࡯ࡄࡒࡦ␹⚻♽ࠍ⾮ᵴߔࠆߎߣߢ㒰∩ലᨐࠍ⊒⃻ߢ߈ࠆน⢻ᕈࠍႎ๔ߒߡ߈ߚ‫੹ޕ‬࿁‫ޔ‬ኙ಄
−∩⽶⩄ᓟߩ⡬ⷡࠝ࠼ࡏ࡯࡞⺖㗴ߦࠃࠆ♖ᴤๆ౉ߩ੐⽎㑐ㅪ㔚૏߅ࠃ߮♖ᴤ೎ߩᮡḰᵈᗧᬌ
ᩏᴺ %QPVKPWQWU2GTHQTOCPEG ߆ࠄ♖ᴤߩⷡ㉕࡮ᵈᗧ෻ᔕߩ෸߷ߔᓇ㗀ߦߟ޿ߡᬌ⸽ታ㛎ࠍⴕߞߚ⚿ᨐ‫♖ޔ‬ᴤ߇ࡕࡁࠕࡒࡦ␹⚻♽ࠍㅢߓߡ⣖ᯏ⢻ࠍᵴᕈൻߔࠆᩮ᜚ߣߥࠅᓧࠆ⍮⷗ࠍᓧߚߩߢႎ๔ߔࠆ‫ޕ‬♖ᴤߩๆ౉ߪ Nogo ߢ conflict negative‫ޔ‬Go ߢ CNV ߩᒻᚑࠍ⹺߼㧔Fig.3㧕‫ ޔ‬Fig.5 ߢߪ♖ᴤๆ౉ߢ㧿㧞೨ߦ CNV ߇ᒻᚑ‫ޔ‬S1 ᓟߦᱜߦ߰ࠇߚ‫ޕ‬control㧔᳓㧕ߢߪ CNV ߩᒻᚑߪߥ߆ߞߚ‫ޕ‬♖ᴤߩๆ౉ߪ control ࠃࠅ߽೨㗡⪲ߩᯏ⢻߇㜞޿ߎߣ߇ផ᷹ߐࠇߚ‫♖ߚ߹ޕ‬ᴤๆ౉ߦࠃߞߡ෻
ᔕᤨ㑆ߪㅦߊߥߞߡ޿ࠆ(Fig.4)‫ޕ‬એ਄ߦߟ޿ߡ㍙૕⚦⢩ߩ㒶ᕈ㔚૏ᒻᚑ㧔CNV)ߪ Ach ߦࠃࠆᛥ೙ࡕ࠺࡞߆ࠄផ᷹ߔࠆߣ‫ޔ‬㒶ᕈߦ⌕⋡ߔࠇ߫⥝ᅗᕈ NE ߩᵴᕈ‫ޔ‬ᛥ೙ᕈߩ࠮ࡠ࠻࠾ࡦߩᵴ
ᕈ߇᧦ઙߣߥࠆߚ߼‫ޔ‬SNRI ߢߩ⊹⾰ߩ㎾㕒߇␜ໂߐࠇࠆ‫ߚ߹ޕ‬㓁ᕈߣߥࠆߚ߼ߦߪ VTA ߩ
DA‫ޔ‬CC ߩ 5HT ߩ߶߆ STh ߩ Glu㧘NTh ߩ Ach ߩᵴᕈ߇ᔅⷐߣߥࠆ‫♖ޔࠄ߆ࠄࠇߎޕ‬ᴤๆ౉߇⊹⾰ߩ㎾㕒ൻ㧔಴ജߩߚ߼ߩḰ஻㧕‫ޔ‬㓁ᕈൻߩߚ߼ߩ VTA ߩ DA ߩᵴേ߇␜ໂߐࠇߚ‫ߐ ޕ‬ࠄߦᵈᗧߩ⛽ᜬߪ࠮ࡠ࠻࠾ࡦ␹⚻♽ߩ૞↪ߣߒߡ⊹⾰ߩᛥ೙‫ޔ‬D2 ᛥ೙㧔tonic DA ߩᛥ೙㧕ߩน⢻ᕈ߽޽ࠆߣ⠨߃ࠆ‫♖ޕ‬ᴤ೎ߩᮡḰᵈᗧᬌᩏᴺ(Continuous Performance)ߦࠃࠆ෻ᔕᤨ㑆㧔Fig.6)ߪ‫♖ߩ♽㉕ⷡޔ‬ᴤߪࠦࡦ࠻ࡠ࡯࡞ࠃࠅ߽෻ᔕᤨ㑆߇⍴ߊ‫ޔ‬㎾㕒♽ߪࠦࡦ࠻ࡠ࡯࡞ࠃࠅ
߽㐳ߊߥߞߚ‫ޕ‬෻ᔕᤨ㑆ߪ 80 ࿁ߩ⹜ⴕࠍㅢߓߡᄢ߈ߥᄌൻߪߥߊ‫ޔ‬ᵈᗧᯏ⢻ߪ⹜㛎ࠍㅢߓߡ৻⽾ߒߡ৻ቯߢ޽ࠅ‫⹜ޔ‬㛎ߦࠃࠆᵈᗧᯏ⢻ߩᷫዋ߿Ⴧᄢߪߥ߆ߞߚ‫ޕ‬㎾㕒♽ߩ♖ᴤߢߪ෻ᔕ
ᤨ㑆߇ో૕⊛ߦ㐳ߊߥࠆߩߢߪߥߊ‫ࠅࠃߦⴕ⹜ޔ‬ᭂᐲߦᔕ╵ᤨ㑆߇㐳ߊߥࠆࡃ࡯ࠬ࠻⃻⽎߇ߺࠄࠇࠆ‫ޕ‬㎾㕒૞↪߇⌧⌁⊒૞㘃ૃߩ⃻⽎ࠍᒁ߈⿠ߎߒߚ߽ߩߣផኤߐࠇࠆ‫♖ߩ♽㉕ⷡޕ‬ᴤߢߪ‫ߩ࡞ࡌ࡟㉕ⷡߥ⊛࠻ࠬ࡯ࡃߥ߁ࠃߩߎޔ‬ૐਅ߇ᷫዋߒߡ߅ࠅ‫߇࡞ࡌ࡟㉕ⷡޔ‬ၷ࿕ߦ⛽ᜬߐࠇࠆߣ⠨߃ࠄࠇࠆ‫ޕ‬෻ᔕᤨ㑆ߩᭂᐲߩ⍴❗ൻߪࡁ࡞ࠕ࠼࡟࠽࡝ࡦߦࠃࠆୃ㘼ߣផኤߐࠇࠆ‫ޕ‬ᧄ⎇ⓥߩ৻ㇱߪ‫⑼ޔ‬ቇ⎇ⓥ⾌ഥᚑ੐ᬺ㧔ၮ⋚ %㧕߅ࠃ߮ ,56%4'56 ߩࠣ࡜ࡦ࠻ߦࠃࠆ‫ޕ‬−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

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(KI ኙ಄−∩⽶⩄ᓟߩ⡬ⷡࠝ࠼ࡏ࡯࡞⺖㗴ߦࠃࠆ♖ᴤߩ੐⽎㑐ㅪ㔚૏ࡊࡠ࠻ࠦ࡞㩷㩷 㪜㪩㪧㩷 㪑㩷 㪞㫆㩿㪉㪇㪇㪇㪟㫑㪀䈏䌇䌯䇮㪥㫆㩷㪾㫆㩿㪈㪇㪇㪇㪟㫑㪀䉕 㪊㪌㪇㫄㫀㫅 䈮㈩೉䈚䈢䉺䉴䉪 㪋 䊌䉺䊷䊮䉕䊤䊮䉻䊛䈮ឭ␜䈚䇮ඨ㐿⌒⁁ᘒ䈪 㪌㪇㪺㫄 䈱〒㔌䈮⾍ઃ䈚䈢චᢥሼ䉕⷗䈭䈏䉌⊒ჿ䉁䈢䈲䊗䉺䊮䉕᛼䈜෻ᔕേ૞䉕ታⴕ䇯㩷㩷 ⷡ㉕♽♖ᴤ䉕ᷙ๺䈚䇮㪇㪅㪉㪌㫄㫃 䉕㥦 㪌㪺㫄 䈮࿕ቯ䈚䈢✎⃿䈮Ṣਅ䇯㩷
ⷡ㉕♽♖ᴤ䋺㽲䊨䊷䉵䊙䊥䊷䊶䊕䉾䊌㩷䊷䊶䊡䊷䉦䊥䇮㽳䊔䊦䉧䊝䉾䊃䊚䊮䊃䊶㩷㪚㫆㫅㫋㫉㫆㫃㩿᳓䋩㫆㫉㩷㪚㪧㪫㩷䉝䊦䉮䊷䊦ྃ㔵㩷቟㕒㩷㪜㪩㪧㩷䊧䊝䊮䊋䊷䊛䊶䊕䊌䊷䊚䊮䊃㩷 㩷㩷♖ᴤ䈱੐೨ๆ౉㩷㪏㪇 ࿁㩷଻᷷䈚䈭䈏䉌ભᙑ㩷
㪌㫄㫀㫅㩷
㪌㫄㫀㫅㩷
㪊㫄㫀㫅㩷
㪌㫄㫀㫅㩷
㪈㪇㫄㫀㫅㩷
㩷㪝㫀㪾㪅㪉 ♖ᴤ೎ᮡḰᵈᗧᬌᩏᴺ㩿㪚㫆㫅㫋㫀㫅㫌㫆㫌㫊㪧㪼㫉㪽㫆㫉㫄㪸㫅㪺㪼㩷㪫㪼㫊㫋䋺㪚㪧㪫㪀䈮䉋䉎෻ᔕᤨ㑆䈱⋧㆑䈱䊒䊨䊃䉮䊦㩷

Essential oil㩷㩷㪇㪅㪇㪌㪏㩷㪄㩷㪇㪅㪇㪍㪉㩷㪟㫑㩷䊋䊮䊄䊑䊨䉾䉪㩷㪽㪽㫋㩷㪽㫀㫃㫋㪼㫉㩷㫆㪽㩷㩹㪥㫆㪾㫆㩹Ὂ Essential oilᴾ Nogo㩷Ὂ Contorol Nogo㩷㪉㪇㪍㪘㩷㪥㫆㪾㫆㩷㩷㪉㪇㪍㪚㩷㫅㫆㪾㫆㪈㪌㪈㪇㪉㪇㪌㪜㪩㪧㪋
㪈㪇㪌㪉㪇㪍㪘㩷㪥㫆㪾㫆

㪌

㪥㫆㪾㫆

㪇

㪇

㪄㪌

㪄㪌

㪄㪈㪇
㪉㪏㪇㪇

㪄㪈㪇

㪄㪈㪌
㪉㪏㪇㪇

㪊㪇㪇㪇

㪊㪉㪇㪇

㪊㪋㪇㪇

㪊㪍㪇㪇

㪊㪏㪇㪇

㪊㪇㪇㪇

㪊㪉㪇㪇

㪋㪇㪇㪇

㪫㫀㫄㪼㩿㫄㫊㪀
㩷㪇㪅㪇㪌㪏㩷㪄㩷㪇㪅㪇㪍㪉㩷㪟㫑㩷䊋䊮䊄䊑䊨䉾䉪㩷㪽㪽㫋㩷㪽㫀㫃㫋㪼㫉㩷㫆㪽㩷㩹㪞㫆㩹

㪈㪇

㪊㪋㪇㪇

㪊㪍㪇㪇

㪊㪏㪇㪇

㪋㪇㪇㪇

㪫㫀㫄㪼㩿㫄㫊㪀
㩷㪉㪇㪍㪘㩷㪞㫆
Ὂ Essential oilᴾ
Nogo㩷㩷㪉㪇㪍㪚㩷㪞㫆
Ὂ Contorol Nogo㩷

㪈㪇

㪌
㪌

㩷 㪇
㩷
㪄㪌
㩷
㩷 㪄㪈㪇
㪉㪏㪇㪇
㪊㪇㪇㪇
㪊㪉㪇㪇
㪊㪋㪇㪇
㪊㪍㪇㪇
㪊㪏㪇㪇
㪋㪇㪇㪇
㪫㫀㫄㪼㩿㫄㫊㪀
㩷㪝㫀㪾㪅㪊㪊㩷 ♖ᴤ䈫䉮䊮䊃䊷䊦䋨᳓䋩䈱੐⽎㑐ㅪ㔚૏㩷
㩷
㩷
㩷
㩷
㩷
㩷

㪉㪇㪍㪘㩷㪞㫆

㩷㪇㪅㪇㪌㪐㩷㪄㩷㪇㪅㪇㪍㪈㩷㪟㫑㩷䊋䊮䊄䊑䊨䉾䉪㩷㪽㪽㫋㩷㪽㫀㫃㫋㪼㫉㩷㫆㪽㩷㩹㪥㫆㪾㫆㩹Control(water)㩷
㩷 㪈㪌
㩷
㪈㪇
㩷
P300㩷
㪌
P100㩷
㩷
㩷 㪇
㩷 㪄㪌
N80㩷
N200㩷
㩷
㩷㪇㪅㪇㪌㪐㩷㪄㩷㪇㪅㪇㪍㪈㩷㪟㫑㩷䊋䊮䊄䊑䊨䉾䉪㩷㪽㪽㫋㩷㪽㫀㫃㫋㪼㫉㩷㫆㪽㩷㩹㪞㫆㩹
㪄㪈㪇
㪈㪇
㪉㪏㪇㪇
㪊㪇㪇㪇
㪊㪉㪇㪇
㪊㪋㪇㪇
㪊㪍㪇㪇
㪊㪏㪇㪇
㪋㪇㪇㪇
㩷
㪫㫀㫄㪼㩿㫄㫊㪀
㩷 㪌
㩷

㪞㫆

㪞㫆

㪥㫆㪾㫆

㩷

㪇

㪇

㪄㪌

㪄㪌

㪄㪈㪇
㪉㪏㪇㪇

㪄㪈㪇
㪉㪏㪇㪇

㪊㪇㪇㪇

㪊㪉㪇㪇

㪊㪋㪇㪇

㪫㫀㫄㪼㩿㫄㫊㪀

㪊㪍㪇㪇

㪊㪏㪇㪇

㪋㪇㪇㪇

㪊㪇㪇㪇

㪊㪉㪇㪇

㪊㪋㪇㪇

㪊㪍㪇㪇

㪊㪏㪇㪇

㪋㪇㪇㪇

㪫㫀㫄㪼㩿㫄㫊㪀㪝㫀㪾㪅㪋㪾 㪋㩷 ♖ᴤ䈫 㪺㫆㫅㫋㫉㫆㫃 䈱 㪚㪥㪭㩷㫇㪸㫉㪸㪻㫀㪾㫄㩷㫇㪾㩷㩷㪝㫀㪾㪅㪌㩷 㩷 㪚㫑 ㇱ૏䈮䈍䈔䉎♖ᴤๆ౉䈱 㪚㪥㪭㩷㪝㫀㪾㪅㪍㩷 ♖ᴤ೎䈱ᮡḰᵈᗧᬌᩏᴺ䈮䉋䉎෻ᔕᤨ㑆㩷㩷−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

ᗵᖱ⒖౉ߦࠃࠆ๺㖸ߦኻߔࠆ㓐઻㒶ᕈᄌേߩᄌൻ

Ꮉේ㕏ᒄ(⊒⊒⴫⠪)1,1᩿␽㓷 2᡼ㅍᄢቇ ᄢቇ㒮ᢥൻ⑼ቇ⎇ⓥ⑼ ජ⪲⋵ජ⪲Ꮢ⟤ᵿ඙⧯⪲ 2-112ᖱႎㅢା⎇ⓥᯏ᭴ ⣖ᖱႎㅢାⲢว⎇ⓥ࠮ࡦ࠲࡯       ౓ᐶ⋵␹ᚭᏒ⷏඙ጤጟ↸ጤጟ 588-2
email: kawahara@ouj.ac.jp
Keywords: ੐⽎㑐ㅪ㔚૏‫ޔ‬๺㖸‫ޔ‬ᗵᖱⷐᣦ ᧄ⎇ⓥߢߪ‫ޔ‬ᗵᖱ⒖౉ߩ᦭ήߦߟ޿ߡ⹏ଔࠍⴕ߁ߚ߼ߩ‫ޔ‬๺㖸ೝỗࠍ↪޿ߚ੐⽎㑐ㅪ㔚૏᷹ቯࡊࡠࠣ࡜ࡓࠍឭ᩺ߒߚ‫ޕ‬๺㖸㧔ࡔࠫࡖ࡯ࠦ࡯࠼ߣࡑࠗ࠽࡯ࠦ࡯࠼㧕ࠍೝỗⷐ⚛ߣߒߡ↪޿ߚ⺖㗴ߦࠃࠆ੐⽎㑐ㅪ㔚૏ߩ᷹ቯࠍⴕ޿‫ޔ‬ᗵᖱ㧔ᭉߒ޿‫ޔ‬෸߮ᖤߒ޿㧕⒖౉ߦࠃࠆᄌൻࠍ᷹ቯߒߚ‫ޕ‬ ᗵᖱ⒖౉ߒߚᗵᖱߩ⒳㘃ߣᮡ⊛ೝỗߩ๺㖸ߩ⒳㘃߇৻⥌ߒߚߣ߈ߦᲧߴ‫ߥߒ⥌৻ޔ‬޿ߣ߈ߪ P300 ߩẜᤨ߇ᄢ߈ߊߥࠆ⚿ᨐࠍᓧߚ‫ޕ‬੹ᓟߩዷᦸ⥄ᓞ␹⚻ᄬ⺞߿ട㦂ߦࠃࠆ‫ޔ‬ᗵᖱ⴫⃻ߦ㑐ߔࠆᡰ㓚ߩ⹏ଔᣇᴺߣߒߡߩᔕ↪ࠍ⋡ᜰߔ‫ޕ‬ ࡔࠫࡖ࡯ࠦ࡯࠼߿ࡑࠗ࠽࡯ࠦ࡯࠼ߦ㒢ቯߒߥ޿๺㖸ೝỗࠍ↪޿‫ޔ‬ᗵᖱ⒖౉ߩᐲว޿
ࠍ್ᢿߔࠆߎߣࠍ⹜ⴕߔࠆ‫ޕ‬ታ㓙ߩⵍ㛎⠪ߩᗵᖱ⒖౉ߩ᦭ήࠍ್ᢿߔࠆᣇᴺࠍ૬↪ߒ‫ޔ‬ࠃࠅାᙀᕈߩ㜞޿ታ㛎ᣇᴺࠍ⏕┙ߔࠆ‫ޕ‬−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

૎ੲ୎ো
5 min

Start

হ଴ঢ়৴ਗ਼ਜ਼೾৒‫ق‬૎ੲ୎োಲਢ‫ك‬
about 7.5 minEnd࿑㧝㧚੐⽎㑐ㅪ㔚૏᷹ቯߪ‫ޔ‬5 ಽ㑆ߩᗵᖱ⒖౉ߩᓟߦⴕߞߚ

⴫㧝㧚㔚ᭂߩ૏⟎㧔࿖㓙 10-20 ᴺ㧕Cz, Fz ߦ߅ߌࠆ੐⽎㑐ㅪ㔚૏᷹ቯߢ‫ޔ‬3 ੱߩⵍ㛎⠪ోߡߦ߅޿ߡ‫ޔ‬ᗵᖱ⒖౉ߒߚᗵᖱߩ⒳㘃ߣᮡ⊛ೝỗߩ๺㖸ߩ⒳㘃߇৻⥌ߒߚߣ߈ߦᲧߴ‫৻ޔ‬⥌ߒߥ޿ߣ߈ߪ P300 ߩẜᤨ߇ᄢ߈ߊߥߞߚ‫ޕ‬

[Cz]
଺ୡ঻

૎ੲ঳ಱ
‫ق‬๩‫ك‬૎ੲਂ঳ಱ‫ق‬๩‫ك‬૎ੲ঳ಱ‫ق‬௫‫ك‬૎ੲਂ঳ಱ‫ق‬௫‫ك‬

A

297

342

307

359

B

389

396

330

438

C

299

332

299

326

P300ཎৎ (ms)
[Fz]
଺ୡ঻

૎ੲ঳ಱ
‫ق‬๩‫ك‬૎ੲਂ঳ಱ‫ق‬๩‫ك‬૎ੲ঳ಱ‫ق‬௫‫ك‬૎ੲਂ঳ಱ‫ق‬௫‫ك‬

A

297

341

307

387

B

389

420

329

439

C

299

334

295

342

P300ཎৎ (ms)−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

㘩‛ฃኈߦ߅ߌࠆળ⹤ߣ๰ྨߩ㑐ଥᕈߩၮ␆ᬌ⸛㈬ㅧ ᱜ᮸ 1㧘ᧁ᧛ ᢕ 2
12␹ᄹᎹᄢቇ Ꮏቇㇱ 221-8686 ␹ᄹᎹ⋵ᮮᵿᏒ␹ᄹᎹ඙౐ⷺᯅ 3-27-1
᧲੩㔚ᯏᄢቇ ᖱႎⅣႺቇㇱ 270-1382 ජ⪲⋵ශ⷏Ꮢᱞ⷏ቇ࿦บ 2-1200
email: shuzo@kanagawa-u.ac.jp
Keywords: ౒㘩, ࠙ࠚࠕ࡜ࡉ࡞࠮ࡦࠨ, ๰ྨ, ࠦࡒࡘ࠾ࠤ࡯࡚ࠪࡦ

ⷐᣦᧄ⎇ⓥߩᦨ⚳⊛ߥ⋡⊛ߪ‫ޔ‬㘩‛ฃኈߦ㑐ࠊࠆࠦࡒࡘ࠾ࠤ࡯࡚ࠪࡦዻᕈࠍታ㛎⊛ߦ᛽಴ߒ‫ޔ‬ኻ㕙ࠦࡒࡘ࠾ࠤ࡯࡚ࠪࡦ߇ᣂᄸ㘩‛ߩฃኈߦ෸߷ߔᓇ㗀ߩᔃℂ࡮ⴕേࡕ࠺࡞ࠍ
᭴▽ߔࠆߎߣߢ޽ࠆ‫ޕ‬ⶄᢙੱߢ㘩੐ࠍⴕ߁౒㘩ߦ߅޿ߡ‫⋧ߪੱޔ‬ᚻߣߩ㑐ଥ⺞ᢛߩߚ߼ߦ㘩ຠㆬᛯ߿៨㘩㊂ࠍ⺞ᢛߔࠆߣߐࠇࠆ‫ޕ‬ኻ㕙ࠦࡒࡘ࠾ࠤ࡯࡚ࠪࡦ߇㘩‛ฃኈߦ෸߷ߔᓇ㗀ࠍ⸃᣿ߔࠆ
ߴߊ‫౒ޔ‬㘩ߩ႐ߦ߅޿ߡળ⹤㊂߿๰ྨ⁁ᘒࠍࡕ࠾࠲࡝ࡦࠣߔࠆታ㛎ࠪࠬ࠹ࡓࠍ᭴▽ߒߚ‫ߚߞⴕߦߢ߹ࠇߎޕ‬ၮ␆ᬌ⸛ߦߟ޿ߡႎ๔ߔࠆ‫ޕ‬੹ᓟߩዷᦸળ⹤㊂߿๰ྨ⁁ᘒࠍࡕ࠾࠲࡝ࡦࠣߔࠆߚ߼‫ߩဳࡦࠜࡈࡗࠗޔ‬㛽વዉࡑࠗࠢ㧔࠙ࠚࠕ࡜ࡉ࡞࠮ࡦࠨ㧕ࠍ↪޿ߡญ⣧ౝߩ㖸ᖱႎࠍ෼㓸ߒ‫ޔߚ߹ޔ‬㖸ᖱႎ߆ࠄ‫ޔ‬㘩੐ߩ࠲ࠗࡒ
ࡦࠣ‫ޔ‬๰ྨߩ⁁ᘒ‫ޔ‬㘩ຠߩᕈ⁁ߣ޿ߞߚ᭽‫ߥޘ‬㘩⠌ᘠߦ㑐ߔࠆᖱႎࠍ⥄േ⊛ߦಽᨆߔࠆࠕ࡞ࠧ࡝࠭ࡓ㧔࿑㧝㧕ߩ⎇ⓥࠍⴕߞߡ߈ߚ[1]‫ߩߎޕ‬㘩⠌ᘠߩ࠮ࡦࠪࡦࠣࠪࠬ࠹ࡓߪ‫ޔ‬୘ੱߩ↢ᵴ⠌ᘠߩ⃻⁁ᛠីߦᵴ߆ߔߎߣ߇น⢻ߢ޽ࠅ‫ޔ‬කᏧ߿ᩕ㙃჻ߩᜰዉߣ޿߁ࡈ
ࠖ࡯࠼ࡃ࠶ࠢࠍᓧࠆߎߣߦࠃࠅ‫ⴕߩࠄ⥄ޔ‬േᄌኈࠍଦߔߎߣࠍ⁓ߞߚ߽ߩߢ޽ࠆ㧔࿑㧞㧕‫ޕ‬ߐߡ‫ޔ‬ᣂᄸ㘩‛ฃኈߦߪઁ⠪ߣߩࠦࡒࡘ࠾ࠤ࡯࡚ࠪࡦ߇㊀ⷐߢ޽ࠆߎߣ߇␜ໂߐࠇ
ߡ޿ࠆ߽ߩߩ‫ޔ‬ታ㓙ߦ౒㘩ⴕὑ߇ᣂᄸ㘩‛ߩ៨ขߦ෸߷ߔᓇ㗀ߦߟ޿ߡߩ⋥ធ⊛ߥᬌ⸛ߪዋߥ޿‫੹ߢߎߘޕ‬ᓟߪ‫ޔ‬෹ੱห჻ 2 ฬߢߩ౒㘩ⴕὑ߇ᣂᄸ㘩‛ߩ៨ขߦ෸߷ߔᓇ
㗀ࠍ‫ޔ‬਄⸥ߩ㖸ᖱႎߦࠃࠆ࠮ࡦࠪࡦࠣࠪࠬ࠹ࡓ߿ࡆ࠺ࠝࠞࡔ࡜ࠪࠬ࠹ࡓߥߤࠍ↪޿ߚታ㛎⊛ᬌ⸛ࠍߔࠆߣߣ߽ߦ‫ޔ‬ᣂᄸ㘩‛ࠍ៨ขߔࠆ㓙ߦෳਈ⠪ߩ㑆ߦߤߩࠃ߁ߥ⸒⺆࡮㕖⸒⺆ⴕേ߇ߺࠄࠇࠆ߆ࠍ⺞ߴߡ޿ߊ੍ቯߢ޽ࠆ‫ޕ‬ෳ⠨ᢥ₂[1] M. Shuzo, et al., “Wearable Eating Habit Sensing System Using Internal Body Sound,”
Journal of Advanced Mechanical Design, Systems, and Manufacturing, vol. 4, no. 1, pp.
158-166, 2010.

−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

࿑㧝㧚㖸ᖱႎࠍ↪޿ߚળ⹤㊂ߣ๰ྨ⁁ᘒߩಽᨆ࿑㧞㧚㖸ᖱႎߦࠃࠆ㘩⠌ᘠ࠮ࡦࠪࡦࠣࠪࠬ࠹ࡓ

−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

Emotion Sense Communication for
Web healthcare monitoring service†Rikyo Takahashi,††Hironao Takahashi,

Abstract– At present Japanese society, the old age people is majority now.
The healthcare service is getting high demand to old people to maintain theirlife condition. Web service has interoperable communication from user to user
and user to service provider by present ability. But traditional healthcare
service provides few case of interoperable communication. It also doesn't
support personal emotion sensing too. Therefore, web based healthcare
service level is lower than traditional healthcare service in hospital. The
solution to meet the demand of high quality healthcare, Emotion Sense
Communication for Web healthcare monitoring service is proposed. GSR
value shows each person's emotion real timely and it can analyze how thestatus is changing at present time using sensing historical data withcomparison. To enhance the accuracy level of illness or life status, Ontology
model is designed for Historical data analyzing. Traditional remote healthcare
service utilizes individual data with a large number of people's data forcommon knowledge. But, if person's emotion status adds as one of factor, theaccuracy of decision of disease is much more high than traditional way.
Evaluation GSR sensor value, temperature and heartbeat are shown in thispaper. We evaluated six different types of users a week. Evaluation also
shows that each user has individual GSR value zone day by day. Analyzing
GSR value of each person's historical data, ontology engine using OWL, JINA
API, Protege API, GUI and inference layer is utilized in this paper. The
architecture with analyzing method that is using ontology is also shown. The
accuracy of user preferences result shows more than 90%. The high accuracyof GSR value brings the possibility to sense pre-illness status.

Keywords-

GSR, emotion sense, healthcare service

1. Introduction
At the era of Web 1.0, web service has been proving
single source data as general browsing data fromwebsite system to all the accessing users. At Web
2.0, people can communicate with each other byInternet and can create social community. Social
Networking Sites (SNS) and blogs are applications
that are established by participation of these people
by the manner of interoperable communication [1],[2] & [3]. But, high quality communication between
customers to healthcare operator is achieved byphysical real pharmacy shop and hospital so thattraditional web healthcare service is not enough tocommunicate for each patient. Therefore, if highquality communication method that senses real
†Rikyo Takahashi is Department of Applied informatics,
Faculty of Science and Engineering, HOSEI University Tokyo,
Japan and ††Hironao Takahashi, Khalid Mahmood Malik arewith DTS, Inc 3-39-5 Higashi Ueno Taitou-ku Tokyo Japan.

††Khalid Mahmood Malik

time human emotion is achieved, then it maybe
realized that the service of quality to be muchhigher than physical healthcare. To achieve thehigh quality communication between user tohealthcare service operator, Emotion Sense
Communication for Web healthcare monitoring
service is proposed. To sense each person's emotion,
it uses GSR value to sense it.
It utilizes the historical value of Galvanic Skin
Response (GSR) and other healthcare datameasures and real time measuring data [4]. The
scientific study of GSR began in the early 1900s.
GSR is a method of measuring the electricalconductance of the skin, which varies with its
moisture level. This is of interest because the sweat
glands are controlled by the sympathetic nervous
system [5] so skin conductance is used as anindication of psychological or physiological arousal.
There has been a long history of electro-dermal
activity research [6], most of it dealing withspontaneous fluctuations or reactions to stimuli [7].
GSR value shows the human emotion situation by
the value. The average value of human cell is
500-ohm and skin level is 10 M-ohm to 50M-ohms.
We estimate if value is higher than average ofordinate value of user, it shows much higher
positive emotion and less stress than averagesituation of user. To do this decision make, theservice needs historical database for each user.
Proposing service has own ontology rule engine for
GSR sensing data for each user autonomously.
There are some ways to approach emotional sense. The
capturing face situation analysis cannot evaluate quantitatively
due to different describing emotion level on face by person.
ECG is a limited condition measurement for example, thebed-side terminal in hospital for patient. But proposing service
is required much easier measurement for all people tocommunicate. Therefore, GSR measurement approach is much
more appropriate.

The judgment of emotion sense utilizes real time
sensing value with historical analysis data by
ontology database. Using a quantitative analysis−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

such as Monte Carlo simulation model andstandard deviation from the central limit theorem isrequired a large amount of data to enhance theaccuracy of decision make. Also, human emotion isreflected by various biological data conditions suchas body temperature, heartbeat rate, weekday, timezone, weather, and other environmental conditions.
In view of them, ontology database approach isapplied. Ontology database utilizes as basic data
the history of personal information by generating
ontology attribute of each entity of GSR sense data.
Adopt Ontology technique to determine the feelingsof individuals with high accuracy in the inference
rules, by describing the conditions in Ontology Web
Language (OWL). OWL makes input events evenfrom low self-learning information that are
intended to achieve a high decision making rate ofemotional judgment.
Evaluation shows how users’ emotion is moving
every day. We estimate user's emotion by the valueof GSR every time it makes a decision. To enhance
the high accuracy rate, GSR value is analyzed byontology rule that was designed by us for thisservice with historical data at individual person.
Ontology GSR creates individual user entities andattributes. Evaluation shows higher accuracy rate
in comparison with the traditional approach.
Ontology analyzer achieves more than 90%
accuracy of emotion situation.
The rest of the paper is structured as follows:
Section 1 is introduction. Section 2 discusses therelated works while section 3 describes architectureof Emotion Sensor Communication to Web Virtual
Shop Service for individual user. Section 4 narrates
Ontology rule engine for GSR sensor. Section 5shows the evaluation and Section 6 concludes thispaper.

2. Related Works
In the early 1900s, one of the first references to theuse of GSR instruments in Psychoanalysis is thebook by C. G. Jung entitled Studies in Word
Analysis, published in 1906 [8]. Wilhelm Reich alsostudied GSR in his experiments at the Psychological
Institute at the University of Oslo in 1935-6 toconfirm the existence of a bio-electrical charge
behind his concept of vegetative, pleasurable'streaming' [9]. GSR was used for a variety of typesof research in the 1960s through the late 1970s,
with a decline in use as more sophisticatedtechniques (such as EEG and MRI) replaced it inmany areas of psychological research.
The Galvanic Skin Response (GSR) feedback
instrument measures skin conductivity from thefingers and/or palms. The GSR is highly sensitive toemotions in some people. GSR feedback has beenused in the treatment of excessive sweating(hyperhidrosis)
andrelateddermatologicalconditions, and for relaxing and desensitization
training. GSR was often misunderstood as adifficult technique. GSR has gone through many
phases of interest and rejection since the early
1900's. It has been used in important research on
anxiety and stress levels (Fenz & Epstein, ' 67) andit has been a part of lie detection (Raskin, ' 73).
Controversy has centered on the technique,underlying mechanisms, and the meaning of the
responses obtained from the skin. There has been along history of electro-dermal activity research,most of it dealing with spontaneous fluctuations.
Most investigators accept the phenomenon without
understanding exactly what it means (Hume, ' 76).
Although GSR is the oldest and yet most confusing
term, it is also the one in common use. Many
attempts have been made to improve and update
the terminology. Two such systems are proposed by
the Society for Psycho-Physiological Research(Brown, ' 67), and Venables & Martin ('67). Electro
Dermal Response (EDR) is the umbrella underwhich the terms fall. Basically there are twotechniques in the history of electro-dermal
measurement.
In one a current is passed through the skin and theresistance to passage is measured; in the other nocurrent is used externally and the skin itself is thesource of electrical activity.
GSR Physiology is easily measured and is relatively
reliable. GSR has been used as an index for those
who need some measurable parameter of a person's
internal "state".
As in EEG, there is not a clear understanding ofwhat the measures reflect. Physiology, the GSR
reflects sweat gland activity and changes in thesympathetic nervous system and measurement
variables. Measured from the palm or fingertips,
there are changes in the relative conductance of asmall electrical current between the electrodes. The−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

activity of the sweat glands in response to
sympathetic
nervousstimulation(increased
sympathetic activation) results in an increase in thelevel of conductance. There is a relationshipbetween sympathetic activity and emotional arousal,
although one cannot identify the specific emotion
being elicited. Fear, anger, startle response,orienting response and sexual feelings are allamong the emotions that may produce similar GSR
responses.
The Galvanic Skin Response (GSR) can be used for capturing
the autonomic nerve response as a parameter of the sweat
gland function. Due to relatively simplicity of measurement
and a quite good repeatability, GSR can be considered to be
useful and simple method for examining autonomic nervous
system function, specifically the peripheral sympathetic
system.

Physically GSR is a change in the electricalproperties of the skin in response to different kindsof stimuli. In GSR changes in the voltage measured
from the surface of the skin are recorded. The main
origin of the signal has been suggested to be theactivation of the sweat glands. The most commonlyused stimuli are an electrical shock delivered to a
peripheral nerve or auditory stimuli. However, any
stimulus capable of an arousal effect can evoke theresponse and the amplitude of the response is more
dependent on the surprise effect of the stimulus
than on the physical stimulus strength.

3.

Architecture

The architecture of Emotion Sense Communication
for Web healthcare monitoring service is shown infigure 1.

Figure 1. System Architecture

The service is web based user who was registered tooperator model. First of all, the user accesses thesite of service and touches his hand or body with
GSR sensor. GSR sensor senses user’s electric
resistance and sends it to website. The data from
GSR sensor is stored in to GSR ontology database.
GSR ontology database analyzes what is the status
of user’s GSR sensing value today. It is also
analyzed with historical data from database. ThenGSR ontology rule defined the condition of user by
the value of GSR eventually. Generally, if GSR
value is higher than averaged value, user is in
positive emotion. If the sensed value is smaller than
average value, user may have some psychologicalstress and is in negative emotion. The algorithm ofthis decision make is individual user's historicalentities relationship. Therefore, decision makecriteria are individual value for each user. GSR
ontology is tuned by user’s historical GSR value. It
also adds the value of user’s healthcare data such as
body temperature and heartbeat historical data as
shown in figure 2. Each value has weight value by
the times of frequently cycle.

−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

Figure 2. GSR and healthcare data sense connection

4. Ontology rule engine for GSR sensor

The sampling time of GSR and other healthcare
data from user, the GSR ontology program counts
the value of each data per minute. It is greatly
dependent on the sampling of the data processing
time. In addition less transient effect is also cachedby the data to pass. The value of sensed data wascalculated by the total sum of sense data and its
weight plus threshold value to judge. Basically,function and weight are main parameters. The
following formula is very standard model of thisdecision make.

Xi n 1ºª nf «¦Wi X i n  T i »¼¬i 1

Yi n  1ª nºf «¦W jYi n  T j »¬j 1¼

Zk n  1ª nºf «¦Wk Z k n  T k »¬k 1¼

Function X is GSR value, Y is temperature value and Z isheartbeat value. Each of them is a different aspect and weightvalue. Basically, weight and type of data are not static but
dynamic parameters. This module senses each sampling timeand calculates with historical sensed data.

Ontology rule engine is utilizing intelligent web
application attack protection and network security[10], [11] & [12]. Our ontology engine is designed
referring these existing models.
Ontology rule engine for GSR sensor is shown infigure-4.

Figure 4. GSR sensor ontology rule engine

The decision make of emotion of user is decided
threshold table in figure 3. Temperature has two
steps of value from normal plus/minus 1-degrees
Celsius from weekly average value is warning and2-degrees Celsius is critical. Also plus/minus 20
percent from average heartbeat value is warningand 40 percent from average heart value is criticalsituation. Both of cases are getting higher weightvalue from normal value as shown in figure 3.

The input data are GSR sensor value, human body temperatureand heartbeat value. The ontology rule was written by
Ontology Web Language (OWL) and its architecture iscomposed of four layers as shown in figure 5. Inference layer
does consistency, classification and inference by the result by
the historical data with real time measured data. Rule layer iswritten in JENA [13] and Semantic Web Rule Language(SWRL). This layer does parsing and reasoning for rule. The
ontology layer is composed by OWL API and OWL GUI.
OWL GUI edits correction of data and widget. OWL API does
logic cache for restriction and making of definition. The last
one is conceptualization of domain layer. This layer iscomposed by Protégé API and Protégé GUI. Protégé GUI
makes table, class and widget. Protégé API makes class,properties and individual model. All layers are connected bymain Ontology database [14]. By these layers, the accuracy of
GSR sensor result is enhanced.

Figure 3. Weight and threshold value table−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

Thus, Ontology rule engine studies by the real timemeasured sensor data and add ontology database toadjust more realistic value for each user.
GSR ontology has rule generator. Figure-7 is dynamic rule
generation process. Every rule contains an indicator for
detecting a specific GSR value of the time. Rule based
reasoning is done through Inference Engine by using semantic
rules and ontology model saved in the knowledge base.
Inferred knowledge model given by Inference engine isqueried by the Rule Generator for the generation of detection
rules. Semantic Query is generated by Query builder by using
Rule template. Rules Generator pass the semantic query toinferred knowledge model and populate the rule template.
Rules will be stored in the Rule Cache until unless there issome update in knowledge base. Detection Rules will befetched by the Analyzer from Rule Cache and it uses the Rule
Grammar for parsing the Detection Rules and analyzer
examines the incoming user requests and outgoing responses.

Figure 5. Ontology Design Layered Model

The class of GSR sensing data has properties,
attributes system, policy, consequence, systemcomponent, input, encoding scheme, protocol andport. These classes are further sub-classified andonly important one has been discussed. Class input
describes the interaction of target application withother application, database, RMI or users. This
class having property causing that connects withclass ‘Means’ having subclasses ‘Input Validation
Error’ and ‘Logical Exploit’. The subclass ‘Logical
Exploit’ is further sub-classified into the classes of‘Exception Condition’, ‘Race condition’, ‘Atomicity
Error’ and ‘Serialization Error’.
The Ontology attribute of each entity of GSR sense
data is shown in Figure 6.

Figure 7. Dynamic Rule Generation Process

5. Evaluation
A. GSR sense data test

This section evaluates GSR sense data from individual user.
GSR sensing data is variable from user to user. Therefore,system needs to store individual historical data logs toestimate the average value for each user.

Figure 6. Ontology attribute of each entity of GSR sense data−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

physiological literature, the response is known also as the
Peripheral Autonomic Surface Potential (PASP). Most of the
GSR studies in last decades are concerned with the normal
values of response amplitude and latency. Also the habituationof response amplitudes during repeated stimulations has beenstudied. Response amplitudes vary substantially, depending on
the experimental conditions.

Figure 8.GSR sense data result by multiple users

Figure 8 is GSR sense data result by multiple users. Testing
equipment is GSR sensor (original made) and measurement
tool by MT-4520 digital multiple testers from mother-tool Inc
and Windows XP SP3 OS with testing monitor tool fromtester. A to F users are already registered in this service. The
service measures whenever there is log in by user. The service
stores all sensing data by date and by time. Sensing data isvariable and changes day by day. Therefore, the result of GSR
ontology sense for human emotion is influenced by thesesituations. It also shows user’s sex type and occupation. The
human emotion is very sensitive by personal mind stability. It
also shows daily basis behavior. Forty-three yeas old
businessman is high stress on Monday to Friday and thesevalues are 18.0, 25.1, 21.0, 19.0 and 15.0. But weekend values
are 22.0 and 27.0. These are much more relaxing than
weekday. Fifty years old Women’ GSR result is 22.0, 24.0,
26.0 and 23.0 but weekend is 9.0 and 15.0. In her case, she hassome stress in weekend. Sixty-six years old man’s GSR value
shows much higher than other people. His value is from 41.0
to 51.0. The skin resistance value shows depend on healthcondition and age. Old person shows higher than other by thecondition of health of person in this case. Thirty-seven years
old jobless person’s GSR value is unstable from 7.2 to 19.9.
Therefore, service needs to study individual user’s
characteristic with its data to recognize individual user’s
features.

Figure 9. GSR value for each person

We measured temperature of body and heartbeat
times for each person from A to F as shown in
Figure-10 and 11. Each person has own range ofboth values and we define the emotion impact fromthem. The decision make that we use areplus/minus 1-degrees Celsius from weekly averagevalue is unusual and plus/minus 20 percent fromaverage heartbeat value is unusual. If one of themshows unusual, we determine that person is in highstress situation. Without this, we use very low
weight value to add both values with GSR value.

Figure 9 is GSR value for each person by the result of figure 7.
These results are gained from same testing equipment. People
emotion has been changed by the date but this graph showseach person has different average GSR values [15]. The causes
are heterogeneous but skin condition and emotion stability aremain factors. In history GSR is also known as or closely
related to the Sympathetic Skin Response (SSR) and Skin
Conductance Response (SCR) [16]. In clinical neuron−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）nrt¦ P GSRhist * e

P GSRhisti 1

P GSRreal?
P(total )P GSRreal * C

P GSRhist  P GSRrealnrt¦ P GSRhist * eP GSRreal * C

i 1

Here,
P(GSRhist) = GSR historical sense data accuracy

P(GSRreal) = real time measure sense data of
GSR
Figure 10 Tempareture of body for each person

Figure 11 Heartbeat

B.

Figure-12 is Ontology GSR accuracy vs.
traditional GSR sensing accuracy. The accuracy rateof ontology maintains from 99.17% to 94.49% but
traditional real time measure model is getting low
accuracy. In this case, it drops 65.57% after 35 days.
The reason of this result comes from advantage of
Ontology database. Its rule utilizes historical data
to adjust the average value of GSR for each user forhigh accuracy rate.

value for each person

Ontology engine accuracy level simulation

Evaluation carried out here includes GSR
historical sense data with real time measure GSR
sense data. The traditional real time measure of
GSR sense data in both case are same accuracy as
50%. And historical GSR sense data is adjusted once
a seven on ontology approach but traditional GSR
model doesn’t consider this value. It is always
considering real sensing data only. Therefore,
ontology GSR sensing accuracy result can maintainhigher than traditional real time measure sensing
data model. Accuracy rate is time consuming factor.
In this simulation, we utilize exp (-rt) curve.

Figure 12.

Ontology GSR accuracy vs traditional GSR sensing accuracy

6. Conclusion
The healthcare service is getting high demand to oldpeople to maintain their life condition. Web service isinteroperable communication from user to user and−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

user to service provider by present ability. But
traditional healthcare service provides few case ofinteroperable communication. It also doesn't support
personal emotion sensing too. Therefore, web based
healthcare service level is lower than traditional
healthcare service in hospital. The solution to meetthe demand of high quality healthcare, Emotion Sense
Communication for Web healthcare monitoring service
is proposed. The architecture of the proposal approach
was shown. The evaluation of GSR value was also
shown at evaluation section. The method to analyze
the human emotion is utilizing GSR value andtemperature and heartbeat value. The human emotion
is very sensitive by personal mind stability. We
measured GSR value of each person from A to F aweek. It also measured daily base behavior.
Forty-three yeas old Businessman is high stress on
Monday to Friday and these values are 18.0, 25.1, 21.0,
19.0 and 15.0. But weekend values are 22.0 and 27.0.
These are much more relaxing than weekday. Fifty
years old Women' GSR result is 22.0, 24.0, 26.0 and
23.0 but weekend is 9.0 and 15.0. In her case, she hassome stress in weekend. Sixty-six years old man's GSR
value shows much higher than other people. His value
is from 41.0 to 51.0. The skin resistance value shows
depend on health condition and age. Old person showshigher than other by the condition of health of personin this case. Thirty-seven years old jobless person's
GSR value is unstable from 7.2 to 19.9. Therefore,
service needs to study individual user's characteristicwith its data to recognize individual user's features.
We also measured temperature and heartbeat value
from A to F to consider decision making of emotion
level. The weight value of temperature and heartbeat
is lower than GSR value. But when the value exceeds
a threshold, it becomes a heavy weight value. To help
decision make from the emotion, we designed GSR
ontology rule engine to infer the emotion for individualuser. Analyzing GSR value of each person's historical
data, ontology engine using OWL, JINA API, Protege
API, GUI and inference layer is utilized in this paper.
This approach shows high accuracy rate than
traditional GSR data mining. The accuracy rate of
ontology maintains from 99.17% to 94.49% but
traditional real time measure model is getting low
accuracy. In this case, it drops 65.57% after 35 days.
Thus, historical data of GSR value by ontology
analyzing shows each person's emotion situation withhigh accuracy. The conclusion of this approach shows
the potential to detect the before illness status aspre-disease, near future.

specific relationship between GSR with humanhealthcare data with a long-term period withheterogeneous type of person.

㩷

REFERENCES
Hironao Takahashi, Hafiz Farooq Ahmad, Kinji Mori, "Layered
Memory Architecture for High IO Intensive Information
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Nanjing, China, pp. 343-349.
[2] Hironao Takahashi, Hafiz Farooq Ahmad, Kinji Mori,"Balanced Memory Architecture for High I/O Intensive
Information Services for Autonomous Decentralized System",
The 9th International Symposium on Autonomous Decentralized
Systems (ISADS 2009), Athens, Greece, March 23-25, 2009[3] Kinji Mori, Akio Shiibashi, “Trend of Autonomous Decntralized
System Technologies and Their Application in IC Card Ticket
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BIOFEEDBACK Methods and Procedures in clinical practice ( 1977)
written by George D Fuller, Ph.D.

Reich, W. 'Experimentelle Ergebnisse ueber die electrische
Funktion von Sexualitat und Angst' (Sexpolverlag, Copenhagen,1937). Translated as 'Experimental investigation of the electricalfunction of sexuality and anxiety' in J. of Orgonomy, Vol. 3, No.
1-2, 1969.
[6] Conesa, J. (1995). Electrodermal palmar asymmetry and nostril
dominance. Perceptual and Motor Skills, 80, 211-216[7] Montagu, J. D.; Coles, E. M. Psychological Bulletin, Vol 65(5),May 1966, 261-279.Mechanism and measurement of thegalvanic skin response.
[5]

[8]

Cornell GSR class project: Electrodermal activity meterhttps://instruct1.cit.cornell.edu/courses/ee476/FinalProjects/s2006/hmm3
2_pjw32/index.html browsed at June 3rd 2012/06/03[9] electrocnic
dyagnostic
devise:http://www.quackwatch.org/01QuackeryRelatedTopics/electro.html
browsed at 2012-06-03[10] Stefan Fenz and Edgar Weippl “Information Security Forti¿cation by
Ontological Mapping of the ISO/IEC 27001 Standard” 2008.
[11] Almut Herzog, Nahid Shahmehri, Claudiu Duma, “An Ontology ofInformation Security” International Journal of Information Security andPrivacy, Volume 1, Issue 4, 2007.
[12] Steve Pettit, Sanctum Inc. “ANATOMY OF A WEB APPLICATION:
Security Considerations” White Paper July, 2001[13] Jena – A Semantic Web Framework for Java, http://jena.sourceforge.net/[14] Gustavo Isaza, Andrés Castillo, Manuel López, Luis Castillo Towards
Ontology-Based Intelligent Model for Intrusion Detection andPrevention
Journal of Information Assurance and Security, 376-383,5(2010)[15] GSR http://it.uku.fi/biosignal/research/gsr.shtml browsed at 2012-06-03[16] Makoto Sato, Chie Morita and Miwako Tdoi, Human
actionrecognitionusingphysiologicaland acceleration data ಳ
TOSHIBA Corp. Research & Development Center IPSJ No65
conference 3T5B-2 page 239 to 242.

Our next step or our research is investigation of the−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

Rikyo Takahashi He researches computer
security algorithm and cloud computer
technology at Department of Applied
informatics, Faculty of Science andEngineering, HOSEI University and joins
University of Tokyo interfaculty initiative
information studies department from 2013.
Also he is researching ontology dataclassification at DTS, Inc as a internshipresearcher.
Hironao Takahashi received PhD degree in
Computer Science in 2010 from Tokyo
Institute of Technology and received the MS
degree in MOT in 2006 Tokyo University of
Science. His research area is High speed I/OsystemarchitectureonAutonomous
Decentralized System. He invented Data
Transmission System technology architecture
and is holding nine patents of this field. He ismember of IEEE, IEICE, IEE and IPSJ.
Khalid Mahmood Malik received PhD
degree in Computer Science in 2010 from
Tokyo Institute of Technology He is
researching Ontology security, Ontology URL
classification and semantic community
creation. He also researches Autonomous
Decentralized Mobile Network system, Data
Transmission System architecture at DTS,
Inc. IEEE member.

−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）

ࠝ࡯࠻࠲ࠠࠪࡦࡁ࠶ࠢ࠳࠙ࡦߦࠃࠆ
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Gabor TIGYI3, ቶફ߈ߺሶ 2*


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3 㩷University of Tennessee, Department of Physiology

894 Union Avenue Memphis, TN 38163, USA e-mail: gotoh.mari@ocha.ac.jp, murofushi.kimiko@ocha.ac.jp
Keywords: ߇ࠎ⚦⢩, ᶐẢ, ォ⒖, ࠝ࡯࠻࠲ࠠࠪࡦ,࡝࠱ࡎࠬࡈࠔ࠴ࠫࡦ㉄ⷐᣦ࡝࠱ࡎࠬࡈࠔ࠴ࠫࡦ㉄ (LPA)ߪ‫⚦ࠎ߇ޔ‬⢩ߩ↢ሽ‫ޔ‬Ⴧᱺ‫ޔ‬ᶐẢ࡮ォ⒖ߥ
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ࠦ࡝ࡦ߆ࠄ LPA ࠍ↥↢ߔࠆ㉂⚛ߣߒߡࠝ࡯࠻࠲ࠠࠪࡦ(ATX)߇หቯߐࠇߚ‫ޕ‬ߎࠇ߹ߢߦ‫ޔ‬ATX ߇߇ࠎ⚦⢩ߩᶐẢ࡮ォ⒖ߦᷓߊ㑐ਈߔࠆߎߣ߿‫ޔ‬ᖡᕈᐲߩ㜞޿߇ࠎ⚦⢩ߢߪ ATX ࠍ㜞⊒⃻ߒߡ޿ࠆߎߣߥߤ߇ႎ๔ߐࠇߡ޿ࠆߎߣ
߆ࠄ‫ޔ‬ATX ߩᵴᕈࠍ೙ᓮߔࠆߎߣߦࠃࠅ‫ࠎ߇ޔ‬ᴦ≮߇น⢻ߦߥࠆߎߣ߇ᦼ
ᓙߐࠇࠆ‫⎇ᧄޕ‬ⓥߢߪ‫ޔ‬ATX ࡁ࠶ࠢ࠳࠙ࡦ⚦⢩ᩣࠍ૞⵾ߒ‫ߩࠎ߇ޔ‬ᖡᕈᐲߩᜰᮡߣߥࠆ‫⚦ࠎ߇ޔ‬⢩ߩჇᱺ‫ޔ‬ᶐẢ࡮ォ⒖⢻ߦߟ޿ߡ⺞ߴࠆߎߣࠍ⋡⊛ߣߒ‫ޔ‬ᬌ⸛ࠍⴕߞߚ‫ޕ‬੹ᓟߩዷᦸᧄ⎇ⓥߦ߅޿ߡ‫ޔ‬ATX ߩ⊒⃻ࠍᛥ೙ߒߚ߇ࠎ⚦⢩ߢߪ‫ޔ‬ᶐẢ࡮ォ⒖߇᦭ᗧߦᛥ೙ߐ
ࠇࠆߎߣ߇ in vitro‫ޔ‬in vivo ߩታ㛎߆ࠄ᣿ࠄ߆ߦߥߞߚ‫⚿ߩߎޕ‬ᨐߪ‫ߦࠎ߇ޔ‬ኻߔࠆᣂ
ⷙᴦ≮ᴺߣߒߡ‫ޔ‬ATX 㒖ኂ೷߇㜞޿ലᨐࠍᜬߟน⢻ᕈࠍ␜ໂߔࠆ‫ޕ‬ᚒ‫ޔߪޘ‬Ⅳ⁁ࡎࠬࡈࠔ࠴ࠫࡦ㉄(cPA)߇ ATX ߩᵴᕈࠍ㒖ኂߒ‫⚦ࠎ߇ޔ‬⢩ߩᶐẢ࡮ォ⒖ࠍᛥ೙ߔࠆ⚿ᨐࠍᓧߡ޿ࠆ‫੹ޕ‬ᓟ‫⚦ࠎ߇ޔ‬⢩ߩᶐẢ߅ࠃ߮ォ⒖ࠍᛥ೙ߔࠆߎߣߢ‫ࠎ߇ޔ‬ᴦ≮ߩലᨐࠍ㜞߼ࠆᣂⷙߩ೙߇ࠎ೷ߣߒߡ‫ޔ‬cPA ࠍᔕ↪࡮㐿⊒ߒߡ޿ߊߎߣࠍ⸘↹ߒߡ޿ࠆ‫ޕ‬−  −

第12回人間情報学会講演会ポスターセッション（2012.9.11）−  −

ᇹ ᵏᵐ ‫ׅ‬ᴾᇹ ᵏᵐʴ᧓ऴ‫˟ܖإ‬ἯἋἑὊႆᘙᨼᴾ‫ׅ‬ᴾ ʴ᧓ऴ‫˟ܖإ‬ἯἋἑὊႆᘙᨼᴾ
ᴾ
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