人間情報学会

Academy of Human Informatics

第 15 回 人 間 情 報 学 会 ポ ス タ ー 発 表 集

2013 年 9 月 19 日

東京大学山上会館

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第15回人間情報学会講演会ポスターセッション（2013.9.19）

Modulation of Central Fatigue is Dependent on
Sound Environment
Yi SHI1 Yoshitada KATAGIRI2

Zhi-Wei LUO1

1 Graduate School of System Informatics, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe-shi, Hyogo, Japan
2 Center for Information and Neural Networks, National Institute of Information and Communications Technology 588-2,
Iwaoka, Nishi-ku, Kobe, Hyogo, Japan

Abstract
Introduction: Activation of brain in living environment where elders spend much of their time in daily life leads tonursing care prevention. Different environments would modulate the central fatigue. Meanwhile, previous studies
suggest that music can suppress central fatigue by activating the reward system accompanied with dopaminergic
pathways. We here neurophysiologically investigated whether this hypothesis would be feasible.
Method: 2 kinds of experiments were carried out in this research and both of them used continuous performance
test (CPT) included 3 types of tasks. In the first experiment, heart rate variability (HRV) was also measured. In
the second experiment, throughout the background music presentations which were including pleasant andunpleasant music chosen by the subjects, we measured the EEG, Near-infrared hemoencephalogram and galvanic
skin conductance. Before and after the CPT tasks, salivary amylase was also measured to evaluate the stress levelchanges.
Results: In Experiment 1, it showed that the subjects gave the shortest response time under the voice environment,
and LF/HF in the quiet environment showed the lowest which means in this environment, the participants
suffered the smallest stress.
In Experiment 2, throughout various back-ground music presentations, we found that higher bilateral fundamental
brain activities (FBAs) correlated with higher work scores. The condition of providing higher FBA was entirely
dependent on music preference of subjects. Music belonged to a specific category (jazz in this experiment) was
more effective than the pleasant music according to subject’s choice.
Keyword: living environment CPT HRV background music NIRS EEG

elements including harmony, rhythm or
melody [4]. Previous studies havereported that music can activate thereward-related brain network systems,
while the most favorite music could
increase the content of dopamine [5].
Before proving that whether the most
preferable music would maintain the
cognitive performances, it is also very
important to evaluate the activation ofbrain functions in the proper environment.
Therefore, in this research, 2 experiments
were included to prove the hypothesis.

ĉIntroduction
Nowadays, superfluous stress is a serious
problem in our society. It leads to plentyof health disorders and one of them iscauses the central fatigue. From theprevious researches, it has already been
known that central fatigue caused by thestress causes the decrement serotoninwhich is related to dopamine [1,2].
Meanwhile, it also has other reasonscause central fatigue such as exercises
which because the decrement of the
dopamine [3]. Listening to music is acomplex cognitive task and implicatesprocessing and integration of meaningful−−第15回人間情報学会講演会ポスターセッション（2013.9.19）

Ċ. Method
1. Experiment 1
10 healthy participants (6 males, 4
females) aged from 23 to 38 years old(mean 30 years old, SD= 4 years old) had
joined for our study, and none of themhad previous records of serious cardiac
diseases.
3 environments had been prepared in thisexperiments which including quiet
environment, voice environment whichmeans human voices are in the
background and noise environment
The experiment was carried on between 3
to 5 o’clock in the afternoon which was 2hours after the lunch.
A simple Single Response Time (SPT)
scheme which included in CPT
examination was carried out in thisexperiment.
A wearable EEG sensor (Union Tool. Co.)
operated to monitor heart beats fornumerically evaluating the autonomic
nerve activities which were derived froma series of measured heartbeat intervals.

directly before the CPT test. The music
was chosen by the subjects. After the CPT
test which lasted 17 minutes, the subjects
did the salivary amylase measurement
again then had a 5 minutes rest.

Fig.1

Protocolforinvestigating

cognitive

performance by the different background music

ċ. Results
1. Experiment 1

2. Experiment 2
7 healthy subjects (6 females, 1 male)whose average age is 26, (SD =3)
participated in this pilot experiment. The
participants had no previous record ofboth mental disorders.
An experimental procedure was designed
about 30 minutes once a time (Fig.1).
EEG cap which including 16 channels,
Near-infrared
spectroscopic
hemoencephalogram(NIRS-HEG),
galvanic skin conductance (SC) (NeXus)
were set up and measured during theentire experiments. The backgroundmusic which including unpleasant,pleasant music and Jazz started and thesubjects did a salivary amylase
measurement (salivary amylase monitor)


Table 1 The raw data of the response time underthe 3 environments.

From the data above, we can obviously
find that most of the subjects gave us theshortest response time was in the voiceenvironment (in the laboratory).
According to the whole data, LF/HF in
the quiet environment showed the lowest
which means in this environment, theparticipants suffered the smallest stress.
Furthermore, LF/HF became larger as thechanges among quiet, voice and noisy
environment.

−−第15回人間情報学会講演会ポスターセッション（2013.9.19）

One of the subjects did the CPT task
under 3 kinds of music which including
unpleasant, pleasant music and Jazz andthe results showed as follows (Fig.3): itshowed that under the unpleasant music,
the right part of the occipital cortex (O2)
was much more active compared with theleft part (O1) and under the pleasantmusic, the right part was a little bit active
while under the Jazz, both left and rightwere activate.

2. Experiment 2
Because the differently burdened stressbetween doing and without doing tasks
when listening to the music, firstly, thesubjects listened to the music only for theneurophysiological responses. Results of
GSR and HEG were showed as follows(Fig.2).
Although both increment and decrement
were appeared during the unpleasant andpleasant music, it showed that under theunpleasant music, GSR changed larger
than under the pleasant music.
The figure showed that during theunpleasant music, the HEG was
decreasing while during the unpleasantmusic, it was increasing.
The results of CPT task (Table 2) showedthat under the pleasant music, theresponse time was shorter than under theunpleasant music and got the statisticallysignificant difference (p=0.009).
The average value of SA showed that
under the unpleasant music, it was larger(1.014) than under the pleasant music
condition (1.0089) but it was not getting
the statistically significant difference.
However, we confirmed the results
indicated that pleasant music could
reduce the scores of SA during the CPT
task.

Table 2 The results of CPT task and salivary
amylaseFig.3 The changes of HEG and FBA under the
background musicČ. Discussion
In this study, we identified the modulationof central fatigue under different
background music. From the result of
CPT task, we approved that the mostfavorite music could help the subjects
maintained cognitive performance.
Under the unpleasant music, HEG wasincreased because the unpleasant musicFig.2 Neurophysiological responses to different
background music−−第15回人間情報学会講演会ポスターセッション（2013.9.19）

led to the decrement of serotonin which
made the subjects feel painful while
under the pleasant music, the increase of
the content of dopamine could control thecentral fatigue.
CPT scores were maintaining the brain
functions and during the sustained
attention task, the largest cluster ofactivation was found in the frontal lobe
and, in particular, in the anterior
cingulated cortex (ACC). The ACC playsa central role in attention processing by
modulating target selection, motor
response selection, error detection, andperformance monitoring [6-9]. From theresult, only under the Jazz, the activation
both appeared in the left and rightoccipital cortex, otherwise, only the rightpart showed the activation during the task
[10]. O1, O2 bilateral activity for Jazz
maintain the CPT high scores are still
unknown and we will be necessary foraddressing this issue in the future study.
The reason why salivary amylase
decreased should be considered that whensubjects did the CPT task which was a
cognitive behavior, it needed the
dopamine to control the central fatigue,on the other hand, listening to the music
also a behavior which needed the
dopamine, which finally led thedecrement of the serotonin and madedecrement of salivary amylase.

Reference[1] M.W. Bridge, A.S. Weller, M. Rayson, D.A.
Jones, “Responses to exercise in the heat related
to measures of hypothalamic serotonergic anddopaminergic function”, Eur J Appl Physiol, 89,
pp.451-459, 2003.
[2] E.L. Bliss, J.Ailion, “Relationship of stressand activity to brain dopamine and homovanillicacid”, Life Sciences, volume 10, Issue 20, Part 1,
pp.1161-1169, 1971.
[3] S.Yajun, “Exercise, Dopamine and Central
Nervous System Fatigue”, Hournal of Jining
University, pp.1004-1877,2007.
[4] S. VJ, “Separate cortical networks involvedin music perception: preliminary functional MRI
evidence for modularity of music processing”,
Neurolmage 25, pp. 444-451, 2005.
[5] V.N Salimpoor, M.Benovoy, K.Larcher, A.
Dagher, R.J Zatorre, “Anatomically distinct
dopaminereleaseduringanticipation

andexperience of peak emotion to music”, natureneuroscience, doi:10.1038/22.2726, 2011.
[6]M. G. Tana, E.Montin, S. Cerutti, A.M.
Bianchi, “Exploring Cortical Attentional System
by Using fMRI during a Continuous PerfomanceTest”,

Computational

IntelligenceandNeuroscience, Article ID 329213, 6 pages, 2010.
[7] M. I. Posner, S. E. Petersen, P. T. Fox, and M.
E. Raichle, “Localization of cognitive operationsin the human brain,” Science, vol. 240, no. 4859,
pp. 1627–1631, 1988.
[8] R. D. Badgaiyan and M. I. Posner, “Mapping
the cingulated cortex in response selection andmonitoring,” NeuroImage, vol. 7, no. 3, pp.

Ϭ. Conclusion
The data from this experiment indicatedthat under the pleasant music, theresponse time according to the CPT task
could be considered to maintain the
cognitive performance. And under aspecial music (Jazz in this experiment)
could activate the brain functions.

255–260, 1998.
[9]

W.

J.

Gehring“Prefrontal-cingulate

and

R.

T.

Knight,interactions

inactionmonitoring,” Nature Neuroscience, vol. 3, no. 5,
pp. 516–520, 2000.
[10] R.I. Goldman, J.M. Stern, J.Engel Jr, and
M.S. Cohen, “Simultaneous EEG and fMRI of
the alpha rhythm”, Neuroreport, 13(18), pp.
2487-2492, 2002.

−−第15回人間情報学会講演会ポスターセッション（2013.9.19）

Neurophysiological Study of Taste Preference Mechanism
Chenghong HU1, Yoshitada KATAGIRI2, Yoshiko KATO3, Zhiwei LUO1
1

Graduate School of System Informatics, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe-shi, Hyogo, 6578501 Japan

2

Center for Information and Neural Networks, National Institute of Information and Communications Technology588-2, Iwaoka, Nishi-ku,

Kobe, Hyogo 6512492, Japan
3
Graduate School of Human Development and Environment, Kobe University, 3-11, Tsurukabuto, Nada-ku, Hyogo, 6578501, Japan

Abstract:
The aim of this study was to investigate neurophysiologic responses to pleasant and aversive tastants using
electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) to clarify the intrinsic food preference
mechanism. The pleasant stimulus used was sweet taste and unpleasant stimuli were sour and bitter taste. We found the earlymarkers before P3 were related to sour and bitter taste and considered they would be unconscious electrophysiological earlymarkers for attention. And the prefrontal cortex was involved the sweet and sour taste cognitive processing.
Keywords: electroencephalogram, functional near-infrared spectroscopy, taste preference, pleasant taste, aversive taste.

Introduction
Recently, the prevalence of taste diseases which caused byaging society and the changes of the diet structure, such as
anorexia nervosa, dyspepsia and taste disorder, appears to
be increasing[1]. Although these diseases are notlife-threatening diseases directly, they have a strong impact
on our quality of life. For this reason, the interest inunderstanding gustatory processing is growing, not only
for basic science, but also for applications.
With the rapid developments of electrophysiology or
neuroimaging techniques, many insights have been gained
about the sensory function of brain. So far, someresearchers have done a lot of works about gustatory
sensation and some knowledge of taste cognitive
processing has been accumulated. But compared with someother sensory such as olfaction, vision and audition,
relatively little is known of how the human brain process
taste. And the results are also on disputed.
In present study, we investigated the neurophysiologicalresponses to pleasant and aversive tastants, using
electroencephalogram (EEG) and functional near-infrared
spectroscopy (fNIRS) to clarify the intrinsic foodpreference mechanism.

Materials and methods
Different taste-related brain function experiments werecarried out as part of present study.
Experiment 1 measurements were made for a group of
five healthy subjects, aged from 26 to 28 years, using a 21
channels electrode cap and NIR-HEG simultaneously. We
selected the sour solution (1% citricacid) and bitter
solution (0.01% caffeine) as tastants. Meanwhile, purified
water was selected as a control condition to minimize theartificial noise from somatosensory effects, swallowing
effects and other brain function. For the EEG and
NIR-HEG recording session, the subjects were seated in a
comfortable chair in a quiet room. A conventional delayed
response task paradigm was adopted for detecting
neurological signs responding to gustatory stimuli. The
experiments begun with a click, a warning signal, and 2
seconds later we used another click as imperative signal toorder the experimenter inject the stimulus. Each stimulus
was presented 30 times repeatedly and continuously. The
ERPs were recorded using NeXus-32 and Bio Trace+
Software. In each condition (purified water, sour solution
and bitter solution), the EEG was recorded for 2.5 min, andsigns (5s) were extracted as an average value of 30 times.
Data was filtered offline between 0.05Hz and 45Hz. The
intensity of the tastants was rated by the subjects as a
subjective evaluation on the dislike-like scale after the
EEG session.
Experiment 2 To investigate more information about
higher cognitive processing of taste, an fNIRS sensor of
OEG-16 was used to monitor the prefrontal cortex
activation during taste processing among 16 healthy
volunteers (Fig1). Sweetened and acidic solution was
selected as pleasant and aversive stimuli. The subjects wereseated in a comfortable armchair in a quiet room. During
the experiment, the subjects were asked to try to relax for
40 seconds. Following this, purified water, in quantities of
8 ml, was manually injected into the subject’s mouth via ahand-held syringe connected to a tube. The subject held thestimulus in the mouth for 25 seconds. After the task, thesubject rinsed off his/her mouth with purified water. Thenthey continued to run the same process with other two
stimuli, sweetened and acidic solution. Oxygenatedhemoglobin, deoxygenated hemoglobin, and total

Fig.1 OEG-16 channels arrangement. The channels covered
inferior frontal gyrus, middle fontal gyrus, superior frontal
gyrus and the lateral orbitofrontal gyrus.

−−第15回人間情報学会講演会ポスターセッション（2013.9.19）

Fig.2 ERPs to taste stimuli. A represents the evoked potential response to water, B for bitter taste and C for sour taste.
hemoglobin FRQFHQWUDWLRQFKDQJHVDVǻR[\+EǻGHR[\+E
DQG ǻWRWDO+E were calculated from the output signals. In
this case, the ǻR[\+E data was select for the following
statistical processing.

Results and Discussion
Experiment 1 Fig 2 showed the ERP component to taste
stimulus. The results showed the main or interaction effects
were observed for the P1, N1, N2 and P2 on the bitter andsour condition, which are not found on the control
condition clearly. Therefore, we suggested that thesecomponents we found would be only attributed to sour andbitter tastes. As these potentials appeared before P3, weconsidered they would be unconscious electrophysiologicalearly markers for attention. The early sensory ERP
deflections to taste found in present study were inagreement with previous researches, which reported P1,P2
and N2 were observed for salt, glucose and electric taste[2,3].
We also identified a contingent negative variation (CNV)
component in a late stage for the sour (Fig2 C). Relevant topresent study, the late CNV was suggested to be related tothe prejudice for sourness, which matched the subjects’
self-reported sourness made them feel more unpleasant [4].
Experiment 2 with the help of analysis of variance, we
observed different lateral prefrontal cortex areas acrossdifferent tasks. the group analysis showed the ROI(tasteless-sweetness) included the regions of one part of thefrontopolar area, orbitofrontal area and dorsolateralprefrontal cortex, while the ROI (tasteless-sourness)overlapped the area activated by sweetness (ch3, 4, 6 and
11) and also showed some another foci (ch5, 9 ,10 and 13).
Thus, we conclude that, in some sense, these regions in theprefrontal cortex of human brain are involved in theperception of sweetness and sourness in taste system.
Another novel finding of this study is that the difference
between sweet and acidic taste appeared in parts of thedorsolateral prefrontal cortex in the right hemisphere (ch3and 4), frontopolar area in the left hemisphere (ch4) andthe orbitofrontal area in bilateral hemisphere (ch6 and 11).
This provides new evidence to support the issue thathuman prefrontal area is involved in the discrimination of
taste qualities.
Up to now, the issue of cortex localization of taste
cognitive processing is still on dispute. While manyneuroimaging studies have shown the right prefrontal
cortex was involved in perception and recognition ofgustatory stimuli [5, 6], some studies have shown bilateralor left areas were activation [7]. Results found in thisexperiment are similar to some previous studies suggesting
the bilateral prefrontal cortex is activated in taste system.
However, this study has some limitations, and moreevidence is required to clarify this issue. A future NIRS
study may overcome the influences induced by taste
quality, persons’ physical self to further assesse questions
about taste cortical activation on human brain.

Conclusion
Taken together, we reported the EEG and fNIRS study toinvestigate taste preference using pleasant and aversive
stimuli. The early markers before P3 were related to sourand bitter taste and we considered they would be
unconscious electrophysiological early markers forattention. And the prefrontal cortex was involved thesweet and sour taste high cognitive processing. In future,
we will extend such investigation to more participants
having various food preferences.
Reference
1. Parrott, M.D., Greenwood, C.E. “Dietary influenceson cognitive functionwith aging: from high-fat diets tohealthful eating”, Ann N Y AcadSci 1114, 389–97,
2007.
2. Wada, M. “ Evoked responses to taste stimulation”, Int
Tinnitus J 11 (1), 43–47, 2005.
3. Ohla, K., Toepel, U., le Coutre, J., Hudry, J.
“Electrical neuroimaging reveals intensity-dependent
activation of human cortical gustatory andsomatosensory areas by electric taste”, Biol Psychol
85(3), 446-455, 2010.
4. Chiu, P., Ambaby, N., Deldin, P. “Contingent negative
variation to emotional in- and out-group stimuli
differentiated high- and low-prejudiced individuals”, J
Cogn Neurosci 16 (10), 1830-1839, 2004.
5. Barry, M.A., Gatenby, J.C., Zeiger, J.D., Gore, J.C.
“Hemispheric dominance of cortical activity evokedby focal electrogustatory stimuli”, Chem.Senses 26(5),471– 482, 2001.
6. Zatorre, R.J., Jones-Gotman, M. “Functional imagingof the chemical senses”, In: Toga , A.W., Mazzoiotta,
J.C. (Eds.), Brain Mapping: The Systems. Academic
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7. Pritchard, T.C., Macaluso, D.A., Eslinger, P.J. “Taste
perception in patients with insular cortex lesions”,
Behav. Neurosci 113 (4), 663– 671,1999.

−−第15回人間情報学会講演会ポスターセッション（2013.9.19）

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1 ୰ኸ኱Ꮫ⌮ᕤᏛ㒊 ࠛ112-8551 ᮾி㒔ᩥி༊᫓᪥ 1 ୎┠ í
2 㟼ᒸ኱ᏛᕤᏛ㒊 ࠛ432-8561 㟼ᒸ┴὾ᯇᕷ୰༊ᇛ໭ 3-5-1
3 ᮾ୰㔝࡜ࡳ࡝ࡇࢁ║⛉ ࠛ164-0003 ᮾி㒔୰㔝༊ᮾ୰㔝 5-1-1 ࣘࢽࢰ࣮ࣥࣔࣝ 3F㏆ᖺࠊ⥳ෆ㞀࡟ᑐࡋ࡚⥙⭷⚄⤒⥺⥔ᒙཌࡢ࣮࣐࢝ࣛࢵࣉࢆ⏝࠸ࡓࢥࣥࣆ࣮ࣗࢱᨭ᥼デ᩿(CAD)ࡀ୍⯡໬ࡋ࡚࠸ࡿࠋࡋ࠿ࡋࠊ⑓≧ࡀึᮇࡢሙྜࠊ࣮࣐࢝ࣛࢵࣉ࡛ࡢุ᩿ࡣᅔ㞴࡛࠶ࡿࠋࡑࡇ࡛ᮏ◊✲࡛ࡣཌࡳ࡟ຍ࠼᪂ࡓ࡞ホ౯್ࢆᥦ᱌ࡍࡿࡇ࡜࡛⥳ෆ㞀⑌ᝈ㡿ᇦࡢ᳨ฟᛶ⬟ࡢྥୖࢆ┠ᣦࡍࠋ⑌ᝈ㡿ᇦࢆ᳨ฟࡍࡿࡓࡵ࡟ࠊ⥙⭷⚄⤒⥺⥔ᒙࡢཌࡳࠊ㠀ᑐ⛠ᛶࠊᕪศࠊࡤࡽࡘࡁࢆ⾲ࡍᅄࡘࡢホ౯್ࢆྲྀᚓࡍࡿࠋホ౯್ࢆྲྀᚓࡋࡓᚋࠊࢫ࢟ࣕࣥ⠊ᅖࢆศ๭ࡋࠊศ๭ࡋࡓ㡿ᇦࡈ࡜࡛ホ౯್ࡢᖹᆒ್ࢆ⟬ฟࡍࡿࠋྛศ๭㡿ᇦ࡟ᑐࡋ࡚ࠕṇᖖࠖࠊࠕ⥳ෆ㞀ࠖࢡࣛࢫࢆసᡂࡋࠊ࣐ࣁࣛࣀࣅࢫ㊥㞳ἲࠊ᭱ᑬἲࠊ᭱▷㊥㞳ἲࠊࢧ࣏࣮ࢺ࣋ࢡࢱ࣮࣐
ࢩࣥࡢ㸲✀㢮ࡢศ㢮࢔ࣝࢦࣜࢬ࣒࡛⑌ᝈ㡿ᇦ໬࠿ྰ࠿ࢆุูࡍࡿࠋ᳨ฟᛶ⬟ࢆឤᗘ(Recall)ࠊ⢭ᗘ(Precision)ࠊṇ⟅⋡(Accuracy)ࡢほⅬ࠿ࡽ᳨ドࡍࡿࠋ⥳ෆ㞀ࡢ⑕౛࡟ᑐࡋ࡚ឤᗘ࡛ࡣཌࡳࢆ⏝࠸ࡓ࣐ࣁࣛࣀࣅࢫ㊥㞳ἲ࡛ࡢ᳨ฟࡀ .5%࡜୍␒㧗࠸⤖ᯝ࡛࠶ࡗࡓࠋホ౯್ࡈ࡜࡟⤖ᯝࢆࡳࡿ࡜ࠊAve_thick ࡢឤᗘࡀྛᡭἲࡢ୰࡛ࡣ㧗࠸⤖ᯝ࡛࠶ࡗࡓࠋ୍᪉⢭ᗘ࡛ࡣ Ave_asym ࢆ⏝࠸ࡓሙྜࡀྛᡭἲ࡛㧗࠸⤖ᯝ࡛࠶ࡗࡓࠋḟ࡟ṇᖖࡢ⤖ᯝࢆࡳࡿ࡜ࠊṇ⟅⋡ࡀ 99%௨ୖࡢ⤖ᯝࢆ」ᩘࡢ᳨ฟᡭἲ࡛ᚓ
ࡿࡇ࡜ࡀฟ᮶ࡓࠋ
Ave_thick ࡢឤᗘࡀྛᡭἲࡢ୰࡛㧗࠸⤖ᯝࢆ♧ࡋࡓ⌮⏤࡜ࡋ࡚ࠊ௒ᅇᐇ㦂࡟⏝࠸ࡓ 14 ⑕౛඲࡚ࡢ⑓≧ࡀ࠶ࡿ⛬ᗘ㐍⾜ࡋ࡚࠾ࡾࠊ⥙⭷⚄⤒⥺⥔ᒙࡢῶᑡࡀࡣࡗࡁࡾࡋ࡚࠸ࡓࡓࡵࡔ࡜⪃࠼ࡽࢀࡿࠋࡲࡓࠊAve_asym ࡢ⢭ᗘࡀྛᡭἲ
ࡢ୰࡛㧗࠸⤖ᯝࢆ♧ࡋࡓ⌮⏤ࡣࠊᑐ⛠ᛶࢆࡳࡿࡇ࡜࡛⥳ෆ㞀ࡢ⑕≧ࡢ≉ᚩࢆ࡜ࡽ࠼ࡿࡇ࡜ࡀฟ᮶࡚࠸ࡓࡓࡵࡔ࡜
⪃࠼ࡽࢀࡿࠋ
ᮏ◊✲࡛ࡣ⥙⭷⚄⤒⥺⥔ᒙࡢཌࡳࠊ㠀ᑐ⛠ᛶ࡟ຍ࠼࡚ᕪศࠊࡤࡽࡘࡁࢆホ౯್࡜ࡋ࡚ྲྀᚓࡋࠊ␗࡞ࡿศ㢮࢔ࣝࢦࣜࢬ࣒ࢆ⏝࠸࡚⥳ෆ㞀⑌ᝈ㡿ᇦࡢ᳨ฟᡭἲࢆᥦ᱌ࡋࡓࠋ᳨ฟ⤖ᯝ࠿ࡽ」ᩘࡢ᳨ฟᡭἲ࡛ࡢ᫂☜࡞ඃຎࡢᕪࡣ⌧ࢀ࡞࠿ࡗࡓࠋṇᖖ࡟ᑐࡍࡿ⤖ᯝ࡛ࡣ㧗࠸ṇ⟅⋡ࢆᚓࡿࡇ࡜ࡀฟ᮶ࡓࠋ୍᪉ࠊ⥳ෆ㞀࡟ᑐࡍࡿឤᗘࠊ⢭ᗘࡣᐇ㝿ࡢ་⒪ࡢ⌧ሙ࡟⏝࠸ࡽࢀࡿ⛬ࡢ⤖ᯝࢆᚓࡿࡇ࡜ࡣ࡛ࡁ࡞࠿ࡗࡓࠋࡋ࠿ࡋࠊ࠶ࡿ⛬ᗘࡢ㡿ᇦ᳨ฟࢆ⾜࠼ࡓࡇ࡜࠿ࡽ᰿
ᖿ࡜࡞ࡿࢩࢫࢸ࣒ࡢᵓ⠏ࢆ⾜࠺ࡇ࡜ࡀฟ᮶ࡓࠋ
㸺࣮࣮࢟࣡ࢻ㸼 ⥳ෆ㞀 ࢥࣥࣆ࣮ࣗࢱᨭ᥼デ᩿ ⥙⭷⚄⤒⥺⥔ᒙཌ1㸬 ᗎㄽ
㏆ᖺࠊ⥳ෆ㞀࡟ᑐࡍࡿ CAD ࡛ࡣங㢌㍯㒌࡟ᑐࡍࡿ㝗พ㒊ࡢẚ(Cup/Disc ẚ)(1)ࡸங㢌ฟ⾑ࠊ⥙⭷⚄⤒⥺⥔
Ḟᦆࡢ᳨ฟ(2㹼4)࡞࡝ࡀ࠶ࡿࡀࠊࡇࢀࡽࡢデ᩿⿵ຓ࡛ࡣ
⥳ෆ㞀ࡢึᮇẁ㝵࡛ࡢデ᩿ࢆ⾜࠺ࡇ࡜ࡀᅔ㞴࡛࠶ࡿࠋࡑࡇ࡛║࡜⥳ෆ㞀ࡢ≉ᚩ࡟ὀ┠ࡋࠊங㢌࡜㯤ᩬࢆ㏻

Fig.1 Fundus image

Fig.2 OCT imageࡿ┤⥺ࢆᑐ⛠⥺࡜ᐃ⩏ࡋࡓ᫬ࡢ⥙⭷⚄⤒⥺⥔ᒙཌࡢᑐ⛠ᛶ(5)ࢆホ౯ࡍࡿࡇ࡜࡛ࠊ⑓≧ࡀึᮇࡢሙྜ࡛࠶ࡗ

ࡳ(thickness)ࠊ㠀ᑐ⛠ᛶ(asymmetry)ࠊᕪศ(difference)ࠊ

࡚ࡶ⥳ෆ㞀ࡢྍ⬟ᛶࢆ♧၀ฟ᮶ࡿࡇ࡜ࡀ♧ࡉࢀ࡚࠸

ࡤࡽࡘࡁ(thick_SD)ࢆ⾲ࡍᅄࡘࡢホ౯್ࢆྲྀᚓࡍࡿࠋ

ࡿࠋࡋ࠿ࡋデᐹࡢ㝿ࠊ᭱⤊ⓗ࡞ุ᩿࡟ࡣ་ᖌࡢᡤぢ㠀ᑐ⛠ᛶࢆ⾲ࡍホ౯್ࢆᐃ⩏ࡍࡿࡓࡵ࡟ Fig.1 ࡟♧ࡍࡣᚲせ୙ྍḞ࡛࠶ࡾࠊࡇࡢᡭຓࡅࢆ⾜࠺ࡓࡵ࡟ࡣ༢

ᵝ࡟ங㢌࡜㯤ᩬࡢ୰ᚰࢆ㏻ࡿ┤⥺ࢆᑐ⛠⥺࡜ࡋ࡚ྲྀ࡟⑓≧ࢆุᐃࡍࡿࡔࡅ࡛࡞ࡃ⑌ᝈ㡿ᇦࢆ᳨ฟࡍࡿࡇ

ᚓࡍࡿࠋ⥙⭷⚄⤒⥺⥔ᒙࡢཌࡳࡣ Fig.2 ࡛♧ࡍ Optical࡜ࡀ㔜せ࡛࠶ࡿ࡜⪃࠼ࡽࢀࡿࠋඛ⾜◊✲(6)࡛ࡣ⥙⭷⚄Coherence Tomography (OCT) ⏬ീ࠿ࡽྲྀᚓ࡛ࡁࡿ

⤒⥺⥔ᒙࡢཌࡳ࡜㠀ᑐ⛠ᛶ࡟ὀ┠ࡋ⑌ᝈ㡿ᇦࡢ᳨ฟ

Nerve Fiber Layer (NFL) ࡢཌࡳࢆ⏝࠸ࡿࠋࡲࡓࠊࢫ࢟

ࢆ⾜ࡗ࡚࠸ࡿࡀ༑ศ࡞᳨ฟᛶ⬟࡛ࡣ࡞࠸ࠋࡑࡇ࡛ᮏ

ࣕࣥ⠊ᅖ S ෆࡢ࠶ࡿ఩⨨ A ࡛ࡢ NFL ࡢཌࡉࢆ◊✲࡛ࡣࠊ⥙⭷⚄⤒⥺⥔ᒙࡢཌࡳࠊ㠀ᑐ⛠ᛶ࡟ຍ࠼

thickness(A)࡜ᐃ⩏ࡍࡿࠋ㠀ᑐ⛠ᛶࡣࠊA ࡢᑐ⛠⥺࡟㛵࡚ᕪศࠊࡤࡽࡘࡁࢆ⪃៖ࡋࠊ」ᩘࡢ␗࡞ࡗࡓ᳨ฟ࢔

ࡍࡿᑐ⛠఩⨨ࢆ B ࡜ࡋࠊὀ┠Ⅼ A ࡜ᑐ⛠Ⅼ B ࡟࠾ࡅࣝࢦࣜࢬ࣒ࢆ⏝࠸ࡿࡇ࡜࡛⑌ᝈ㡿ᇦࡢ᳨ฟᛶ⬟ࡢྥࡿ NFL ࡢཌࡳࡢᕪศࢆࠊὀ┠Ⅼ A ࡜ᑐ⛠Ⅼ B ࡟࠾ࡅୖࢆ┠ᣦࡍ()ࠋ

ࡿ NFL ࡢཌࡳࡢᖹᆒ್࡛๭ࡗࡓࡶࡢࢆ asymmetry(A)࡜ᐃ⩏ࡍࡿࠋᕪศࡣὀ┠Ⅼ A ࡢཌࡳ࡜ὀ┠Ⅼ A ࢆ୰2㸬 ᪉ἲ
2.1 ホ౯್
⑌ᝈ㡿ᇦࢆ᳨ฟࡍࡿࡓࡵ࡟ࠊ⥙⭷⚄⤒⥺⥔ᒙࡢཌᚰ ࡜ ࡋ ࡓ U×V ࡢ ⠊ ᅖ ࡢ ཌ ࡳ ࡢ ᕪ ࡢ ⥲ ࿴ ࢆ
difference(A)࡜ᐃ⩏ࡍࡿࠋࡲࡓࠊࡤࡽࡘࡁࢆ⾲ࡍホ౯
್ thick_SD ࡣࠊࡲࡎࠊࢫ࢟ࣕࣥ⠊ᅖࡢ⾜ࠊิࡈ࡜ࡢ

−−

第15回人間情報学会講演会ポスターセッション（2013.9.19） (a) Affected Segment(b) Mahalanobis using Ave_thickFig.4 Results of glaucoma region detection
Fig.3 Results of area segmentationཌࡳࡢᶆ‽೫ᕪ row_SD(A)ࠊcolumn_SD(A)ࢆ⟬ฟࡍࡿࠋࡇࡢ row_SD(A)ࠊcolumn_SD(A)ࢆ⏝࠸࡚ thick_SD(A)ࢆḟᘧ࡛ᐃ⩏ࡍࡿࠋܿ‫)ܣ(ܦܵ_݊݉ݑ݈݋‬  ‫= )ܣ(ܦܵ_݄݇ܿ݅ݐ‬‫)ܣ(ܦܵ_ݓ݋ݎ‬(1)

2.2 ⑌ᝈ㡿ᇦ᳨ฟ

Table 1 Experimental results [%]Disease condition
⥳ෆ㞀
ᡭἲ
ホ౯್
ឤᗘ
⢭ᗘ
ṇ⟅⋡

Mahalanobis⑌ᝈ㡿ᇦࢆ᳨ฟࡍࡿࡓࡵࢫ࢟ࣕࣥ⠊ᅖࢆ M×N ศ๭ࡍࡿࠋศ๭ࡣ Fig.3 ࡟♧ࡍᵝ࡟ங㢌࡜㯤ᩬࡢ୰Ⅼࢆᇶ‽࡜ࡋࠊங㢌࡜㯤ᩬࢆ㏻ࡿ┤⥺࡟ᑐࡋ࡚ᖹ⾜ࠊᆶ┤࡟⾜࠺ࠋศ๭ࡋࡓᚋࠊྛ㡿ᇦෆ࡛ྲྀᚓࡋࡓホ౯್࠿ࡽ(m ࠊ n)␒┠ࡢ㡿ᇦࡢᖹᆒ್ Ave_thick(m ࠊ n)ࠊ

MLE

Ave_asym(mࠊn)ࠊAve_dif(mࠊn)ࠊAve_SD(mࠊn) (m=1ࠊ…ࠊM n=1ࠊ…ࠊN)ࢆ⟬ฟࡍࡿࠋྛศ๭㡿ᇦ࡟ᑐࡋ࡚
ࠕṇᖖࠖࠊࠕ⥳ෆ㞀ࠖࡢࢡࣛࢫࢆసᡂࡋࠊศ㢮࢔ࣝࢦࣜࢬ࣒࡜ࡋ࡚ࠊ࣐ࣁࣛࣀࣅࢫ㊥㞳ἲࠊ᭱ᑬἲ(MLE)ࠊ
᭱▷㊥㞳ἲ(NN)ࠊࢧ࣏࣮ࢺ࣋ࢡࢱ࣮࣐ࢩࣥ(SVM)ࢆ
⏝࠸ࡓࠋᮏᡭἲ࡛ࡣ SVM ࡟ࢯࣇࢺ࣐࣮ࢪࣥἲ࡜࣮࢝

NNࢿࣝࢺࣜࢵࢡ(8ࠊ9)ࢆ㐺⏝ࡍࡿࠋ

3㸬 ⑌ᝈ㡿ᇦ᳨ฟᛶ⬟ホ౯ᐇ㦂ᮏᡭἲࡢ᭷ຠᛶࢆ᳨ドࡍࡿࡓࡵࠊ་ᖌ࡟ࡼࡗ࡚⑌
ᝈ㡿ᇦࡀ≉ᐃࡉࢀ࡚࠸ࡿ 14 ⑕౛࡟ᑐࡋ࡚⑌ᝈ㡿ᇦ᳨ฟࢆ⾜ࡗࡓࠋࡲࡓࠊṇᖖ 13 ⑕౛࡟ᑐࡋ࡚ࡶ⑌ᝈ㡿ᇦSVM

᳨ฟࢆ⾜ࡗࡓࠋᛶ⬟ホ౯࡟⏝࠸ࡿឤᗘ(Recall)ࠊ⢭ᗘ(Precision)ࠊṇ⟅⋡(Accuracy)ࡣࡑࢀࡒࢀḟᘧ࠿ࡽồࡵࡓࠋ  ܴ݈݈݁ܿܽ = ܶܲΤ(ܶܲ + ‫)ܰܨ‬(2)  ܲ‫ܲܶ = ݊݋݅ݏ݅ܿ݁ݎ‬Τ(ܶܲ + ‫)ܲܨ‬(3)

  ‫= ݕܿܽݎݑܿܿܣ‬(ܶܲ + ܶܰ)Τ(ܶܲ + ‫ ܲܨ‬+ ܶܰ + ‫)ܰܨ‬ṇᖖṇ⟅⋡

Ave_thick
Ave_asym
Ave_dif
Ave_SD
Ave_thick &Ave_asym
All


58.3
43.1


39.0

24.8
21.0

91.3
91.4
88.3
83.5




85.1

58.3

36.2

91.0







92.6

99.2

Ave_thick
Ave_asym
Ave_dif
Ave_SD
Ave_thick &Ave_asym
All

68.1
52.1



38.1
40.1
25.3


91.1
92.2
86.8


96.6
93.6

93.6

54.2



91.6



46.5

42.1



99.3

Ave_thick
Ave_asym
Ave_dif
Ave_SD
Ave_thick &Ave_asym
All

66.0
41.0
29.9



46.1
26.2
22.2

90.9
93.3
90.4



96.2
95.2
95.3

Ave_thick
Ave_asym
Ave_dif
Ave_SD
Ave_thick &Ave_asym
All

52.1

43.1





51.4

43.3

92.8



68.1
36.8
36.8
28.5

40.8

29.3
26.6

91.9
93.0
90.5
90.6

99.5
99.5
99.5
99.8

64.6

30.6



99.8



31.1

88.4

99.8

᱌ᡭἲࡣ་⒪ࡢ⌧ሙ࡛⏝࠸ࡿࡶࡢ࡜ࡋ࡚᝿ᐃࡋ࡚࠾
ࡾࠊ⑌ᝈ⩌ࢆぢ㏨ࡋ࡚ࡋࡲ࠺ࡇ࡜ࡀ୍␒ࡢ༴㝤࡛࠶ࡿ࡜⪃࠼ࡽࢀࡿࡓࡵ࡛࠶ࡿࠋ

(4)ࡲࡎࡣ⥳ෆ㞀ࡢ⑕౛࡟ᑐࡍࡿ⤖ᯝࢆࡳࡿࠋឤᗘ࡛

ឤᗘࡣ⑌ᝈ⩌ࡀ㝧ᛶ࡟࡞ࡿ☜⋡ࠊ⢭ᗘࡣ㝧ᛶ࡜ศࡣ Ave_thick ࢆ⏝࠸ࡓ࣐ࣁࣛࣀࣅࢫ㊥㞳ἲ࡛ࡢ᳨ฟࡀ

㢮ࡋࡓ࠺ࡕࡢ⑌ᝈ⩌࡛࠶ࡿ☜⋡ࠊṇ⟅⋡ࡣṇࡋࡃุ

.5%࡜୍␒㧗ࡃࠊḟ࠸࡛ All ࢆ⏝࠸ࡓ SVM ࡛ࡢ᳨᩿ࡉࢀࡿ☜⋡ࢆ⾲ࡋ࡚࠸ࡿࠋ⑌ᝈ㡿ᇦ᳨ฟ᪉ἲࡈ࡜

ฟࡀ஧␒┠࡟㧗ࡃ .1%࡜࠸࠺⤖ᯝࢆᚓࡿࡇ࡜ࡀฟ

ࡢឤᗘࠊ⢭ᗘࠊṇ⟅⋡ࡢẚ㍑ࢆ Table 2 ࡟ࠊAve_thick᮶ࡓࠋ⢭ᗘ࡛ࡣ Ave_asym ࢆ⏝࠸ࡓ᭱▷㊥㞳ἲ࡛ࡢ᳨ࢆ⏝࠸ࡓ࣐ࣁࣛࣀࣅࢫ㊥㞳࡛ࡢ᳨ฟ౛ࢆ Fig.4 ࡟♧ࡍࠋ

ฟࡀ 46.1%࡜୍␒㧗࠸⤖ᯝࢆᚓࡿࡇ࡜ࡀฟ᮶ࡓࠋࡋ࠿ᅄࡘࡢホ౯್ࢆ⤌ࡳྜࢃࡏࡓ㝿ࡢ⤖ᯝࢆ All ࡜グ㍕ࡋࡇࡢ᫬ࡢឤᗘࢆࡳࡿ࡜ 41.0%࡜ప࠸⤖ᯝ࡛࠶ࡗࡓࠋ

ࡍࡿࠋᐇ㦂࡟ࡣ leave-one-out cross-validation ࢆ⏝࠸ࡓࠋṇ⟅⋡࡛ࡣ⢭ᗘࡢ⤖ᯝࡀ㧗࠸᳨ฟᡭἲ࡯࡝⤖ᯝࡀ㧗ᮏᐇ㦂࡛ࡣᛶ⬟ࢆホ౯ࡍࡿ࡟࠶ࡓࡾࠊឤᗘࠊ⢭ᗘࠊ࠸ഴྥࡀぢཷࡅࡽࢀࡓࠋホ౯್ࡈ࡜࡟⤖ᯝࢆࡳࡿ࡜

ṇ⟅⋡࡜୕ࡘࡢホ౯ᣦᶆࢆ⏝ពࡋࡓࡀࠊࡑࡢ୰࡛ࡶ

Ave_thick ࡢឤᗘࡀྛᡭἲࡢ୰࡛ࡣ㧗࠸⤖ᯝ࡛࠶ࡗࡓࠋ

ឤᗘࡢ⤖ᯝࢆ᭱ࡶ㔜せどࡍࡿࠋࡑࡢ⌮⏤࡜ࡋ࡚ࠊᥦ

ࡇࢀࡣ௒ᅇᐇ㦂࡟⏝࠸ࡓ 14 ⑕౛඲࡚ࡢ⑓≧ࡀ࠶ࡿ⛬ᗘ㐍⾜ࡋ࡚࠾ࡾࠊ⥙⭷⚄⤒⥺⥔ᒙཌࡢῶᑡࡀࡣࡗࡁ

−−

第15回人間情報学会講演会ポスターセッション（2013.9.19）(3)ࡾࡋ࡚࠸ࡓࡓࡵࡔ࡜⪃࠼ࡽࢀࡿࠋ୍᪉⢭ᗘ࡛ࡣ

Y. Hayashi, T. Nakagawa, Y. Hatanaka, et al,

Ave_asym ࢆ⏝࠸ࡓሙྜࡀྛᡭἲ࡛㧗࠸⤖ᯝ࡛࠶ࡗࡓࠋ

“Detection of retinal nerve fiber layer defects inࡇࢀࡣᑐ⛠ᛶࢆࡳࡿࡇ࡜࡛⥳ෆ㞀ࡢ⑕≧ࡢ≉ᚩࢆ࡜

retinal fundus images using Gabor filtering”, inࡽ࠼ࡿࡇ࡜ࡀฟ᮶࡚࠸ࡓࡓࡵࡔ࡜⪃࠼ࡽࢀࡿࠋ

Proc. 63,(0HGLFDO,PDJLQJ&RPSXWHU-aided

Ave_thick ࡢ⢭ᗘࡀప࠸⌮⏤࡜ࡋ࡚ࡣࠊᒙࡢཌࡳ࡟ࡣ

Diagnosis,

Diego,

6514,

pp.65142Z-1-65142Z-8, .

ಶேᕪࡀᏑᅾࡋࠊṇᖖ⟠ᡤ࡛࠶ࡗ࡚ࡶ௚࡜ẚ㍑ࡋࡓ
᫬࡟ཌࡳࡀⷧࡅࢀࡤ⑌ᝈ㡿ᇦ࡜ࡉࢀ࡚ࡋࡲ࠺࠿ࡽࡔ

San

(4)

R. Sihota, P. Sony, V. Gupta, et al, “Diagnostic
capability of optical coherence tomography in࡜⪃࠼ࡽࢀࡿࠋ

evaluating the degree of glaucomatous retinal nerveḟ࡟ṇᖖࡢ⤖ᯝࢆࡳࡿࠋṇ⟅⋡ࡀ 99%௨ୖࡢ⤖ᯝࢆ」ᩘࡢ᳨ฟᡭἲ࡛ᚓࡿࡇ࡜ࡀฟ᮶ࡓࠋSVM ࡛ࡢ᳨

fiber damage”, Invest Ophthalmol Vis Sci, ,ฟᡭἲ࡛ࠊAve_thickࠊAll ࢆ⏝࠸ࡓ᫬ࡣ⥳ෆ㞀ࡢឤᗘ

pp.2006-2010, 2006.

ࡀ㧗࠸࡟ࡶ㛵ࢃࡽࡎࡇࡢࡼ࠺࡞㧗࠸⤖ᯝࢆᚓࡽࢀࡓ

(5)⁪⏣Ύ, すඃ⣖⨾, ᑎᯘ㈼ྖ, ᱵ⏣࿴᪼, ᐩᡤᩔࡇ࡜࠿ࡽࠊṇᖖ⩌࡟ᑐࡋ࡚ṇࡋࡃ㝜ᛶ࡛࠶ࡿ࡜ุ᩿

⏨, “OCT ⏬ീ࠿ࡽᚓࡽࢀࡿ⥙⭷ࡢᒙ᝟ሗࢆ⏝ฟ᮶࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡿࠋ

࠸ࡓ⥳ෆ㞀デ᩿⿵ຓ”, ⏬ീࢭࣥࢩࣥࢢࢩ࣏ࣥ
ࢪ࣒࢘ 2012, IS1-11, 2012.

௨ୖࢆࡲ࡜ࡵࡿ࡜ࠊᮏᡭἲࢆ⏝࠸࡚⑌ᝈ㡿ᇦࢆ᳨ฟࡍࡿࡇ࡜ࡀྍ⬟࡛࠶ࡿࡇ࡜ࢆ☜ㄆࡋࡓࠋ(6)⁪⏣Ύ, ᑎᯘ㈼ྖ, ᱵ⏣࿴᪼, ᐩᡤᩔ⏨, “⥙⭷⚄⤒⥺⥔ᒙཌࡢ㠀ᑐ⛠ᛶࢆ⏝࠸ࡓ⥳ෆ㞀⑌ᝈ㡿

4㸬 ⤖ㄽ࡜௒ᚋࡢᒎᮃᇦ ࡢ ᳨ ฟ ”, ⢭ ᐦ ᕤ Ꮫ ఍ ⛅Ꮨ ኱ ఍ 2012, G18,
2012.

ᮏ◊✲࡛ࡣ⥙⭷⚄⤒⥺⥔ᒙࡢཌࡳࠊ㠀ᑐ⛠ᛶ࡟ຍ࠼࡚ᕪศࠊࡤࡽࡘࡁࢆホ౯್࡜ࡋ࡚ྲྀᚓࡋࠊ␗࡞ࡿ

ศ㢮࢔ࣝࢦࣜࢬ࣒ࢆ⏝࠸࡚⥳ෆ㞀⑌ᝈ㡿ᇦࡢ᳨ฟᡭ

⁪⏣Ύ, ᑎᯘ㈼ྖ, ᱵ⏣࿴᪼, ᐩᡤᩔ⏨, “⥙⭷⚄⤒⥺⥔ᒙཌࡢ≉ᚩࢆ⏝࠸ࡓ⥳ෆ㞀⑌ᝈ㡿ᇦࡢἲࢆᥦ᱌ࡋࡓࠋᥦ᱌ᡭἲࡣࢫ࢟ࣕࣥ⠊ᅖෆ࡟Ꮡᅾࡍ

᳨ฟ”, ືⓗ⏬ീฎ⌮ᐇ฼⏝໬࣮࣡ࢡࢩࣙࢵࣉ

ࡿங㢌࡜㯤ᩬࡢ఩⨨ࢆࡶ࡜࡟㡿ᇦࢆศ๭ࡋࠊศ๭㡿

2013, I1-1, 2013.

ᇦࡈ࡜࡛ྲྀᚓࡋࡓホ౯್ࡢᖹᆒ್ࢆ⟬ฟࡍࡿࠋྛศ

(8)

N. Cristianini and J. S. Taylor, “Introduction to
Support Vector Machines and Other Kernel-based๭㡿ᇦ࡛ᖹᆒ್ࢆ⏝࠸࡚ࠕṇᖖࠖࠊࠕ⥳ෆ㞀ࠖࢡࣛࢫࡢసᡂࢆ⾜࠸ࠊ࣐ࣁࣛࣀࣅࢫ㊥㞳ἲࠊ᭱ᑬἲࠊ᭱▷Learning Methods”, Cambridge University Press,㊥㞳ἲࠊࢧ࣏࣮ࢺ࣋ࢡࢱ࣮࣐ࢩࣥ࡜࠸ࡗࡓ␗࡞ࡿศ

2000.

㢮࢔ࣝࢦࣜࢬ࣒࡟ࡼࡾ⑌ᝈ㡿ᇦࡢ᳨ฟࢆ⾜ࡗࡓࠋࡲ(9)๓⏣ⱥస, ᮧ℩ὒ, “࣮࢝ࢿࣝ㠀⥺ᙧ㒊ศ✵㛫ἲࡓࠊᥦ᱌ᡭἲࡢ᭷⏝ᛶࢆ᳨ドࡍࡿࡓࡵࠊணࡵ་ᖌ࡟

࡟ࡼࡿࣃࢱ࣮ࣥㄆ㆑”, 㟁Ꮚ᝟ሗ㏻ಙᏛ఍ㄽᩥ

ࡼࡗ࡚⑌ᝈ㡿ᇦ࡜≉ᐃࡉࢀ࡚࠸ࡿಶᡤ࡜᳨ฟ⤖ᯝࡢㄅ, J82-D-II(4), pp600-612, 1999.

ẚ㍑᳨ウࢆ⾜ࡗࡓࠋ᳨ฟ⤖ᯝ࠿ࡽ」ᩘࡢ᳨ฟᡭἲ࡛
ࡢ᫂☜࡞ඃຎࡢᕪࡣ⌧ࢀ࡞࠿ࡗࡓࠋṇᖖ࡟ᑐࡍࡿ⤖ᯝ࡛ࡣ㧗࠸ṇ⟅⋡ࢆᚓࡿࡇ࡜ࡀฟ᮶ࡓࠋ୍᪉ࠊ⥳ෆ㞀࡟ᑐࡍࡿឤᗘࠊ⢭ᗘࡣᐇ㝿ࡢ་⒪ࡢ⌧ሙ࡟⏝࠸ࡽࢀࡿ⛬ࡢ⤖ᯝࢆᚓࡿࡇ࡜ࡣฟ᮶࡞࠿ࡗࡓࠋࡋ࠿ࡋࠊ
࠶ࡿ⛬ᗘࡢ㡿ᇦ᳨ฟࢆ⾜࠼ࡓࡇ࡜࠿ࡽ᰿ᖿ࡜࡞ࡿࢩ
ࢫࢸ࣒ࡢᵓ⠏ࢆ⾜࠺ࡇ࡜ࡀฟ᮶ࡓࠋ
௒ᚋࡢᒎᮃ࡜ࡋ࡚ࠊ᳨ฟᛶ⬟ࡢྥୖࡀᣲࡆࡽࢀࡿࠋ
ࡑࡢࡓࡵࠊࢡࣛࢫࡢసᡂ࡟⏝࠸ࡿ⑕౛ᩘࢆࠕṇᖖࠖࠊࠕ⥳ෆ㞀ࠖ࡜ࡶ࡟ቑࡸࡍࡇ࡜ࡀᚲせ࡛࠶ࡿ࡜⪃࠼ࡽࢀࡿࠋࡲࡓࠊ⌧ᅾࡣ୍ࡘ୍ࡘࡢ᳨ฟᡭἲࢆ༢⊂࡛⏝࠸࡚࠸ࡿࡀࠊ」ᩘࡢᡭἲࢆ⤌ࡳྜࢃࡏࡿࡇ࡜᳨࡛ฟ
⤖ᯝࡢྥୖࢆ┠ᣦࡍࠋ

ᩥ ⊩
(1)

⏿୰⿱ྖ, ⚟⏣ᆂ♸, ⣡ᅵ῟, ௚, “║ᗏ⏬ീୖࡢ⚄⤒ங㢌㒊ゎᯒ࡟ࡼࡿ⥳ෆ㞀ࣜࢫࢡ᥎ᐃ”,ಙᏛᢏሗ(MI),   , pp.25-28, 2009.

(2)ᑠᯘ⩏἞, “OCT ࡢぢ᪉”, ᪥ᮏど⬟カ⦎ኈ༠఍ㄅ, 39, pp.21-, 2010.

−−第15回人間情報学会講演会ポスターセッション（2013.9.19）

⾲㠃➽㟁ಙྕࢆ⏝࠸ࡓ⎔ቃኚ໬࡟ࡼࡿ➽ືసኚ໬࡟㛵ࡍࡿ◊✲኱࿴⏣ᩗ࿃ 1ᡞ⏣┿ᚿ 2 Ḉἑ⦾ 1 ⛅⏣⣧୍ 3㏆⸨୍᫭ 4୰ᮧ⿱୍ 41 බ❧ࡣࡇࡔ࡚ᮍ᮶኱Ꮫ ࠛ041-8655 ఫᡤ ໭ᾏ㐨ภ㤋ᕷட⏣୰㔝⏫ 116-2 බ❧ࡣࡇࡔ࡚ᮍ᮶኱Ꮫ
2 ⇃ᮏ኱Ꮫ ࠛ860-8555 ఫᡤ ⇃ᮏ┴⇃ᮏᕷ㯮㧥 2-39-1 ⇃ᮏ኱Ꮫ⥲ྜ᝟ሗᇶ┙ࢭࣥࢱ࣮
3 㔠ἑ኱Ꮫ ࠛ920-1192 ఫᡤ ▼ᕝ┴㔠ἑᕷゅ㛫⏫ 㔠ἑ኱ᏛᕤᏛ㒊᝟ሗࢩࢫࢸ࣒ᕤᏛ⛉4 ி㒔኱Ꮫ ࠛ606-8501 ఫᡤ ி㒔ᗓி㒔ᕷᕥி༊ྜྷ⏣ᮏ⏫ ி㒔኱Ꮫ Ꮫ⾡᝟ሗ࣓ࢹ࢕࢔ࢭࣥࢱ࣮ᴫせ⫼ᬒ࡜┠ⓗ㸸ேࡣ㐠ືࢆ⾜࠺࡜ࡁ࡟࿘ᅖࡢ⎔ቃ࡟ᑐᛂࡋ࡚ືసࢆኚ໬ࡉࡏࡇ࡜ࡀ࠶ࡿࠋࡑࡇ࡛ᮏ௳◊✲࡛ࡣ⾲㠃➽㟁ಙྕࢆ⏝࠸࡚⎔ቃࢆኚ໬ࡉࡏࡓᐇ㦂ࢆ⾜ࡗࡓ࡜ࡁ࡟ಙྕࡀ࡝ࡢࡼ࠺࡟ኚ໬ࡍࡿ࠿ࢆほᐹࡍࡿࠋ᪉ἲ㸸⫝㛵⠇ࡢࡳࢆ⏝࠸࡚⌫ࢆᡴᧁࡍࡿᐇ㦂ࢆ⏝ពࡋࡓࠋ⿕㦂⪅ࡣ⌫ࡢ㔜ࡉࢆࢃ࠿ࡗ࡚࠸ࡿ≧ែ࡜ࢃ࠿ࡽ࡞࠸≧ែ࡛⌫ࢆ྇ࡃᐇ㦂ࢆ⾜ࡗࡓࠋ⌫ࡢ㔜ࡉࢆࢃ࠿ࡗ࡚࠸ࡿ≧ែࡣ஦๓࡟⦎⩦ࢆࡋࡓ≧ែ࡛⌫ࢆᡴᧁࡍࡿࠋ⌫ࡢ㔜ࡉࢆࢃ࠿ࡽ࡞࠸≧ែࡣࣛࣥࢲ࣒࡟㔜ࡉࡀኚ໬ࡍࡿ⌫ࢆᡴᧁࡍࡿࡇ࡜࡛ᵓᡂࡋࡓࠋゎᯒ᪉ἲ࡜ࡋ࡚ RMS ࢆ⏝࠸࡚ಙྕࡢ≉ᚩࢆほᐹࡍࡿࠋ
⤖ᯝ㸸ࢃ࠿ࡗ࡚࠸ࡿ≧ែ࡛ࡣ㔜ࡉ࡟ᛂࡌ࡚୺ື➽ࡀ኱ࡁࡃ࡞ࡿᵝᏊࡀほᐹ࡛ࡁࡓࠋࡲࡓࠊᡴᧁ๓࡟୺ື➽ࡀ཰⦰ࡋ࡚࠸ࡿᵝᏊࢆほᐹ࡛ࡁࡓࠋࢃ࠿ࡽ࡞࠸≧ែ࡛ࡣ㔜ࡉ࡟ᛂࡌ࡚␗࡞ࡿ≉ᚩࡀᚓࡽࢀࡓࠋ⌫ࡀ㔜ࡓ࠸ሙྜࡣᡴᧁᚋ
࡟෌ࡧ୺ື➽ࡀ཰⦰ࡋࠊ㍍࠸ሙྜࡣ෌ࡧᣕᢠ➽ࡀ཰⦰ࡍࡿᵝᏊࡀほᐹ࡛ࡁࡓࠋ
⪃ᐹ㸸ࢃ࠿ࡗ࡚࠸ࡿ≧ែ࡛ࡣ㔜ࡉ࡟ᛂࡌ࡚஦๓࡟ᚲせ࡞ຊࢆฟࡋ࡚࠸ࡿࡇ࡜ࡀ⪃࠼ࡽࢀࡿࠋࢃ࠿ࡽ࡞࠸≧ែ࡛ࡣᡴᧁࡋࡓ▐㛫࡟⌫ࡢ㔜ࡉࢆ⌮ゎࡋ࡚㔜ࡉ࡟ᛂࡌ࡚཰⦰ࡉࡏࡿ➽ࢆኚ໬ࡉࡏ࡚࠸ࡿࡇ࡜ࡀ⪃࠼ࡽࢀࡿࠋࡇࢀࡽࡢኚ
໬ࡢཎᅉ࡜ࡋ࡚⓶㉁⤒⏤཯ᑕࡀ㛵ಀࡋ࡚࠸ࡿࡢ࡛ࡣ࡞࠸࠿࡜⪃࠼ࡽࢀࡿࠋ
⤖ㄽ㸸ࡇࢀࡽࡢ⤖ᯝ࠿ࡽேࡣ࿘ࡾࡢ⎔ቃࢆ▱ࡗ࡚࠸ࡿ࡜⎔ቃ࡟ྜࢃࡏ࡚ᚲせ࡞ຊࢆฟࡋ࡚㐠ືࢆࡍࡿࡇ࡜ࡀࢃ࠿
ࡗࡓࠋᅇࡾࡢ⎔ቃࢆ▱ࡽ࡞࠸ሙྜࠊ⎔ቃࢆ⌮ゎࡋࡓᚋ࡟⎔ቃ࡟ᛂࡌ࡚ືసࢆኚ໬ࡉࡏ࡚㐺ᛂࡉࡏ࡚࠸ࡃࡇ࡜ࡀࢃ
࠿ࡗࡓࠋࡇࢀ࡟ࡼࡗ࡚ேࡢືసࡀពᅗࡋ࡚⾜ࢃࢀࡓࡶࡢ࡞ࡢ࠿࡝࠺࠿ࢆุู࡛ࡁࡿ࡜⪃࠼ࡽࢀࡿࠋ
࣮࣮࢟࣡ࢻ㸸⾲㠃➽㟁ಙྕ ⎔ቃኚ໬ ᡴᧁືస ୺ື➽ ᣕᢠ➽ ⓶㉁⤒⏤཯ᑕ

1. ⫼ᬒ

ࡢ◊✲ࡣᩘከࡃ⾜ࢃࢀ࡚࠸ࡿࠋࡑࡢ୰࡛ࡶ⎔ቃ࡬ࡢᑐᛂ࡟ࡼࡿ➽άືᵝᘧࡢኚ໬ࢆほᐹࡋ࡚࠸ࡿ◊✲࡜ࡋ࡚ ேࡣ㐠ືࢆ⾜࠺࡜ࡁ࿘ᅖࡢ⎔ቃ࡟ᑐᛂࡋ࡚ືసࢆኚ
ධᓥࡽࡣࣃ࣮࣡࢔ࢩࢫࢺ௜ࡁ⮬㌿㌴ࡢ㈇Ⲵไᚚࢆᑐ㇟
໬ࡉࡏࡿࡇ࡜ࡀ࠶ࡿࠋ౛࠼ࡤࠊᆅ㠃࡟ịࡀᙇࡾࠊ⁥ࡾࡸࡍ࠸≧ែ࡛ࡣே㛫ࡣ㊊⿬ࡢ⾲㠃✚ࢆቑຍࡉࡏ࡚Ṍࡁࠊ ࡟ࠊไᚚ≉ᛶࡀே㛫࡟୚࠼ࡿᙳ㡪ࢆ᳨ウࡋ࡚࠸ࡿ[1]ࠋ
⮬㌿㌴ࡢ㉮⾜୰࡟Ⓨ⏕ࡍࡿࢥ࣮ࢫࡢ໙㓄ࡀኚ໬ࡍࡿሙ
⁥ࡽ࡞࠸ࡼ࠺࡟Ṍ⾜ࡍࡿࠋࡇࡢࡼ࠺࡞࿘ࡾࡢ⎔ቃ࡟ᑐᡤࡸඛࡢぢ࠼࡞࠸᭤ࡀࡾゅ࡞࡝ࡢ࢖࣋ࣥࢺண ࡀᚲせᛂࡋࡓືసࡢ୰࡟⟽ࢆᣢࡕୖࡆࡿືసࡀ࠶ࡿࠋ⟽ࢆᣢ࡞ሙ㠃࡟╔┠ࡋࠊᚰᢿኚືࡸ➽άືࢆほᐹࡋ࡚࠸ࡿࠋ
ࡕୖࡆࡿ࡜ࡁ࡟ࡍ࡛࡟⟽ࡢ୰㌟ࢆ▱ࡗ࡚࠸ࡿሙྜࠊࡑ
ࡑࡢ⤖ᯝࠊࡢࡰࡾᆏ࡟ኚ໬ࡍࡿሙ㠃࡛➽⑂ປࡀቑຍࡋࠊ
ࡢ㔜ࡉ࡟㐺ᛂࡋࡓຊࢆⓎ⏕ࡉࡏࠊࢫ࣒࣮ࢬ࡟ືసࢆ⾜ࢺࣝࢡ୺యࡢࡇࡂ᪉࡟ኚ໬ࡋࡓࡇ࡜ࡀ♧ࡉࢀ࡚࠸ࡿࠋ
࠺ࠋࡋ࠿ࡋࠊ⟽ࡢ୰㌟ࢆ▱ࡽ࡞࠸ሙྜࠊ㔜ࡉ࡟㐺ࡋࡓࡇࡢ⤖ᯝ࠿ࡽ⎔ቃࡀኚ໬ࡍࡿࡇ࡜࡟ࡼࡗ࡚ྠࡌືస࡛
ຊ࡛⟽ࢆᣢࡕୖࡆࡿࡇ࡜ࡀ࡛ࡁࡎࠊ⟽ࡀᣢࡕୖࡀࡽ࡞
ࡶ➽⫗ࡢ౑࠸᪉ࡀ␗࡞ࡿࡇ࡜ࡀ♧ࡉࢀࡓ࡜ゝ࠼ࡿࠋࡋࡃ࡞ࡿࡇ࡜ࡸࠊᣢࡕୖࡆࡍࡂ࡚ࡋࡲࡗ࡚⭜ࢆ③ࡵ࡚ࡋ࠿ࡋࠊࡇࡢ◊✲࡛ࡣ㐠ືࡢ┠ⓗࢆኚ໬ࡉࡏ࡚࠸࡞࠸࡜
ࡲ࠺ྍ⬟ᛶࡀ࠶ࡿࠋࡇࡢࡼ࠺࡞ືసࡣືࡁࡀྠࡌࡔࡀࠊ
ゝ࠼ࡿࠋ࿘ᅖࡢ⎔ቃ᝟ሗࢆ▱ࡽ࡞࠸ࡓࡵ࡟㉳ࡇࡿࡢ࡛ࡣ࡞࠸࠿
༢⣧࡞⎔ቃࠊືస࡛⎔ቃኚ໬࡬ࡢᑐᛂࢆぢࡓ◊✲࡜࡜⪃࠼ࡓࠋࡋ࡚ᑠụࡽ[2]ࡢ◊✲ࡀ࠶ࡿࠋᑠụࡽࡣ࣮࣎ࣝ࢟ࣕࢵࢳ
ࡑࡇ࡛ࠊᡃࠎࡣ⏕యಙྕࢆ⏝࠸࡚ࠊ㐠ືࡢ⎔ቃኚ໬ࣥࢢࢱࢫࢡ࡟╔┠ࡋࠊVR ᢏ⾡ࢆ⏝࠸࡚ࡉࡲࡊࡲ࡞㔜࡟ࡼࡿືసኚ໬ࢆẚ㍑ࡍࡿᚲせࡀ࠶ࡿ࡜⪃࠼ࡓࠋࡑࡢ
ຊ⎔ቃࢆᨃఝⓗ࡟෌⌧ࡋࡓࠋࡇࢀ࡟ࡼࡗ࡚ࠊ㸯G ௨እࡓࡵࠊᮏ◊✲࡛ࡣ⏕యಙྕࡢ୰࡛ࡶ➽⫗ࡢ཰⦰ࢆほᐹ࡛ࡢ࣮࣎ࣝ࢟ࣕࢵࢳࣥࢢࢱࢫࢡࢆᵓᡂࡋࠊ⿕㦂⪅ࡢᏛࡍࡿࡇ࡜ࡀ࡛ࡁࠊࣜ࢔ࣝࢱ࢖࣒࡛ࡢゎᯒ࡟ࡶᛂ⏝ࡍࡿ⩦ࡢ㐣⛬ࢆほᐹࡋ࡚࠸ࡿࠋࡑࡢ⤖ᯝࠊ㸯G ௨እ࡛ࡢ㔜ࡇ࡜ࡀྍ⬟࡞➽㟁ಙྕࢆ฼⏝ࡍࡿࡇ࡜࡛➽⫗ࡢ≧ែࢆຊ⎔ቃ࡛ࡶ᭱⤊ⓗ࡟㸯G ࡢ⎔ቃ࡜ኚࢃࡽ࡞࠸ࢱ࢖࣑ࣥほᐹࡋ࡚࠸ࡃࠋࡲࡓࠊ➽㟁ಙྕࡢィ ᪉ἲࡣ⓶⭵⾲㠃ࢢ࡛ᡭࢆື࠿ࡍࡇ࡜ࡀ࡛ࡁࡿ࡜♧ࡋࡓࠋࡇࡢ◊✲࡛ࡣ࡛ィ ࢆ⾜࠺⾲㠃➽㟁࡜┤᥋➽⫗࡟㟁ᴟࢆ่ࡍࡇ࡜࡛ண ࡛ࡁ࡞࠸⎔ቃࢆᏛ⩦ࡋ࡚࠸ࡃࡇ࡜ࢆぢ࡚࠸ࡿࡀࠊィ ࢆ⾜࠺㔪➽㟁ࡀ࠶ࡿࡀࠊᮏ◊✲࡛ࡣࡼࡾᡭ㍍࡛↓ᮏ◊✲࡛ࡣᏛ⩦ࡋ࡚࠸ࡿ⎔ቃ࡜Ꮫ⩦࡛ࡁ࡞࠸⎔ቃࢆẚ౵く࡟➽㟁఩ࢆィ ࡛ࡁࡿࠋ⾲㠃➽㟁ಙྕࢆ౑⏝ࡋ࡚࡭࡚࠸ࡃࡇ࡜࡟ࡼࡗ࡚Ꮫ⩦࡛ࡁ࡞࠸⎔ቃ࡟ᑐࡋ࡚ேࡀ
➽⫗ࡢ≧ែࢆほᐹࡍࡿࠋ࡝ࡢࡼ࠺࡟ᑐᛂࡋ࡚࠸ࡿ࠿ࢆほᐹࡋ࡚࠸ࡃࠋ
ࡇࢀࡽࡢࡇ࡜࠿ࡽᮏ✏࡛ࡣ㐠ືࡣྠࡌࡔࡀ⎔ቃࡀ␗」㞧࡞ືస࡛➽άືᵝᘧࡢኚ໬ࢆほᐹࡋ࡚࠸ࡿ◊✲࡞ࡾࠊ➽⫗ࡢ౑࠸᪉ࢆኚ໬ࡉࡏࡿᚲせࡀ࠶ࡿືసࢆ⾜࡜ࡋ࡚⯅㇂ࡽ[3]ࡢ◊✲ࡀ࠶ࡿࠋ⯅㇂ࡽࡣ๢㐨ࡢᘬࡁ㠃ࠊ࠸ࠊࡑࡢ࡜ࡁࡢ⾲㠃➽㟁ಙྕࢆほᐹࡍࡿࡇ࡜࡛➽⫗ࡢ౑࠸᪉ࡀ␗࡞ࡿ࠿࡝࠺࠿ࢆほᐹࡍࡿࡇ࡜ࢆ┠ⓗ࡜ࡍࡿࠋ  ᘬࡁᑠᡭືస࡟࠾࠸࡚ࠊ๢㐨➇ᢏ⪅࡜㠀๢㐨➇ᢏ⪅ࡢ                           㛫࡛ࠊୖ⫥ࡢ཯ᛂ᫬㛫࡜➽⫗ࡢ౑࠸᪉࡟࡝ࡢࡼ࠺࡞㐪2. 㛵㐃◊✲࠸ࡀ⌧ࢀࡿ࠿࡟ࡘ࠸࡚◊✲ࢆ⾜ࡗ࡚࠸ࡿࠋᐇ㦂࡛ࡣࠊࡇࢀࡲ࡛࡟⾲㠃➽㟁ಙྕࢆ⏝࠸ࡓືసኚ໬࡟ࡘ࠸࡚
ග่⃭ࡀᥦ♧ࡉࢀࡓ▐㛫ࠊ๢㐨⏝ᡴࡕ㎸ࡳྎ࡟ᑐࡋ࡚

−  −

第15回人間情報学会講演会ポスターセッション（2013.9.19）

ᅗ 2 ᐇ㦂࡛౑⏝ࡋࡓ⌫
Fig. 2 Ball used in the experimentᅗ 1 ᐇ㦂ᴫ␎ᅗ
Fig. 1 Experiment diagrammatical viewᘬࡁ㠃ࠊᘬࡁᑠᡭࢆᡴࡕ㎸ࡴࠋࡑࡢ⤖ᯝࠊ๢㐨➇ᢏ⪅࠿ࡽ≉ᚩⓗ࡞➽άືᵝᘧࡀ☜ㄆࡉࢀࡓࡀࠊ཯ᛂ᫬㛫࡟࠾࠸࡚᭷ព࡞ᕪࡣᚓࡽࢀ࡞࠿ࡗࡓࠋࡲࡓࠊᯇୗࡽ[4]ࡣ
࢔࣮ࢳ࢙࣮ࣜࡢᘪᑕᢏ⾡࡟ࡘ࠸࡚⾲㠃➽㟁ಙྕࢆ⏝࠸࡚⩦⇍⪅࡜ᮍ⩦⇍⪅ࡢẚ㍑ࢆ⾜ࡗ࡚࠸ࡿࠋࡑࡢ⤖ᯝࠊ⇍⦎⪅ࡣヨ⾜ᅇᩘࡸⅬᩘ࡟㛵ಀ࡞ࡃᏳᐃࡋ࡚ྠᵝࡢ⾲㠃➽㟁ಙྕࢆฟຊࡋ⥆ࡅ࡚࠸ࡿࡇ࡜࡞࡝ࡀࢃ࠿ࡗࡓࠋ
ࡇࢀࡽࡢ◊✲࡛ࡣ」㞧࡞ືసࡢ⇍⦎⪅࡜ᮍ⇍⦎⪅ࢆẚ࡭ࡿࡇ࡜࡛㐠ືࡢኚ໬ࢆほᐹࡋ࡚࠸ࡿࠋࡋ࠿ࡋࠊ⇍⦎⪅࡜ᮍ⇍⦎⪅ࢆẚ࡭ࡿሙྜࠊືసࡀ␗࡞ࡗ࡚ࡋࡲ࠺ྍ
⬟ᛶࡀ࠶ࡿࠋࡲࡓࠊ」㞧࡞ືసࡣྍື㛵⠇ࡀከࡃ࡞ࡾࠊ
」ᩘࡢ➽⫗ࡀ౑ࢃࢀࡿࡓࡵ⓶⭵⾲㠃࡛ࡢィ ࢆ⾜࠺⾲㠃➽㟁ಙྕ࡛ࡣィ ࡀ㞴ࡋ࠸࡜⪃࠼ࡽࢀࡿࡓࡵࠊᮏ◊✲࡛ࡣᑡ࡞࠸㛵⠇࡛ࡢ㐠ື࡛࠶ࡾࠊ༢⣧࡞ືసࢆ⾜࠺ᚲせࡀ࠶ࡿࠋ

3. ᐇ㦂ෆᐜ
3.1 ィ ⎔ቃ➽㟁ಙྕࢆィ ࡍࡿࡓࡵ࡟ᑓ⏝ࡢࣁ࣮ࢻ࢙࢘࢔ࢆ⏝࠸࡚ィ ࢆࡍࡿࠋ⮬సࡢ࢔ࣥࣉ࡟ࡼࡗ࡚➽㟁ಙྕࢆIF +] 5 NȐ& X) ࡢ᮲௳࡟࡚ࣁ࢖ࣃࢫࣇ࢕
ࣝࢱࢆ᥃ࡅ  ಸ࡟ቑᖜࡋ࡚ィ ࡋ࡚࠸ࡿࠋࢧࣥࣉࣜ
ࣥࢢ࿘Ἴᩘࡣ N>+]@ࠊ㔞Ꮚ໬ࣅࢵࢺᩘ >ELW@࡛ࢧࣥࣉࣜࣥࢢࢆ⾜࠺ࠋᮏᐇ㦂࡛౑⏝ࡍࡿ㟁ᴟࡣ㖟ᯈࡢୖ࡟࢜࣌࢔ࣥࣉࢆࡘࡅࡓ⮬సࡢ࢔ࢡࢸ࢕ࣈ㟁ᴟ࡛࠶ࡾࠊ཮
ᴟㄏᑟ᪉ᘧ࡟࡚➽㟁ಙྕࡢィ ࢆ⾜࠺ࠋࡲࡓࠊ➽㟁ಙྕࢆィ ࡍࡿ࡜ࡁࠊࣀ࢖ࢬࡀᴟຊ஌ࡾ࡟ࡃ࠸≧ែ࡛ィࡍࡿᚲせࡀ࠶ࡿࡓࡵࠊ7H[WLOH1HW ࡜࿧ࡤࢀࡿᑟ㟁ᕸࢆ⏝࠸࡚ࣀ࢖ࢬࢆ᭱ᑠ㝈࡟ᢚ࠼࡚࠸ࡿ>@ࠋ᪥ᖖࠊ⏕άࡋ࡚࠸ࡿ✵㛫࡟ࡣᦠᖏ㟁ヰࡸ $& ࢔ࢲࣉࢱ࡞࡝࠿ࡽⓎ⏕ࡍࡿ㟁☢Ἴࡸ஺ὶ㟁※࠿ࡽࡢࣀ࢖ࢬ࡞࡝ࡢࡉࡲࡊࡲ
࡞ࣀ࢖ࢬࡀᏑᅾࡍࡿࠋࡇࢀࡽࡢࣀ࢖ࢬࢆ㜵ࡄࡓࡵ࡟㏻
ᖖࡣࢩ࣮ࣝࢻ࣮࣒ࣝ࡟ࡼࡗ࡚ࣀ࢖ࢬࢆ㐽᩿ࡍࡿࠋࡋ࠿
ࡋࠊࢩ࣮ࣝࢻ࣮࣒ࣝෆ࡟࡚సᴗࢆ⾜࠺ࡢࡣᐇ⏝ⓗ࡛ࡣ↓࠸࡜ゝ࠼ࡿࠋࡑࡇ࡛ᕸ࡟ᑟ㟁ᛶ≀㉁ࢆ⦅ࡳࡇࢇࡔᑟ
㟁ᕸࢆ⏝࠸ࡿࡇ࡜࡛ࢩ࣮ࣝࢻ࣮࣒ࣝ࡜ྠ➼ࡢຠᯝࢆฟ
ࡍࡇ࡜ࡀྍ⬟࡜࡞ࡿࠋ
 ᐇ㦂⎔ቃᮏ◊✲ࢆ⾜࠺࡟ᙜࡓࡾࠊ⎔ቃ᮲௳ࡢࡳࢆኚ໬ࡉࡏࡓ
ᐇ㦂⎔ቃࡀᚲせ࡛࠶ࡿࠋࡑࡢࡓࡵࠊࡼࡾ༢⣧࡞ືస࡛⎔ቃࡢࡳࢆኚ໬ࡉࡏࡓ⎔ቃࢆᵓᡂࡍࡿᚲせࡀ࠶ࡿࠋ༢

⣧࡞ືసࢆ⾜࠺ࡇ࡜࡛౑⏝ࡍࡿ➽ࢆῶࡽࡋࠊ௚➽ࡢᙳ㡪ࢆῶࡽࡍࡇ࡜ࡀ࡛ࡁࡿࠋࡲࡓࠊィ ࢆᐜ᫆࡟ࡍࡿࡇ
࡜ࡀ࡛ࡁࡿࠋࡇࡢ᮲௳࠿ࡽᅗ 1 ࡢࡼ࠺࡞ᐇ㦂⎔ቃࢆᵓᡂࡋࡓࠋ1 ヨ⾜ࡢෆᐜࡣ⫝㛵⠇ࡢࡳࢆ⏝࠸࡚┠ࡢ๓ࡢ⌫ࢆᡴᧁࡍࡿࡇ࡜࡛࠶ࡿࠋࢸ࢖ࢡࣂࢵࢡࡣ⾜ࢃࡎᵓ࠼ࡓ≧ែ࠿ࡽᡭࢆ᣺ࡾୗࢁࡍࡢࡳ࡛࠶ࡿࠋ⿕㦂⪅ࡣ⌫ࡢ
ゅᗘࡀ 60 ᗘ࡟࡞ࡿࡼ࠺࡟ᡴᧁࢆ⾜࠺ࠋ⿕㦂⪅ࡣ࡝ࡢᐇ㦂࡛ࡶ┠㞃ࡋࢆࡋ࡚ᡴᧁࢆ⾜࠺ࠋ1 ヨ⾜ࢆ㛤ጞࡋ࡚ 3⛊ᚋ࡟ࣅ࣮ࣉ㡢ࡀ࡞ࡾࠊ⿕㦂⪅ࡣࡑࢀࢆ⪺࠸࡚࠿ࡽᡴ
ᧁࢆ⾜࠺ࠋᐇ㦂⪅ࡣẖヨ⾜ࠊ⿕㦂⪅࡟ᡴᧁᚋࡢゅᗘࢆࣇ࢕࣮ࢻࣂࢵࢡࡍࡿࠋ⫪㛵⠇ࢆ 90 ᗘ࡟ᅛᐃࡋࠊᡭ㤳㛵⠇ࢆࢧ࣏࣮ࢱ࡛ᅛᐃࡋ࡚࠸ࡿࠋࡲࡓࠊᡭඛࢆẼࡢᯈ࡛ᅛᐃࡍࡿࡇ࡜࡛⫝㛵⠇ࡢࡳࢆືసࡉࡏ࡚࠸ࡿࠋᡴᧁࡍ
ࡿ⌫ࡣ 10ࠊ200ࠊ500ࠊ700ࠊ900g ࢆ⏝࠸ࡓࠋᅗ 2 ࡣᐇ㝿࡟⏝࠸ࡓ⌫࡛࠶ࡿࠋᲬࡢ㛗ࡉࠊ⌫ࡢ኱ࡁࡉࡣྠࡌ࡛
࠶ࡿࠋ⌫ࡣᲬ࡟ࡘࡿࡉࢀ࡚࠸ࡿࡓࡵ 1 ᪉ྥ࡟ࡋ࠿ື࠿
࡞࠸ࠋ⿕㦂⪅ࡣ࡝ࡢᐇ㦂࡛ࡶࡑࢀࡒࢀࡢ㔜ࡉࢆ 10 ᅇࡎࡘᡴᧁࡍࡿࠋᮏᐇ㦂࡛ࡣ 3 ྡ(⏨ᛶ 3 ேࠊ22-24 ṓ)ࡢ⿕㦂⪅ࡀཧຍࡋࡓࠋィ ⟠ᡤࡣ୺ື➽࡛࠶ࡿୖ⭎୕㢌➽࡜ᣕᢠ➽࡛࠶ࡿୖ⭎஧㢌➽࡛࠶ࡿࠋࡲࡓࠊᡴᧁࡋࡓࢱ
࢖࣑ࣥࢢࢆィ ࡍࡿࡓࡵ࡟ᅽຊࢭࣥࢧࡶྠ᫬࡟ィ ࡍࡿࠋᅽຊࢭࣥࢧࡣᣦඛ࡟⿦╔ࡍࡿࠋ ࢃ࠿ࡗ࡚࠸ࡿ≧ែ⿕㦂⪅ࡣࡑࢀࡒࢀࡢ㔜ࡉࢆ 10 ᅇࡎࡘᡴᧁࡍࡿࠋࡲࡓࠊ
⿕㦂⪅ࡣྠࡌ㔜ࡉࢆ 10 ᅇᡴᧁࡋࠊᐇ㦂๓࡟ࡣ⦎⩦ࢆ⾜࠺ࠋ⦎⩦ࡣࡲࡎࠊ┠㞃ࡋࢆࡋ࡞࠸≧ែ࡛ 60 ᗘࡲ࡛ᡴᧁ
ࢆࡍࡿࠋࡇࡢ≧ែࢆ 7 ᅇ㐃⥆࡛ᡂຌࡍࡿࡇ࡜ࡀ࡛ࡁࡓ
ࡽḟ࡟┠㞃ࡋࢆࡋࡓ≧ែ࡛ 60 ᗘࡲ࡛ᡴᧁࢆࡍࡿࠋࡇࡢ
≧ែ࡛ࡶ 60 ᗘࡲ࡛ᡴᧁࡍࡿࡇ࡜ࡀ࡛ࡁࡓࡽィ ࢆ㛤ጞࡍࡿࠋ࡝ࡢ㔜ࡉ࡛ࡶᚲࡎィ ๓࡟⦎⩦ࢆ⾜࠸ࠊ㔜ࡉ࡟⭎ࡀ࡞ࢀࡓ≧ែ࡛ᡴᧁࢆ⾜࠺ࠋࡇࢀࡽࡢࡇ࡜࠿ࡽ⿕㦂⪅ࡣ⌫ࡢ㔜ࡉࢆ⌮ゎࡋࠊ࡝ࡢࡼ࠺࡟྇ࡅࡤ 60 ᗘ࡟࡞ࡿ࠿ࢆࢃ࠿ࡗ࡚࠸ࡿ≧ែ࡛ᐇ㦂ࢆ⾜࠺࡜࠸࠼ࡿࠋ
 ࢃ࠿ࡽ࡞࠸≧ែࡇࡢ≧ែ࡛ࡶ⿕㦂⪅ࡣࡑࢀࡒࢀࡢ㔜ࡉࢆ 10 ᅇࡎࡘᡴᧁࡍࡿࠋࡋ࠿ࡋࠊ⌫ࡣẖヨ⾜ࣛࣥࢲ࣒࡟ኚ໬ࡍࡿࠋ
⿕㦂⪅ࡣࣛࣥࢲ࣒࡟ኚ໬ࡍࡿ⌫ࢆ 60 ᗘࡲ࡛ᡴᧁࡍࡿࠋ
⌫ࡢ㔜ࡉࡀࢃ࠿ࡽ࡞࠸≧ែ࡛⌫ࢆ 60 ᗘࡲ࡛ᡴᧁࡍࡿ
ࡇ࡜ࡣ㠀ᖖ࡟㞴ࡋ࠸ࠋࡑࡢࡓࡵࠊࡶࡋ⿕㦂⪅ࡀᡴᧁࢆࡋࡓ࡜ࡁ࡟ 60 ᗘ࡟࡞ࡽ࡞ࡃ࡚ࡶ 1 ヨ⾜࡜⪃࠼࡚ḟࡢヨ⾜ࢆ⾜ࡗ࡚࠸ࡃࠋࡋ࠿ࡋࠊ⿕㦂⪅࡟ࡣᖖ࡟⌫ࢆ 60 ᗘ࡟ࡍࡿࡼ࠺࡟ព㆑ࡋ࡚ࡶࡽࡗࡓࠋᐇ㦂⪅ࡣ඲ヨ⾜࡛⌫ࢆධࢀ᭰࠼ࡿືసࢆ⾜ࡗ࡚ࡶࡽ࠸ࠊ㡢࡞࡝࡛㔜ࡉࡀኚࢃࡗ࡚࠸࡞࠸ࡇ࡜ࡀ⿕㦂⪅࡟ࢃ࠿ࡽ࡞࠸ࡼ࠺࡟㓄៖ࡋࡓࠋࡇࡢᐇ㦂ࡣࢃ࠿ࡗ࡚࠸ࡿ≧ែࡢᚋ࡟⾜࠺ࠋ஦๓ࡢ⦎⩦ࡣ࡞࠸ࠋ㔜ࡉࡀᖖ࡟ኚ໬ࡋ࡚ࠊ஦๓⦎⩦ࡶ⾜ࢃ࡞࠸ࡇ࡜࠿ࡽࡇࡢᐇ㦂࡛ࡣ࡝ࡢࡼ࠺࡟྇ࡅࡤ 60 ᗘ࡟࡞ࡿ࠿

−  −

第15回人間情報学会講演会ポスターセッション（2013.9.19）

ᅗ 3 10g ࡢ⤖ᯝFig. 3 Result by RMS of 10g

ᅗ 4 200g ࡢ⤖ᯝFig. 4 Result by RMS of 200gࡀ୍ษண᝿࡛ࡁ࡞࠸≧ែ࡛ࡢᐇ㦂࡛࠶ࡿ࡜࠸࠼ࡿࠋ
 ゎᯒ᪉ἲ
➽㟁ಙྕࢆほᐹࡍࡿࡓࡵ࡟ᡴᧁࡋࡓ᫬㛫ࢆ࠶ࢃࡏࡿᚲせࡀ࠶ࡿࠋࡑࡢࡓࡵࠊᮏᐇ㦂࡛ࡣᅽຊࢭࣥࢧࢆᣦඛ
࡟⿦╔ࡋࡓࠋ1 ヨ⾜ࡢ୰࡛ࡢᅽຊࢭࣥࢧࡢ್ࡢ୰࡛ഴ
ࡁࡀ᭱ࡶ㧗࠸᫬้ࢆᡴᧁ᫬้࡜ࡋࡓࠋᡴᧁ᫬้ࢆᇶ‽࡟๓ 500 ࣇ࣮࣒ࣞࠊᚋ 500 ࣇ࣮࣒ࣞࡢྜィ 1000 ࣇ࣮࣒ࣞࢆษࡾฟࡍࠋ10 ヨ⾜ࡍ࡭࡚ࡢಙྕࢆほᐹࡍࡿࡓࡵ࡟
ᮏ◊✲࡛ࡣ RMS ࢆ⏝࠸ࡓࠋRMS ࡣ௨ୗࡢࡼ࠺࡟ồࡵࡿࠋRMS(t) = ඨ

1 ் ଶන ݁ (‫ ݐ‬+ ߬)݀‫ݐ‬2ܶ ି்e ࡣ⾲㠃➽㟁ಙྕࠊT ࡣィ⟬⠊ᅖ࡛࠶ࡿࠋT ࡣ 30 ࣇ࣮࣒࡛ࣞ࠶ࡿࠋRMS ࡣಙྕࡢ≉ᚩࢆᤊ࠼ࡿࡓࡵ࡟⏝࠸ࡓࠋࡇࡢ RMS ࢆ 10 ヨ⾜ࡍ࡭࡚࡟⾜࠸ࠊྠ᫬࡟⾲♧ࡉࡏࡿࡇ࡜࡛ࡑࢀࡒࢀࡢ≧ែࡸ㔜ࡉ࡟ࡼࡿ≉ᚩࢆほᐹࡋ࡚࠸ࡃࠋ4. ⤖ᯝ
ᅗ 3 ࠿ࡽᅗ 7 ࡣࡑࢀࡒࢀࡢ㔜ࡉࡢ⤖ᯝ࡛࠶ࡿࠋᕥഃ

ࡀࢃ࠿ࡗ࡚࠸ࡿ≧ែࠊྑഃࡀࢃ࠿ࡽ࡞࠸≧ែࡢ⤖ᯝ࡛࠶ࡿࠋ᭱ึ࡟㔜ࡉࡈ࡜࡟⤖ᯝࢆぢ࡚࠸ࡃࠋᡴᧁࡋࡓ▐㛫ࡀ 0 ⛊࡛࠶ࡿࠋ10gࠊ200g ࠿ࡽほᐹࡋ࡚࠸ࡃࠋࢃ࠿
ࡗ࡚࠸ࡿ≧ែࡢ࡜ࡁࠊ୺ື➽ࡀ 0 ⛊ࡼࡾࡶ๓࡟άືࡋ
࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡿࠋᣕᢠ➽ࡣᡴᧁ┤ᚋ࠿ࡽᡴᧁᚋ
0.05 ⛊࡯࡝ࡲ࡛άືࡋ࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡿࠋࡇࡢࡇ࡜
࠿ࡽࡶࢃ࠿ࡗ࡚࠸ࡿ≧ែ࡛ࡣ 0 ⛊ࡼࡾࡶ๓࡟ຊࢆධࢀ
࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡿࠋࡲࡓࠊᡴᧁࡋࡓ▐㛫࡟ᣕᢠ➽ࢆάືࡉࡏ࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡿࠋࢃ࠿ࡽ࡞࠸≧ែ࡛ࡣ୺ື➽ࡣᡴᧁᚋ 0.03 ⛊ࡲ࡛άືࢆ⥆ࡅ࡚࠾ࡾࠊ0.05 ⛊ࡲ࡛࡟ࡣάືࢆ⾜ࢃ࡞ࡃ࡞ࡿࠋࡲࡓࠊRMS ࡢ኱ࡁࡉࡶࢃ
࠿ࡗ࡚࠸ࡿ≧ែࡼࡾࡶ኱ࡁࡃάືࡋ࡚࠸ࡿࡇ࡜ࡀࢃ࠿
ࡿᣕᢠ➽ࡣ 0 ⛊࠿ࡽ 0.04 ⛊ࡲ࡛ࢃ࠿ࡗ࡚࠸ࡿ≧ែ࡜ྠᵝ࡟άືࡋ࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡿࡀࠊ0.05 ⛊࡯࡝࠿ࡽ෌
ࡧ኱ࡁࡃᣕᢠ➽ࡀάືࡋ࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡿࠋRMS ࡢ኱ࡁࡉࡶ 0 ⛊࠿ࡽ 0.04 ⛊ࡲ࡛ࡢάືࡣࢃ࠿ࡗ࡚࠸ࡿ≧ែ࡜ྠࡌ⛬ᗘࡢ኱ࡁࡉࡔࡀࠊ0.05 ⛊࠿ࡽࡢάືࡣ኱ࡁ
ࡃ࡞ࡗ࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡿࠋࡇࡢࡇ࡜࠿ࡽࡶࢃ࠿ࡽ࡞
࠸≧ែ࡛ࡣᡴᧁᚋࡶຊࢆධࢀ⥆ࡅ࡚࠾ࡾࠊᣕᢠ➽ࡣᡴᧁࢆࡋࡓ▐㛫࡜ᡴᧁᚋࡢ 2 ᅇ཰⦰ࡋ࡚࠸ࡿࡇ࡜ࡀࢃ࠿
ࡿࠋࡇࡢ 2 ࡘࡢ≧ែࡢ⤖ᯝ࠿ࡽࢃ࠿ࡗ࡚࠸ࡿ≧ែ࡛ࡣ−  −

第15回人間情報学会講演会ポスターセッション（2013.9.19）

ᅗ 5 500g ࡢ⤖ᯝFig. 5 Result by RMS of 500g

ᅗ 6 700g ࡢ⤖ᯝFig. 6 Result by RMS of 700g
ࡍ࡛࡟㔜ࡉࡀࢃ࠿ࡗ࡚࠸ࡿࡓࡵࠊᡴᧁ๓࡟ᚲせ࡞ຊࢆฟࡋ࡚ᡴᧁࡋ࡚࠾ࡾࠊᡴᧁᚋࡣຊࢆฟࡉ࡞࠸࡜࠸࠺ࡇ
࡜ࡀࢃ࠿ࡗࡓࠋࡲࡓࠊࢃ࠿ࡽ࡞࠸≧ែ࡛ࡣ㔜ࡉࡀࢃ࠿ࡽ࡞࠸≧ែ࡛ᡴᧁࢆࡍࡿࡓࡵࠊ⌫࡟ᡭࡀᙜࡓࡿ▐㛫ࡲ࡛⌫ࡢ㔜ࡉࡀࢃ࠿ࡽࡎࠊᡴᧁᚋࡲ࡛ຊࢆฟࡋ⥆ࡅ࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡿࠋᣕᢠ➽ࡣ୧≧ែ࡛ᡴᧁࡋࡓ▐㛫࡟཰⦰ࢆࡋ࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡿࠋࡲࡓࠊࢃ࠿ࡽ࡞࠸≧ែ࡛
ࡢࡳ 0.05 ⛊࡛෌ࡧ኱ࡁࡃᣕᢠ➽ࢆ཰⦰ࡉࡏ࡚࠸ࡿࡇ࡜
ࡀࢃ࠿ࡿࠋḟ࡟ 500gࠊ700g ࡢ⤖ᯝࢆほᐹࡋ࡚࠸ࡃࠋࢃ࠿ࡗ࡚࠸
ࡿ≧ែࡢ࡜ࡁࠊ10ࠊ200g ࡜ྠᵝ࡟୺ື➽ࡀᡴᧁ๓࡟ά
ືࡋࠊᣕᢠ➽ࡣᡴᧁ┤ᚋ࠿ࡽᡴᧁᚋ 0.05 ⛊࡯࡝ࡲ࡛ά
ືࡋ࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡿࠋࡲࡓࠊ୺ື➽ࡣ 10gࠊ200g
ࡼࡾࡶ኱ࡁࡃάືࡋ࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡿࠋࡇࡢࡇ࡜࠿
ࡽࢃ࠿ࡗ࡚࠸ࡿ≧ែ࡛ࡣ 10gࠊ200g ࡜ྠᵝࡢ≉ᚩࡀᚓࡽࢀࡿࡇ࡜ࡀࢃ࠿ࡗࡓࠋࡑࡋ࡚ 10gࠊ200g ࡼࡾࡶ୺ື
➽ࡀ኱ࡁࡃάືࡋ࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡿࠋࢃ࠿ࡽ࡞࠸≧ែ࡛ࡣ୺ື➽ࡣ 0.03 ⛊࡯࡝ࡲ࡛άືࢆ⥆ࡅ࡚࠾ࡾࠊࡑ
ࡇ࠿ࡽ෌ࡧ኱ࡁࡃ཰⦰ࡋ࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡿࠋᣕᢠ➽ࡣࢃ࠿ࡗ࡚࠸ࡿ≧ែ࡜࡯ࡰྠᵝࡢάືࢆࡋ࡚࠸ࡿࡇ࡜

ࡀࢃ࠿ࡿࠋࡇࡢࡇ࡜࠿ࡽࡶ 10gࠊ200g ࡜ྠᵝ࡟ᡴᧁᚋࡶ୺ື➽ࡀ཰⦰ࡋ࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡿࠋࡋ࠿ࡋࠊ10gࠊ
200g ࡜ࡣ␗࡞ࡾࠊ0.05 ⛊࠶ࡓࡾ࡛෌ࡧ୺ື➽ࡀ኱ࡁࡃάືࡋ࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡿᣕᢠ➽ࡣ≧ែࡈ࡜ࡢኚ໬ࡀ
࡞ࡃࠊάືࡋ࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡿࠋࡇࡢ 2 ࡘࡢ≧ែࡢ⤖ᯝ࠿ࡽࢃ࠿ࡗ࡚࠸ࡿ≧ែ࡛ࡣ 10gࠊ200g ࡜ྠᵝࡢάືࢆ⾜ࡗ࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡿࠋࢃ࠿ࡽ࡞࠸≧ែࡣ 10gࠊ200g ࡛ࡣᣕᢠ➽ࡀᡴᧁᚋ 0.05 ⛊࡯࡝࡛෌ࡧάືࡋ࡚
࠸ࡓࡀࠊ500gࠊ700g ࡛ࡣ୺ື➽ࡀ෌ࡧ኱ࡁࡃάືࡋ࡚
࠸ࡿࡇ࡜ࡀࢃ࠿ࡿࠋ
ḟ࡟ 900g ࡢ⤖ᯝࢆほᐹࡋ࡚࠸ࡃࠋ900g ࡛ࡣࢃ࠿ࡗ
࡚࠸ࡿ≧ែࠊࢃ࠿ࡽ࡞࠸≧ែࡢ୧᪉࡛୺ື➽ࡀᡴᧁᚋࡲ࡛άືࢆ⾜࠸ࠊᡴᧁᚋࡢ 0.05 ⛊࡯࡝࡛෌ࡧ኱ࡁࡃά
ືࢆࡍࡿࡇ࡜ࡀࢃ࠿ࡗࡓࠋࢃ࠿ࡗ࡚࠸ࡿ≧ែࡼࡾࡶࢃ
࠿ࡽ࡞࠸≧ែࡢ࡯࠺ࡀ෌ࡧάືࡋࡓ࡜ࡁࡢ RMS ࡀ኱ࡁࡃฟ࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡿࠋᣕᢠ➽ࡣ≧ែࡈ࡜ࡢኚ໬
ࡣ↓࠸ࡇ࡜ࡀࢃ࠿ࡿࠋࡇࡢࡇ࡜࠿ࡽࡶ 900g ࡛ࡣ୧≧ែࡢᕪࡀ࡞ࡃ࡞ࡗ࡚╔࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡿࠋ
௨ୖࡢࡇ࡜࠿ࡽ⿕㦂⪅ࡀ⌫ࡢ㔜ࡉࢆ▱ࡗ࡚࠸ࡿ≧ែ࡜▱ࡽ࡞࠸≧ែ࡛ࡣྠࡌືసࢆ⾜ࡗ࡚࠸ࡿࡀࠊ➽άື−  −

第15回人間情報学会講演会ポスターセッション（2013.9.19）

ᅗ 7 900g ࡢ⤖ᯝFig. 7 Result by RMS of 900gࡀ኱ࡁࡃ␗࡞ࡿࡇ࡜ࡀࢃ࠿ࡗࡓࠋࢃ࠿ࡗ࡚࠸ࡿ≧ែ࡛ࡣ୺ື➽ࡢ➽άືࡀ㔜ࡉ࡟కࡗ࡚ᚎࠎ࡟኱ࡁࡃ࡞ࡗ࡚
࠸ࡿࡇ࡜ࡀࢃ࠿ࡿࠋࡲࡓࠊᡴᧁ๓࡟୺ື➽ࡀ཰⦰ࡋ࡚
࠸ࡿࡇ࡜ࡶࢃ࠿ࡿࠋᣕᢠ➽ࡣᡴᧁࡋࡓ▐㛫࡟➽άືࢆ⾜࠺ࡇ࡜ࡀࢃ࠿ࡗࡓࠋࡇࡢࡇ࡜࠿ࡽࡶࢃ࠿ࡗ࡚࠸ࡿ≧ែ࡛ࡣࡍ࡛࡟㔜ࡉࢆ▱ࡗ࡚࠸ࡿࡓࡵࠊᚲせ࡞ຊࢆᡴᧁ
๓࠿ࡽฟࡋ࡚ᡴᧁࢆ⾜࠺ࡇ࡜ࡀࢃ࠿ࡗࡓࠋࡋ࠿ࡋࠊ900g
ࡢࡼ࠺࡞㔜ࡓࡍࡂࡿ≧ែ࡟࡞ࡿ࡜ᡴᧁ๓ࡢࡔࡅ࡛ࡣ཰
⦰ࡀ㊊ࡾ࡞࠸ࡓࡵᡴᧁᚋࡶᚲせ࡞ຊࢆฟࡋ࡚࠸ࡿࡇ࡜
ࡀࢃ࠿ࡗࡓࠋࢃ࠿ࡽ࡞࠸≧ែ࡛ࡣ㔜ࡉ࡟ᛂࡌ࡚ຊࡢฟ
ࡋ᪉ࡀኚ໬ࡍࡿࡇ࡜ࡀࢃ࠿ࡗࡓࠋ࡝ࡢ㔜ࡉ࡛ࡶ୺ື➽ࡣ㔜ࡉࡀࢃ࠿ࡽ࡞࠸ࡓࡵᡴᧁᚋࡲ࡛ຊࢆฟࡋ⥆ࡅࠊ㔜ࡉࡀࢃ࠿ࡗࡓ࡜ྠ᫬࡟୺ື➽ࡢ཰⦰ࢆࡸࡵࡿࠋࡑࡋ࡚ࠊ
㔜ࡉࡀ㍍࠸࡜ࡁ࡟ࡣᣕᢠ➽ࢆᙉࡃ཰⦰ࡉࡏ࡚ࠊ㔜ࡉࡀ㔜ࡓ࠸࡜ࡁ࡟ࡣ୺ື➽ࢆ෌ࡧᙉࡃ཰⦰ࡉࡏࡿࡇ࡜ࡀࢃ
࠿ࡗࡓࠋ900g ࡛ࡶ㔜ࡉࡀ㔜ࡓ࠸࡜ࡁ࡜ྠᵝ࡟୺ື➽ࢆ኱ࡁࡃ཰⦰ࡉࡏ࡚࠸ࡿࡀࠊࢃ࠿ࡗ࡚࠸ࡿ≧ែ࡛ࡶྠᵝࡢ⤖ᯝࡀᚓࡽࢀ࡚࠸ࡿࠋࡲࡓࠊࢃ࠿ࡽ࡞࠸≧ែ࡛ࡣᡴᧁ๓ࡢ୺ື➽࡟㔜ࡉ࡟ᛂࡌࡓኚ໬ࡀ↓࠸ࡇ࡜ࡶࢃ࠿ࡗ
ࡓࠋࡉࡽ࡟ࠊᡴᧁ๓ࡢ୺ື➽ࡣ࡝ࡢ㔜ࡉ࡛ࡶ㠀ᖖ࡟ࡤࡽࡘࡁࡀ኱ࡁࡃࠊࢃ࠿ࡗ࡚࠸ࡿ≧ែࡢ 900g ࡯࡝ࡢ኱ࡁ
ࡉ࡛཰⦰ࡍࡿࡇ࡜ࡶ࠶ࢀࡤ 500g ࡯࡝ࡢ኱ࡁࡉ࡛཰⦰
ࡍࡿࡇ࡜ࡶ࠶ࡿࠋࡋ࠿ࡋࠊ10gࠊ200g ࡯࡝ࡢ኱ࡁࡉ࡛
཰⦰ࡍࡿࡇ࡜ࡣ↓࠿ࡗࡓ௚ࡢ⿕㦂⪅ࡶ࡯ࡰྠᵝࡢ⤖ᯝࡀᚓࡽࢀ࡚࠸ࡿࠋࡋ࠿
ࡋࠊ900g ࡢࡼ࠺࡞ಙྕࡀᚓࡽࢀࡿ㔜ࡉࡀ⿕㦂⪅࡟ࡼࡗ࡚␗࡞ࡿࡇ࡜ࡀࢃ࠿ࡗࡓࠋ௚ࡢ⿕㦂⪅࡛ࡣ 500gࠊ700g
࠿ࡽࢃ࠿ࡗ࡚࠸ࡿ≧ែ࡜ࢃ࠿ࡽ࡞࠸≧ែࡢᕪࡀ࡞ࡃ࡞
ࡗ࡚⾜ࡃࡇ࡜ࡀࢃ࠿ࡗࡓࠋ୍ᗘᕪࡀ࡞ࡃ࡞ࡿ࡜ࡑࢀ௨
ୖࡢ㔜ࡉ࡛ࡶࢃ࠿ࡗ࡚࠸ࡿ≧ែ࡜ࢃ࠿ࡽ࡞࠸≧ែࡢᕪࡀ࡞ࡃ࡞ࡿࡇ࡜ࡀࢃ࠿ࡗࡓࠋᅗ 3 ࠿ࡽᅗ 6 ࡲ࡛ࡢ 10
࠿ࡽ 700g ࡢࡼ࠺࡞⤖ᯝࡣࡑࢀ௨ୗࡢ㔜ࡉ࡛ฟࡿࡇ࡜
ࡀࢃ࠿ࡗࡓࠋ

5. ⪃ᐹ
௒ᅇࡢᐇ㦂⤖ᯝ࠿ࡽ⌫ࡢ㔜ࡉࢆ▱ࡗ࡚࠸ࡿሙྜ࡜▱ࡽ࡞࠸ሙྜ࡛ࡣᡴᧁᚋࡢᑐฎࡢ௙᪉ࡀ␗࡞ࡿࡇ࡜ࡀࢃ
࠿ࡗࡓࠋࢃ࠿ࡗ࡚࠸ࡿ≧ែ࡛ࡣᡴᧁ๓࡟➽ࡢ཰⦰ࢆ⾜࠸ࠊࢃ࠿ࡽ࡞࠸≧ែ࡛ࡣᡴᧁᚋࡲ࡛཰⦰ࡍࡿࡀ㔜ࡉ࡟ᛂࡌ࡚➽ࡢ཰⦰ࢆኚ໬ࡉࡏࡿࡇ࡜ࡀࢃ࠿ࡗࡓࠋࡲࡓࠊ
ࢃ࠿ࡽ࡞࠸≧ែ࡛ࡣ⌫ࡀ㍍࠸࡜ࡁࠊᡴᧁᚋ࡟ᣕᢠ➽ࢆ኱ࡁࡃ཰⦰ࡉࡏࠊ⌫ࡀ㔜ࡓ࠸࡜ࡁࡣᡴᧁᚋ࡟ᣕᢠ➽ࢆ኱ࡁࡃ཰⦰ࡉࡏࡿࡇ࡜ࡀࢃ࠿ࡗࡓࠋࡇࢀࡽࡢ⤖ᯝ࠿ࡽ࿘ࡾࡢ⎔ቃ᝟ሗࢆࡍ࡛࡟▱ࡗ࡚࠸ࡿሙྜࠊ⎔ቃ᝟ሗ࡟࠶ࢃࡏ࡚ືసࢆ⾜࠺ࡇ࡜ࡀ⪃࠼ࡽࢀࡿࠋࢃ࠿ࡗ࡚࠸ࡿ
≧ែࡢᐇ㦂࡛ࡣ஦๓࡟ఱᗘࡶ⦎⩦ࢆ⾜࠸ࠊ⿕㦂⪅⮬㌟
ࡀఱࢆ྇ࡃࡢ࠿ࢃ࠿ࡗ࡚࠸ࡿ≧ែ࡛ᐇ㦂ࢆ⾜࠺ࠋࡲࡓࠊ
⿕㦂⪅ࡣ┠㞃ࡋࢆࡋ࡞ࡀࡽᡴᧁࢆࡍࡿࡀࠊ┠㞃ࡋࢆࡋࡓ≧ែ࡛ࡶ⦎⩦ࢆ⾜࠺ࡓࡵ࿘ࡾࡀぢ࠼࡞ࡃ࡚ࡶ⌫ࢆ60 ᗘࡲ࡛྇ࡅࡿࡼ࠺࡟࡞ࡿࡲ࡛⦎⩦ࢆࡋ࡚࠸ࡿࠋࡇࡢ
ࡇ࡜࠿ࡽࡶ࿘ࡾࡢ⎔ቃࢆ▱ࡗ࡚࠸ࡿ≧ែ࡛ືసࢆ⾜࠺࡜๓ࡶࡗ࡚ᚲせ࡞ຊࢆฟࡋ࡚ືసࢆ⾜࠺࡜⪃࠼ࡽࢀࡿࠋ
ࡋ࠿ࡋࠊ஦๓࡟࿘ࡾࡢ᝟ሗࢆ▱ࡗ࡚࠸ࡿሙྜ࡛ࡶᑐ㇟
ࡢ≀ࡀ㔜ࡓࡍࡂࡿሙྜࠊ๓ࡶࡗ࡚ฟࡏࡿຊࢆ㉸࠼࡚ࡋࡲ࠺ࡓࡵࡑࡢᚋࡶ෌ࡧຊࢆฟࡋ࡚ᚲせ࡞ຊࢆ⿵᏶ࡋ࡚
࠸ࡿ࡜⪃࠼ࡽࢀࡿࠋ๓ࡶࡗ࡚ฟࡏࡿຊࡣே࡟ࡼࡗ࡚␗
࡞ࡿࡓࡵᡴᧁᚋ࡟୺ື➽ࡢάືࡀ࠶ࡿ㔜ࡉࡀ⿕㦂⪅࡟ࡼࡗ࡚␗࡞ࡿࡢ࡛ࡣ࡞࠸࠿࡜⪃࠼ࡽࢀࡿࠋ
ḟ࡟࿘ࡾࡢ≧ែࡀࢃ࠿ࡽ࡞࠸ሙྜࢆ⪃࠼ࡿࠋᅇࡾࡢ
᝟ሗࡀࢃ࠿ࡽ࡞࠸ሙྜࠊ᝟ሗࡀࢃ࠿ࡗࡓᚋ࡟ࡑࡢ᝟ሗ
࡟ᛂࡌࡓືసࢆ⾜࠺ࡇ࡜ࡀ⪃࠼ࡽࢀࡿࠋ௒ᅇࡢ⤖ᯝ࡛ࡣᡴᧁ๓ࡢ୺ື➽࡟ࡤࡽࡘࡁࡀ࠶ࡿࡇ࡜ࡀࢃ࠿ࡗࡓࠋ
ࡑࡋ࡚ࠊࡑࡢࡤࡽࡘࡁࡣࢃ࠿ࡗ࡚࠸ࡿ≧ែࡢ 500g ࠿ࡽ
900g ࡢ㔜ࡓ࠸≧ែ࡛ࡢ኱ࡁࡉ࡜࡞ࡗ࡚࠸ࡿࠋࡇࡢࡇ࡜
࠿ࡽࡶ࿘ࡾࡢ᝟ሗࢆ▱ࡽ࡞࠸ሙྜࠊ࠶ࡿ⛬ᗘࡢ㔜ࡉ࡟ᑐࡋ࡚㐺ᛂ࡛ࡁࡿࡼ࠺࡟࠶ࡽ࠿ࡌࡵᙉࡵࡢຊࢆฟࡋ࡚
࠸ࡿࡇ࡜ࡀ⪃࠼ࡽࢀࡿࠋ⌫ࡀ㍍࠸ 10gࠊ200g ࡢ࡜ࡁࡣᡴᧁ๓ࡢຊࡀᙉࡍࡂࡿࡓࡵࠊᡴᧁᚋ࡟ຊࢆᙅࡵࡿᚲせࡀ࠶ࡿࠋࡑࡢࡓࡵᡴᧁᚋ࡟ᣕᢠ➽ࢆᙉࡃ཰⦰ࡉࡏࠊࣈ
࣮ࣞ࢟ࢆ࠿ࡅ࡚࠸ࡿࡢ࡛ࡣ࡞࠸࠿࡜⪃࠼ࡽࢀࡿࠋࡲࡓࠊ
⌫ࡀ㔜࠸ 500ࠊ700ࠊ900g ࡢ࡜ࡁࡣᡴᧁ๓ࡢຊࡀ㊊ࡾ࡞ࡃ࡞ࡿࡓࡵᡴᧁᚋ࡟୺ື➽ࢆࡉࡽ࡟཰⦰ࡉࡏ࡚ຊࢆฟ
ࡍࡇ࡜࡛┠ᶆ࡛࠶ࡿ 60 ᗘࡲ࡛⌫ࢆᡴᧁࡍࡿࡇ࡜ࡀ࡛
ࡁࡿࡢ࡛ࡣ࡞࠸࠿࡜⪃࠼ࡽࢀࡿࠋࡑࡢࡓࡵࠊ500g ࡛ࡣᡴᧁᚋࡢ཰⦰ࡀ㉳ࡇࡿヨ⾜࡜㉳ࡇࡽ࡞࠸ヨ⾜ࡀ࠶ࡿࠋ700gࠊ900g ࡛ࡣࡍ࡭࡚ࡢヨ⾜࡛ᡴᧁᚋࡢ཰⦰ࡀ⾜ࢃࢀ࡚࠸ࡿࠋ−  −

第15回人間情報学会講演会ポスターセッション（2013.9.19）
ࢃ࠿ࡽ࡞࠸≧ែ࡛ࡢᡴᧁᚋࡢ୺ື➽ࠊᣕᢠ➽ࡢ཰⦰ࡣ⓶㉁⤒⏤཯ᑕ࡟ࡼࡗ࡚⾜ࢃࢀࡓࡢ࡛ࡣ࡞࠸࠿࡜⪃࠼ࡽࢀࡿࠋ⓶㉁⤒⏤཯ᑕ࡜ࡣ཯ᑕᘪ࡟኱⬻⓶㉁ࡢ㐠ື㔝ࡶྵࡲࢀ࡚࠸ࡿ㛗࠸࣮ࣝࣉࡢ཯ᑕ࡛࠶ࡿ[6]ࠋࡑࡢࡓࡵ
ఙ㛗཯ᑕࡼࡾࡶ㐜ࡃ཯ᛂࡍࡿࠋࡇࡢ཯ᑕࡢᙺ๭ࡣ㈇Ⲵ
⿵ൾ࡜࠸ࢃࢀ࡚࠾ࡾࠊ㐠ື୰࡟ᛴ࡟㈇Ⲵࡀኚࢃࡗࡓ࡜
ࡁ࡟ࡑࡢኚ໬ࢆ࢟ࣕࢵࢳࡋ࡚ࠊࡑࢀ࡟ぢྜ࠺ศࡔࡅ㐠ື㔝ࡢάືࢆ㧗ࡵࠊ➽άືࢆ┒ࢇ࡟ࡍࡿࡇ࡜࡛࠶ࡿ[6]ࠋ
ࡇࡢࡇ࡜࠿ࡽࡶᮏᐇ㦂ࡢ⌫ࡀእ஘࡟࠶ࡓࡾࠊࢃ࠿ࡽ࡞
࠸≧ែ࡛ࡣ⓶㉁⤒⏤཯ᑕࡀ㉳ࡇࡿࡇ࡜࡟ࡼࡗ࡚㐺ษ࡞ຊ࡟ࢥࣥࢺ࣮࡛ࣟࣝࡁࡿࡼ࠺࡟ࡋ࡚࠸ࡓࡢ࡛ࡣ࡞࠸࠿
࡜⪃࠼ࡽࢀࡿࠋ
ᮏ◊✲࡛ࡣ࿘ࡾࡢ⎔ቃࢆ▱ࡿࡇ࡜࡛➽⫗ࡢ౑࠸᪉ࡀ
ኚ໬ࡍࡿࡇ࡜ࢆ♧ࡍࡇ࡜ࡀ࡛ࡁࡓࠋ࿘ࡾࡢ⎔ቃࢆ▱ࡗ࡚࠸ࢀࡤ஦๓࡟ᚲせ࡞ືసࢆ⾜࠸ࠊ▱ࡽ࡞ࡅࢀࡤྠࡌ
⤖ᯝ࡟࡞ࡿࡼ࠺࡟᮲௳࡟ᛂࡌ࡚➽⫗ࡢ౑࠸᪉ࢆኚ໬ࡉ
ࡏࡿࡇ࡜ࡀࢃ࠿ࡗࡓࠋࡇࢀ࡟ࡼࡗ࡚┠ᶆࡢືసࠊ࿘ࡾ
ࡢ⎔ቃࡍ࡭࡚ࢆ⌮ゎࡋࡓ≧ἣ࡜┠ᶆࡢືసࡣ⌮ゎࡋ࡚࠸ࡿࡀ࿘ࡾࡢ⎔ቃࡀࢃ࠿ࡗ࡚࠸࡞࠸≧ἣࡣ➽⫗ࡢ౑࠸᪉ࡀ␗࡞ࡿࡇ࡜ࢆ♧ࡍࡇ࡜ࡀ࡛ࡁࡓࠋࡇࡢ◊✲ࡀⓎᒎࡋ࡚࠸ࡃࡇ࡜࡛ពᅗࡋ࡚⾜ࢃࢀࡓ㐠ື࠿࡝࠺࠿ࢆุู
ࡍࡿࡇ࡜ࡀ࡛ࡁࡿࡢ࡛ࡣ࡞࠸࠿࡜⪃࠼ࡽࢀࡿࠋ┠ᶆࡢືసࠊ࿘ࡾࡢ⎔ቃࢆࡍ࡭࡚⌮ゎࡋ࡚ពᅗⓗ࡟⾜࠺ືస࡜┠ᶆࡢືసࡣࢃ࠿ࡗ࡚࠸ࡿࡀ࿘ࡾࡢ⎔ቃࢆ⌮ゎ࡛ࡁࡎ⮬ศࡢពᅗࡋࡓ࡜࠾ࡾࡢ㐠ືࢆ⾜࠼࡞࠸㐠ືࢆุู࡛ࡁࡿࡢ࡛ࡣ࡞࠸࠿࡜⪃࠼ࡓࠋ㐠ືࢆ⾜࠺࡟࠶ࡓࡗ࡚
ពᅗࡋࡓືస࠿࡝࠺࠿ࡣ㔜せ࡞᝟ሗ࡜࡞ࡿࠋࢫ࣏࣮ࢶ
ࡢሙࢆ⪃࠼ࡿ࡜ࡶࡋືసࢆពᅗࡋ࡚⾜࠺ࡇ࡜ࡀ࡛ࡁࢀ
ࡤ⦎⩦ࢆࡍࡿᚲせࡀ↓࠸ࡀࠊពᅗࡋ࡞࠸࡛ືసࢆ⾜࠺ሙྜࠊྠࡌືసࡀ࡛ࡁࡿࡼ࠺࡟⦎⩦ࡍࡿᚲせࡀ࠶ࡿࠋࡇࡢࡼ࠺࡞≧ែࢆุูࡍࡿࡇ࡜࡛ࡼࡾຠ⋡ⓗ࡞⦎⩦ࢆ⾜࠺ࡇ࡜ࡀ࡛ࡁࡿࡢ࡛ࡣ࡞࠸࠿࡜⪃࠼ࡽࢀࡿࠋ

[3] ⯅㇂ㅬ࿃ࠊ⏣ᕳᘯஅࠊⴗ⏣ኴࠊ᱒ᮏගࠊ໭⏣⪔ྖࠊ➉಴ᏹ᫂ࠊࠕ๢㐨ࡢᘬࡁ㠃࠾ࡼࡧᑠᡭືస࡟࠾ࡅ
ࡿୖ⫥ࡢ཯ᛂ᫬㛫࡜➽άືᵝᘧࠖ㮵ᒇయ⫱኱ᏛᏛ⾡◊✲⣖せࠊpp.33-40ࠊ2007㸬
[4] ᯇୗ೺஧ࠊṊᒣ⚽ࠊࠕዪᏊ࢔࣮ࢳ࣮ࣕࡢᘪᑕᢏ⾡࡜⩦⇍㐣⛬࡟㛵ࡍࡿ◊✲ࠖࠊᐇᢏᩍ⫱◊✲ࠊVol.14ࠊ
pp.49-47ࠊ2000㸬
[5] ᪂ᮧ㐩 ࠊ⛅⏣⣧୍ ࠊḈἑ⦾ࠊᡞ⏣┿ᚿ ࠕᑟ㟁ᛶ⾰᭹ࡢࢩ࣮ࣝࢻຠᯝ࡜㟁※౪⤥ᶵ⬟ࢆ⏝࠸ࡓ㧗⢭ᗘከⅬ⾲㠃➽㟁఩ ᐃࢩࢫࢸ࣒ࠖࠊ᝟ሗฎ⌮Ꮫ఍ㄽᩥㄅࠊVol.48ࠊNo.12ࠊ pp㸬3784-3792ࠊ2007㸬
[6] ᯇἼㅬ୍ࠊෆ⸨ᰤ୍ࠕ᭱᪂㐠ື࡜⬻̿యࢆື࠿ࡍ
⬻ࡢ࣓࢝ࢽࢬ࣒̿ࠖࠊࢧ࢖࢚ࣥࢫ♫ࠊ2000

6. ࡲ࡜ࡵ
ᮏ◊✲࡛ࡣேࡀ࿘ࡾࡢ⎔ቃࢆ▱ࡗ࡚࠸ࡿ࠿࡝࠺࠿࡛
㐠ືࡀኚ໬ࡍࡿࡢ࡛ࡣ࡞࠸࠿࡜⪃࠼࡚⾜ࡗࡓᐇ㦂⤖ᯝࢆほᐹࡋࡓࠋ࿘ࡾࡢ⎔ቃࢆ▱ࡗ࡚࠸ࡿ࠿࡝࠺࠿ࡔࡅࢆ
ኚ࠼ࡓ༢⣧࡞ᐇ㦂ࢆ⏝ពࡋࠊィ ࡍࡿࡇ࡜࡛⎔ቃࡢኚ໬ࡀࡶࡓࡽࡍ㐠ືኚ໬ࡢࡳࢆほᐹࡋࡓࠋࡑࡢ⤖ᯝࠊ࿘ࡾࡢ⎔ቃࢆࢃ࠿ࡗ࡚࠸ࡿ≧ែ࡛ࡣ஦๓࡟ᚲせ࡞ࡔࡅ➽⫗ࢆ཰⦰ࡉࡏ࡚㐠ືࡋ࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡗࡓࠋࢃ࠿ࡽ
࡞࠸≧ែ࡛ࡣ࠶ࡿ⛬ᗘ኱ࡁ࠸ຊࢆⓎ⏕ࡉࡏ࡚ᡴᧁࢆ⾜࠺ࡀࡑࡢ࡜ࡁࡢ⌫ࡢ㔜ࡉ࡟ᛂࡌ࡚ຊࡢ౑࠸᪉ࢆኚ໬ࡉ
ࡏ࡚࠸ࡿࡇ࡜ࡀࢃ࠿ࡗࡓࠋࡇࡢࡼ࠺࡞཯ᛂࡣ⓶㉁⤒⏤཯ᑕ࡟ࡼࡗ࡚㉳ࡇࡗ࡚࠸ࡿࡢ࡛ࡣ࡞࠸࠿࡜⪃࠼ࡽࢀࡿࠋ
௒ᚋࡣࡇࡢࡼ࠺࡞཯ᑕࡀᮏᙜ࡟⓶㉁⤒⏤཯ᑕ࡛࠶ࡿ
ࡢ࠿ࢆㄪ࡭ࠊ⿕㦂⪅ࢆቑࡸࡋ࡚࠸ࡃࡇ࡜ࢆ┠ᶆ࡟ࡍࡿࠋ
ࡲࡓࠊࡇࢀࡽࡢಙྕࡢุู᪉ἲࢆ᳨ウࡋࠊࡇࡢಙྕࡀ
ពᅗࡋࡓ≧ែࢆ⾲ࡍࡇ࡜ࡀ࡛ࡁࡿ࠿ࢆ෌ᗘᐇ㦂ࡋ࡚࠸ࡃࠋ      ᩥ⊩
[1] ධᓥ࿴௦ࠊᮌ❳ᚭࠊᏲᒇ㈗᪊ࠊỈ㔝ᗣᩥࠊࠕண
ࢆక࠺ሙ㠃࡛ࡢ⮬㌿㌴㐠ື᫬⏕యᶵ⬟ࡢኚ໬ࠖࠊ
㟁Ꮚ᝟ሗ㏻ಙᏛ఍ᢏ⾡◊✲ሗ࿌ࠊVol.101ࠊNo.533ࠊ
pp㸬97-104ࠊ2001㸬
[2] ᑠụᗣᬕࠊὥᛶᐶࠊబ⸨ㄔࠊࠕ」ᩘࡢຍ㏿ᗘ⎔ቃ࡛ࡢ࣮࣎ࣝ࢟ࣕࢵࢳࣥࢢࢱࢫࢡ࡟࠾ࡅࡿࢱ࢖࣑ࣥࢢண ࠖࠊ㟁Ꮚ᝟ሗ㏻ಙᏛ఍ᢏ⾡◊✲ሗ࿌ࠊ
Vol.105ࠊNo.341ࠊpp㸬27-32ࠊ2005㸬−  −

第15回人間情報学会講演会ポスターセッション（2013.9.19）

࿘⏘ᮇ࡟࠾ࡅࡿ┦஫స⏝࡟㛵ࡍࡿ◊✲▮㔝⨾⣖ 1∦᱒⚈㞞 2

1 ┴❧ᗈᓥ኱Ꮫ ࠛ723-0053 ᗈᓥ┴୕ཎᕷᏛᅬ⏫ 1-1
2 ⊂㸧᝟ሗ㏻ಙ◊✲ᶵᵓ࣭⬻᝟ሗ㏻ಙ⼥ྜ◊✲ࢭࣥࢱ࣮/⚄ᡞ኱Ꮫಖ೺Ꮫ◊✲⛉ࣜࣁࣅࣜࢸ࣮ࢩࣙࣥ⛉Ꮫ㡿ᇦ㐃ᦠㅮᗙ    
ࠛ565-0871 ኱㜰ᗓ྿⏣ᕷᒣ⏣ୣ 1-4

ᴫせ⫼ᬒ࡜┠ⓗ㸸ᡂ⇍ඣ࡜పฟ⏕య㔜ඣࢆᣢࡘẕぶࡢ⫱ඣ⾜ື࡬ࡢ㐪࠸ࢆ᫂ࡽ࠿࡟ࡋࡓࠋ
᪉ἲ㸸ฟ⏘ᚋ 3~4 ࠿᭶ࡢẕぶ࡟ࠊ⫱ඣ⾜ືࡸࢫࢺࣞࢫ࡟ࡘ࠸࡚ࡢព㆑ㄪᰝࢆ⾜ࡗࡓࠋ⤖ᯝ㸸పฟ⏕య㔜ඣࡢẕぶࡢ᪉ࡀࠊ⫱ඣ࡟⑂ࢀ࡚࠾ࡾ᪥ᖖⓗ࡟ࢫࢺࣞࢫࢆឤࡌ࡚࠸ࡓࠋ
⪃ᐹ㸸పฟ⏕య㔜ඣࡢ⫱࡚࡟ࡃࡉࠊᑐฎࡢ㞴ࡋࡉࡀẕぶࡢ⫱ඣࢆᅔ㞴࡟ࡉࡏࠊ᪥ࠎࡢ⫱ඣࡢ୰࡛࢖ࣛ࢖ࣛࡋࡓࡾࠊ
⑂ປࡀ✚ࡳ㔜࡞ࡗࡓ⤖ᯝࠊ᭷ព࡟ࢫࢺࣞࢫ࡟ᙳ㡪ࡋࡓ࡜⪃࠼ࡽࢀࡿࠋ
⤖ㄽ㸸ẕぶࡢ⫱ඣࢫࢺࣞࢫࡀඣࡢᡂ㛗Ⓨ㐩ࠊ≉࡟ゝㄒⓎ㐩ࡢ㐜ࢀࡢྍ⬟ᛶࡀ࠶ࡿࠋ
࣮࣮࢟࣡ࢻ㸸 ⫱ඣࢫࢺࣞࢫ ẕᛶ⾜ື◊✲┠ⓗ                              ࿘⏘ᮇ࡟࠾ࡅࡿ㐺ษ࡞ẕᏊ㛫ࡢ┦஫స⏝ࡣᗂඣࡢⓎ㐩ࡢࡳ࡞ࡽࡎẕぶࡢ QOL ྥୖ࡟ࡶ㔜せ࡛࠶ࡿࠋᮏ◊✲ࡣࠊẕᏊ࡜ࡶ࡟ཷࡅࡿࢫࢺࣞࢫࡀ┦஫స⏝࡟ࡼࡾ࡝ࡢࡼ࠺࡟ኚ໬ࡍࡿࡢ࠿ࢆ⚄⤒⏕⌮Ꮫⓗ࡟ゎ᫂ࡍࡿࡇ࡜
࡟ࡼࡾࠊ㐺ษ࡞ẕᛶ⾜ື࡟㛵ࡍࡿᣦ㔪ࢆ᫂ࡽ࠿࡟ࡍࡿ
ࡇ࡜ࢆ┠ⓗ࡜ࡍࡿࠋ

◊✲᪉ἲᮇ㛫㸸ᖹᡂ㸯㸰ᖺ㸴᭶㸵᪥㹼ᖹᡂ㸯㸰ᖺ㸷᭶㸱㸮᪥᪋タ㸸㹉኱Ꮫ་Ꮫ㒊㝃ᒓ⑓㝔   㹆⑓㝔᪂⏕ඣࢭࣥࢱ࣮ 㹒ಖ೺ࢭࣥࢱ࣮
᪉ἲ㸸㉁ၥ⣬ἲᑐ㇟㸸ձฟ⏕య㔜ࡀ J ௨ୖ J ᮍ‶࡛㏥㝔ᚋ    㸱㹼㸲ࣧ᭶⤒㐣ࡋࡓஙඣࢆᣢࡘẕぶ ྡ    ղฟ⏕య㔜ࡀ J ௨ୖࡢ㸲ࣧ᭶ඣࢆᣢࡘ    ẕぶ ྡᅗ 2 ẕぶࡢ⫱ඣ࡟ᑐࡍࡿホ౯᭷ពᕪࡀぢࡽࢀࡓࠕᢪࡃࠖ㢖ᗘ࡛ࡸ᝟⥴཯ᛂ࡟ࡘ࠸
࡚ 2 ⩌࡟ศࡅࠊẕᛶ⾜ືࢆヲ⣽࡟ẚ㍑ࡋࡓ㸦ᅗ 3㸧ࠋẕぶࡀኌࢆ࠿ࡅࡿ㢖ᗘࡀᑡ࡞ࡅࢀࡤࠊ኱ࡁ࡞ኌࢆฟࡍ཯
ᛂࡀᑡ࡞࠿ࡗࡓࠋ
኱ࡁ࡞ኌࢆฟࡍ㻌⤖ᯝẕぶࡢ᝟⥴ⓗ཯ᛂ࡜ࡋ࡚ࠊࠕぢࡘࡵࡿࠖࠕᚤ➗ࡳ࠿ࡅࡿࠖࠕኌࢆ࠿ࡅࡿࠖ
ࠕᢪࡃࠖࠕἽ࠸ࡓ᫬ᑐฎࡍࡿࠖ5 㡯┠࡜ࡋࡓࠋࡑࡢ୰࡛ࠊᢪࡃ㡯┠࡟᭷ពᕪࡣࡳࡽࢀࡓ㸦ᅗ
1㸧ࠋపฟ⏕య㔜ඣࡢẕぶࡢ᪉ࡀࠊᢪࡃ㢖ᗘࡀ㧗࠿ࡗࡓ
㸦P㸺0.01㸧ࠋ
ᅗ 3 ẕᏊ㛫ࡢ┦஫స⏝⫱ඣ࡟ᅔ㞴ࡉࡀ኱ࡁࡅࢀࡤࠊẕぶࡢࢫࢺࣞࢫࡶẚ౛ࡋ⫱ඣ⾜ື࡟⌧ࢀࠊࡉࡽ࡟ඣࡢⓎ⫱Ⓨ㐩࡟ᙳ㡪ࡍࡿ࡜⪃࠼ࡽࢀࡿࠋ௒ᚋࡢㄢ㢟ࡣ࡜ࡋ࡚ࠊ୍ぢṇᖖ࡜ᛮࢃࢀ
ࡿẕᛶ⾜ືࡢ⫼ᬒࢆ⚄⤒⏕⌮Ꮫⓗ࡟᫂ࡽ࠿࡟ࡍࡿ㸦⪏ࢫࢺࣞࢫ཯ᛂ࡜࡞ࡗ࡚࠸࡞࠸࠿ࠊṇᖖ࡜ぢࡏ࠿ࡅ࡚࠸
࡞࠸࠿㸧ࠋࡲࡓࠊஙඣࢫࢺࣞࢫࢆ ECG,EEG ➼࡟ࡼࡾᐈほⓗ࡟ホ౯ࡋ࡚࠸ࡃᚲせ࠶ࡿࠋ         ᩥ⊩
ᅗ 1 ẕぶࡢ᝟⥴ⓗ཯ᛂẕぶ࡟࡜ࡗ࡚᪥ࠎࡢ⫱ඣࢆ᣺ࡾ㏉ࡗ࡚ࠊ࢖ࣛ࢖ࣛࡋ
࡚࠸ࡿ⛬ᗘࡸ‶㊊ᗘ࡟ࡘ࠸࡚ẚ㍑ࡋࡓ⤖ᯝࠊᅗ 2 ࡟♧
ࡍ㏻ࡾࠊ‶㊊ᗘࡢ᭷ពᕪࡀࡳࡽࢀࡓࠋపฟ⏕య㔜ඣࡢẕぶࡢ᪉ࡀ‶㊊ᗘࡀప࠿ࡗࡓ㸦P㸺0.01㸧ࠋ>@▮㔝⨾⣖㸪͆ஙඣᮇ࡟࠾ࡅࡿẕぶࡢ⫱ඣ⾜ື࡟㛵ࡍࡿ◊✲㸫పฟ⏕య㔜ඣࢆฟ⏘ࡋࡓẕぶ࡜ᡂ⇍ඣࢆฟ⏘ࡋࡓẕぶࡢẚ㍑㸫͇㸪ẕᛶ⾨⏕➨  ᕳ㸦㸧㸪SS㸬̺㸪㸬>@▮㔝⨾⣖㸪͆ ࠿᭶ඣࢆᣢࡘẕぶࡢ⫱ඣ⾜ື࡜ஙඣࡢⓎ㐩ࡢ㛵㐃㸫ẕぶࡢࢫࢺࣞࢫࢆ୰ᚰ࡟㸫͇㸪Ᏻ⏣ዪᏊ኱Ꮫ኱Ꮫ㝔ᩥᏛ◊✲⛉⣖せ➨  㞟㸪SS㸬̺㸪㸬>@▮㔝⨾⣖㸪͆ஙᗂඣࡢゝㄒⓎ㐩࡜ẕぶࡢ⫱ඣ⾜ື࡟㛵ࡍࡿ◊✲㸫⏕ᚋ  ࠿᭶࠿ࡽ  ࠿᭶ࡲ࡛ࡢẕᏊࢆ୰ᚰ࡟㸫͇㸪㢼㛫᭩ᡣ㸪㸬−  −

第15回人間情報学会講演会ポスターセッション（2013.9.19）⬻Ἴ࡟ࡼࡿࢫࢺࣞࢫ࣭⑊③࣭ᛌ㐺ᛶไᚚ∦᱒⚈㞞
᝟ሗ㏻ಙ◊✲ᶵᵓ࣭⬻᝟ሗ㏻ಙ⼥ྜ◊✲ࢭࣥࢱ࣮
ࠛ656-0871 ኱㜰ᗓ྿⏣ᕷᒣ⏣ୣ 1-4ᴫせ┠ⓗ㸸⑊③ୗ࡟࠾ࡅࡿᚋ㢌㒊⬻ἼȘ㸰ᖏᇦ㸦10㹼13Hz㸧ᙉᗘࡢ≉ᚩ࠿ࡽᛌ୙ᛌࢆ㚷ูࡍࡿᢏ⾡ࡢ☜❧ࢆ┠ᣦࡍࠋ᪉ἲ㸸⦆៏࡞⑊③(dull pain)่⃭ࢆ༳ຍࡋࠊ⮬ᕫ⏦࿌࡟ࡼࡿ⑊③ᗘ࡜ᚋ㢌㒊⬻ἼȘ㸰ᖏᇦᙉᗘ࡜ࡢ㛵ಀࢆᐃ㔞ホ౯ࡍࡿࠋᐇ㦂᮲௳ࡣ௨ୗࡢ࡜࠾ࡾ㸸⿕㦂⪅㸸೺ᖖ࡞ⱝᖺᡂே
⑊③༳ຍ㸸ᐮ෭᪼ᅽヨ㦂ἲ(cold pressure test, CPT)ᚋ㢌㒊⬻ἼȘ㸰ᖏᇦᙉᗘࢆኚㄪࡍࡿ᪉ἲ㸸࢔࣐ࣟࢭࣛࣆ࣮㸦࢔࣐ࣟࢱࢵࢳ㸧࡟ࡼࡿࠋ⤖ᯝ㸸 ᚋ㢌㒊⬻ἼȘ㸰ᖏᇦᙉᗘ࡟ᑐࡋ࡚ CPT(5 ศ)ᚋࡢ⑊③ᗘࡣ㹔Ꮠᆺࡢ≉ᚩࢆ♧ࡋࠊ᭱ᑠ⑊③ᗘࡣ⑊③ᗘ㸮ࡢ↓③≧ែ(analgesia)࡟㐩ࡋࡓࠋ⑊③ᗘࡀᢚไࡉࢀࡓ≧ែ࡛ࠊᚋ㢌㒊⬻ἼȘ㸰ᖏᇦᙉᗘᦂࡽࡂࡢ⦆ࡸ࠿࡞ኚືᡂศ
(0.1Hz ㏆ഐ)ࡣపୗࡋ࡚࠸ࡓࠋ୍᪉ࠊᚋ㢌㒊⬻ἼȘ㸰ᖏᇦᙉᗘᦂࡽࡂࡢ㏿࠸ኚືᡂศࡣ㐠ື࡟ྠᮇࡋ࡚ฟ⌧ࡋࡓࠋࡇࢀࡽࡢᐇ㦂⤖ᯝ࠿ࡽࠊᚋ㢌㒊⬻ἼȘ㸰ᖏᇦᙉᗘᦂࡽࡂࡢ⦆ࡸ࠿࡞ኚືᡂศࡣෆ㒊࡟ὀពࡀྥࡃ tonic ࡞῝㒊⬻άືࢆ཯ᫎࡍࡿ୍᪉ࠊ㏿࠸ᡂศࡣ⿕่⃭ᛶࡢ phasic ࡞άືࢆ཯ᫎࡍࡿࡶࡢ࡜᥎ᐹࡉࢀࡓࠋ
⤖ㄽ㸸 㐺ษ࡞࿘Ἴᩘᖏᇦ࡛ᘚูࡉࢀࡓᚋ㢌㒊⬻ἼȘ㸰ᖏᇦᙉᗘኚືࡣ⑊③࡟㝶కࡍࡿᛌ୙ᛌᗘࢆ཯ᫎࡍࡿྍ⬟ᛶࡀ࠶ࡿࠋ࣮࣮࢟࣡ࢻ㸸 ⬻Ἴ ᚋ㢌㒊⬻Ἴ ῝㒊⬻άື ⑊③ ᛌ୙ᛌࢢࣛࣥࢺ㸸New Energy and Industrial Technology Development Organization     Grants-in-Aid for Scientific Research (25420236, 23593274) promoted by Japan Society for the Promotion of

Scienceᛌ୙ᛌࡣ⑊③ឤࡸ Quality of Life(QOL)ࢆࡢ
ࡳ࡞ࡽࡎ〇ရࡸࢧ࣮ࣅࢫ࡟ᑐࡍࡿಶࠎேࡢ‶㊊ᗘࢆホ౯ࡍࡿᣦᶆ࡛࠶ࡾࠊ⏕⌮ᣦᶆࢆᇶ┙࡜ࡍࡿᐃ㔞ホ౯ἲࡢ☜❧ࡀᮃࡲࢀ࡚࠸ࡿࠋᮏㄽᩥ
࡛ࡣ⮬Ⓨ⬻Ἴ࠿ࡽ⡆౽࡟ᛌ୙ᛌ≧ែࢆᐈほⓗ࡟㚷ูࡍࡿᢏ⾡ࢆ☜❧ࡍࡿࡇ࡜ࢆ┠ⓗ࡜ࡋࡓࠋᚋ㢌㒊࡟ฟ⌧ࡍࡿ⮬Ⓨ⬻ἼࡢȘ㸰ᖏᇦ㸦10
㹼13Hz㸧ࡢᙉᗘࡣୖ㒊⬻ᖿࢆ୰ᚰ࡜ࡍࡿ῝㒊⬻άືᗘࢆ཯ᫎࡍࡿࡇ࡜ࡀሗ࿌ࡉࢀ࡚࠸ࡿ
[1-3]ࠋᮏㄽᩥ࡛ࡣࡇࡢᢏ⾡ࢆᇶ┙࡟ࠊ⑊③ᗘ࡜῝㒊⬻άື࡜ࡢᐃ㔞ⓗ㛵ಀ࠿ࡽ⑊③ឤࢆ୰ᚰ࡜ࡍࡿᛌ୙ᛌࡢᐃ㔞ⓗホ౯ࡀྍ⬟࡛࠶ࡿ࠿ࢆ
ᐇ㦂᳨ドࡋࡓࠋ ⊩ୖ⿕㦂⪅ࢆᑐ㇟࡜ࡋࠊ10Υࡢ෭Ỉ࡟ࡼࡿᐮ෭᪼ᅽヨ㦂(CPT)[4]࡟ࡼࡾᨃఝⓗ៏ᛶ⑊③่
⃭ࢆ 5 ศ㛫∦ᡭ࡟༳ຍࡋࠊ⑊③ᗘ࡜⬻Ἴࢆྠ᫬࡟ ᐃ ࡋ ࡓ ࠋ ⑊ ③ ᗘ ࡣ 0-10 numerical scale
ratings (NSR)࡟ࡼࡗࡓࠋ⮬Ⓨ⬻Ἴࡣᅜ㝿 10-20ἲ࡟‽ᣐࡋࡓࢩࢫࢸ࣒㸦Nexus32㸧ࢆ⏝࠸࡚グ㘓ࡋࡓࠋ ᚋ㢌㒊(O1, O2)⬻ἼȘ㸰ᖏᇦ㸦10㹼13Hz㸧ࡢᖹᆒᙉᗘ࡜ࡋ࡚ᐃ⩏ࡉࢀࡿᇶᖿ⬻άືᣦᩘ
(FBA㸦Fundamental Brain Activity 㸧index)ࢆࣃ
࣓࣮ࣛࢱ࡜ࡍࡿ CPT ࡢ᭱⤊⑊③ᗘࡣ㹔ᆺࡢ≉ᚩࢆ࿊ࡋࡓࠋࡉࡽ࡟⑊③ᗘࡢ᭱ᑠࡣ↓③≧ែ
(analgesia)࡟⮳ࡗࡓ㸦ᅗ 1㸧ࠋࡇࡢ࡜ࡁᚋ㢌㒊⬻ἼȘ㸰ᖏᇦᙉᗘࡣࠊ↓③≧ែ㏆ഐ࡛ 0.1Hz ࢆ୰ᚰ࡜ࡍࡿᖏᇦࡀῶᑡࡍࡿഴྥ࡟࠶ࡗࡓ㸦ᅗ㸰㸧ࠋ

䐟
䐤
䐡

䐣

䐢 䐠

ᅗ 1 ᇶᖿ⬻άືᣦᩘ(FBA index)࡜᭱⤊⑊③ᗘ࡜ࡢ㛵
ಀ

䐟

䐡

䐠

䐢

ᅗ㸰 ᚋ㢌㒊⬻ἼȘ㸰ᙉᗘࡢ࿘Ἴᩘ≉ᛶ ୍᪉ࠊCPT 㐙⾜୰ࠊ⿕㦂⪅ࡢⓎヰ࡟㝶కࡋ
㏿࠸ᙉᗘኚືᡂศࢆ᭷ࡍࡿ⬻Ἴࡀฟ⌧ࡋࡓࠋࡇࡢᡂศࡣ୺࡟ࣂ࣮ࢫࢺⓗ࡞⬻Ἴ࡛࠶ࡾࠊࡑࡢᖏ
ᇦࡣȘ㸰࠿ࡽș㸯㸦13㹼21Hz㸧࡟ཬࢇࡔ㸦ᅗ−  −

第15回人間情報学会講演会ポスターセッション（2013.9.19）

㸱㸧ࠋᚋ㢌㒊Ș㸰ᙉᗘ࡟╔┠ࡋࡓሙྜࠊኚືࣆ
࣮ࢡࡣⓎヰ࡜ྠᮇࡋ࡚࠸ࡓ㸦ᅗ㸲㸧ࠋ䠡䠡䠣 䠽䡈䡌䡄䠽䠎 䡌䡋䡓䡁䡎

Declaration ofpain ratings⚄⤒ࡢ phasic ࡞άືࡀᚲ㡲࡛࠶ࡿ࡜ࡍࡿ࡞ࡽ
ࡤࠊᐇ㦂࡛ᚓࡽࢀࡓ⑊③ᗘࡢ㹔Ꮠᆺࡢ≉ᚩ࡜ᩚྜࡍࡿࠋ↓③≧ែ࡛పୗࡍࡿᚋ㢌㒊Ș㸰ᙉᗘࡢ
0.1Hz ㏆ഐࡢ⦆ࡸ࠿࡞ኚືᡂศࡣࠊ⑊③Ⓨ⌧࡟ಀࡿ tonic άືࢆ཯ᫎࡍࡿࡶࡢ࡛࠶ࡾࠊࡑࢀ௨
እࡢ≉࡟㏿࠸࿘Ἴᩘ㡿ᇦࡣ phasic άືࢆ཯ᫎ
ࡍࡿࡶࡢ࡜⪃࠼ࡽࢀࡿࠋ ௨ୖࡢ⪃ᐹ࠿ࡽ⑊③࡟࠾ࡅࡿᛌ୙ᛌࡣᚋ㢌㒊Ș㸰ᙉᗘࡢ࿘Ἴᩘᖏᇦ࡟ࡼࡾᘚู࡛ࡁࡿྍ
⬟ᛶࡀ♧၀ࡉࢀࡿࠋ༶ࡕࠊ⦆ࡸ࠿࡞ኚືᡂศࡣ
⑊③ࡢ୙ᛌឤࢆᢪ࠸ࡓ≧ែࠊ㏿࠸࿘Ἴᩘᡂศࡣ
ࡑࡢ୙ᛌឤࡀపῶࡶࡋࡃࡣᾘኻࡋࡓ≧ែ࡟ࡑࢀࡒࢀᑐᛂࡍࡿࡶࡢ࡜⪃࠼ࡽࢀࡓࠋ
ᩥ⊩

䠰䡅䡉䡁䠄䡉䡅䡊䠅ᅗ㸱 ↓③≧ែ࡟࠾࠸࡚Ⓨヰ࡟ྠᮇࡋ࡚ฟ⌧ࡍࡿᚋ㢌㒊Ș㸰ᙉᗘࡢ㏿࠸ኚືᡂศ

EEGɴϭ;ϭϱ-Ϯϭ,ǌͿɲϮ;ϭϬ-ϭϯϭ,ǌͿɲϭ;ϴ-ϭϬϭ,ǌͿᅗ㸲 Ⓨヰ࡜ྛ✀ᖏᇦ⬻ἼࠋȘ㸰ࠊș㸯ࡣⓎヰ㐠ື࡟ྠᮇࡍࡿࡀȘ㸯ࡣᚲࡎࡋࡶྠᮇࡋ࡚࠸ࡿ࡜ࡣゝ࠼࡞࠸ࠋ CPT ࡟ࡼࡾ༳ຍࡉࢀࡿᨃఝⓗ⑊③ࡣ෭Ỉ࡟ࡼࡿ c ⥺⥔ୖࡢ⑊③ཷᐜჾࢆ่⃭ࡍࡿࡓࡵ៏
ᛶ⑊③࡟㏆࠸㕌࠸⑊③࡛࠶ࡿࠋࡇࡢࡼ࠺࡞㕌③
ࡣどᗋࢆ୰ᚰ࡜ࡍࡿ῝㒊⬻άືᗘࡀ㧗ࡲࡿ࡜పୗࡍࡿࡇ࡜ࡀ▱ࡽࢀ࡚࠸ࡿ[5]ࠋᅗ㸱࡟࠾࠸࡚ FBA index ࡢᑠࡉ࠸㡿ᇦ࡛ࡣࡇࡢ▱ぢ࡜ᩚྜࡍࡿࠋ୍᪉ࠊFBA index ࡀ኱ࡁ࠸㡿ᇦ࡛ࡣ⑊③ᗘࡣ㏫࡟ቑ኱ࡋࠊୖグ▱ぢ࡜ᩚྜࡋ࡚࠸࡞࠸ࠋ
ࡇࡢ⌮⏤࡜ࡋ࡚ CPT ࡀ c ⥺⥔ࡢࡳ࡞ࡽࡎ Aț⥺⥔ࢆ่⃭ࡍࡿࡓࡵ FBA index ࡢ㧗࠸㡿ᇦ࡛⑊③ࡀቑ኱ࡍࡿ࡜ࡶ⪃࠼ࡽࢀࡿࡀࠊࡇࡢ௬ㄝࢆ
ጇᙜ࡜ࡍࡿ࡞ࡽࡤ Aț⥺⥔࡟ࡼࡿ㗦࠸③ࡳࡀ 5
ศ㛫ࡢ CPT ࡢ⤊┙ࡲ࡛⥅⥆ⓗ࡟Ⓨ⌧ࡋ࡚࠸࡞ࡅࢀࡤ࡞ࡽ࡞࠸ࡇ࡜࡟࡞ࡿࠋ୍᪉ࠊFBA index࡟ᑐࡋ࡚㣬࿴≉ᛶࢆ♧ࡍ phasic ࡞άື࡜ᣦᩘ
㛵ᩘⓗ࡟ቑ኱ࡍࡿ tonic ࡞άືࡢᕪ࡜ࡋ࡚ᐃ⩏ࡉࢀࡿ᭷ຠ phasic άືᗘࡣ U Ꮠᆺࡢ≉ᚩࢆ࿊
ࡍࡿ[6]ࠋ⑊③ᢚไ࡟ࡣࡇࡢ῝㒊ࡢࣔࣀ࢔࣑ࣥ

[1] Laufs H, Kleinschmidt A, Beyerle A, Eger E,
Salek-Haddadi A, Preibisch C, Krakow K.:
EEG-correlated fMRI of human alpha activity.
Neuroimage. 19(4), pp. 1463-1476(2003).
[2] Sadato N, Nakamura S, Oohashi T, Nishina E,
Fuwamoto Y, Waki A, Yonekura Y.: Neural networks
for generation and suppression of alpha rhythm: a
PET study. Neuroreport. 9(5), 893-897 (1998)[3] Omata K, Hanakawa T, Morimoto M, Honda M.:
Spontaneous Slow Fluctuation of EEG Alpha Rhythm
Reflects Activity in Deep-Brain Structures: A
Simultaneous EEG-fMRI Study, PLoS One. 2013 Jun
18;8(6):e66869 (2013).
[4] Birnie KA, Petter M, Boerner KE, Noel M, Chambers
CT.: Contemporary use of the cold pressor task inpediatric pain research: a systematic review ofmethods. J Pain. 13(9), pp. 817-26 (2012)[5] Kupers RC, Gybels JM, Gjedde A.: Positron emission
tomography study of a chronic pain patient
successfully treated with somatosensory thalamicstimulation. Pain. 87(3), pp.295-302 (2000)[6] Grace, A.A: The tonic/phasic model of dopamine
system regulation: its relevance for understanding
how stimulant abuse can alter basal ganglia function,
Drug
and Alcohol
Dependence
37, pp.
111-129(1995).

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第15回人間情報学会講演会ポスターセッション（2013.9.19）

ㄒ⏘⏕࡟࠾ࡅࡿ⓶㉁㸫どᗋࢩࢫࢸ࣒࡟ࡘ࠸࡚ࡢ᳨୍ウ㸫JRQRJR ࣃࣛࢲ࢖࣒࡟ᇶ࡙ࡃ⬻Ἴィ ᐇ㦂㸫௒஭⤮⨾Ꮚ 1∦᱒⚈㞞 2 ᕝཪᩄ⏨ 1

1 ⚄ᡞ኱Ꮫ኱Ꮫ㝔ಖ೺Ꮫ◊✲⛉ ࠛ654-0142 රᗜ┴⚄ᡞᕷ㡲☻༊཭ࡀୣ 7-10-2
2 ⊂㸧᝟ሗ㏻ಙ◊✲ᶵᵓ ⬻᝟ሗ㏻ಙ⼥ྜ◊✲ࢭࣥࢱ࣮ ࠛ565-0871 ኱㜰ᗓ྿⏣ᕷᒣ⏣ୣ 1-4

ᴫせ⫼ᬒ࡜┠ⓗ㸸ㄒ⏘⏕࡟ಀࡿ࡜ࡉࢀࡿ⬻㒊఩ࡣ᫂ࡽ࠿࡟ࡉࢀࡘࡘ࠶ࡿࡀࠊࡑࡢືⓗ࡞┦஫స⏝࡟ࡘ࠸࡚ࡣᮍࡔ᫂ࡽ
࠿࡛ࡣ࡞࠸ࠋᮏ◊✲ࡣࠊ᫬㛫ศゎ⬟ࡢ㧗࠸⬻Ἴ(EEG)ࢆ⏝࠸࡚ㄒ⏘⏕୰ࡢ⬻ᶵ⬟ࢿࢵࢺ࣮࣡ࢡࢆ᳨ウࡍࡿࡇ࡜ࢆ┠ⓗ࡜ࡋࡓࠋ᪉ἲ㸸೺ᖖ࣎ࣛࣥࢸ࢕࢔ 2 ྡࢆᑐ㇟࡟ࠊgo/nogo ࡢពᛮỴᐃࢆせࡍࡿ஦㇟㛵㐃㟁఩(ERPs)ㄏⓎࣃࣛࢲ࢖࣒ࢆ⏝࠸ࡓㄒ⏘⏕ㄢ㢟୰ࡢ⬻Ἴィ ࢆ⾜࠸ࠊ⓶㉁ࡢᶵ⬟ࢆ཯ᫎࡍࡿ஦㇟㛵㐃㟁఩࡜ࠊどᗋࢆྵࡴ῝㒊⬻άືᗘࢆ཯ᫎࡍࡿ
ᚋ㢌㒊ඃ఩ࡢDࣃ࣮࣡ࢆゎᯒࡋࡓࠋ⤖ᯝ㸸ᚋ㢌㒊D2 ࣃ࣮࣡࡜ ERPs ࡣ࡜ࡶ࡟่⃭࡟౫Ꮡࡋ࡚ኚㄪࡋࡓࠋࡍ࡞ࢃࡕࠊ୧⪅࡜ࡶ࿨௧่⃭࡟ྥ࠿ࡗ࡚ῶᑡ
ࡋࠊࡑࡢᚋ෌ࡧቑ኱ࡋࡓࠋࡑࡢቑ኱ࡢࢱ࢖࣑ࣥࢢࡣࠊヨ⾜ࡢ඲࡚ࡀ go ᮲௳ࡼࡾࡶࠊgo/nogo ᮲௳࡟࠾࠸࡚㐜ࢀࠊቑ኱ᖜࡣ go ࡼࡾࡶ nogo ࡟࠾࠸࡚኱ࡁ࠿ࡗࡓࠋ
⪃ᐹ㸸࿨௧่⃭๓ࡢ ERPs ࡢῶᑡ㸦㝶కᛶ㝜ᛶ㟁఩㸸CNV㸧࡜࡜ࡶ࡟ㄆࡵࡽࢀࡓᚋ㢌㒊D2 ࣃ࣮࣡ࡢῶᑡ࠾ࡼࡧ࿨
௧่⃭┤ᚋࡢᛴቑࡼࡾࠊㄒ⏘⏕࡟⓶㉁㸫どᗋࢩࢫࢸ࣒㸦⓶㉁㸫῝㒊⬻ᶵ⬟ࢿࢵࢺ࣮࣡ࢡ㸧ࡀ㛵୚ࡍࡿࡇ࡜ࡀ᥎ᐹ
ࡉࢀࡓࠋ⤖ㄽ㸸ㄒ⏘⏕᫬ࡢ⬻Ἴゎᯒ࡟ࡼࡾࠊERPs ࡜ᚋ㢌㒊D2 ࣃ࣮࣡ࡢ่⃭࡟౫Ꮡࡋࡓኚㄪࡀㄆࡵࡽࢀࡓࠋ஦㇟㛵㐃㟁఩࡜࡜ࡶ࡟ᚋ㢌㒊D2 ࣃ࣮࣡ࡢ phasic ࡞ᣲືࢆ♧၀ࡋࡓࡇ࡜࡟᪂つᛶࡀ࠶ࡿࠋ
࣮࣮࢟࣡ࢻ㸸ㄒ⏘⏕ ᚋ㢌㒊D2 ࣃ࣮࣡ ஦㇟㛵㐃㟁఩ go/nogo ⬻Ἴ

1㸬ࡲ࠼ࡀࡁ
㡢ኌゝㄒ࡟ࡼࡿࢥ࣑ࣗࢽࢣ̿ࢩࣙࣥ࡟࠾࠸࡚ࠊ
ࠕヰࡍࠖࡇ࡜ࡣࠕ⫈ࡃࠖࡇ࡜࡜ྠᵝ࡟㔜せ࡛࠶ࡿࠋࡍ࡞ࢃࡕࠊ
௚⪅ࡢⓎヰෆᐜࢆ⌮ゎࡋࠊ⮬ᕫࡢⓎヰࢆᐇ⌧ࡍࡿࠋࡇ
ࡢⓎヰ࡟୙ྍḞ࡞ㄒࡣࠊព࿡ࡢ᳨⣴ࡸࠊ㡢㡩ࡢ᳨⣴࡜㓄ิࠊⓎヰ㐠ືࡢ௻⏬ࢆ⤒࡚⏘⏕ࡉࢀࡿࠋㄒ⏘⏕࡟㛵ಀࡋ࡚㈿άࡍࡿ⬻㒊఩ࡣࠊᶵ⬟ⓗ᰾☢Ẽඹ㬆(fMRI)ࡸ㝧㟁Ꮚᨺᑕ᩿ᒙ᧜ᙳ(PET)ࢆ⏝࠸ࡓ◊✲࡟ࡼࡗ࡚ࠊࡇࢀࡲ࡛࡟᫂ࡽ࠿࡟ࡉࢀ࡚ࡁࡓ[1]ࠋࡋ࠿ࡋࠊ
fMRI ࡸ PET ࡣ✵㛫ศᯒ⬟࡟ඃࢀࡿࡶࡢࡢࠊ᫬㛫ศᯒ⬟ࡀప࠸ࡓࡵࠊㄒ⏘⏕࡟ಀࡿືⓗ࡞⬻ᶵ⬟ࢿࢵࢺ࣮࣡
ࢡࡣᮍࡔゎ᫂ࡉࢀ࡚࠸࡞࠸ࠋ
ᡃࠎࡣࠊ⓶㉁㸫どᗋࢩࢫࢸ࣒㸦⓶㉁㸫῝㒊⬻ᶵ⬟ࢿࢵࢺ࣮࣡ࢡ㸧࡟╔┠ࡋࠊ᫬㛫ศゎ⬟ࡢ㧗࠸⬻Ἴ(EEG)ࢆ⏝࠸ࡿࡇ࡜࡟ࡼࡾࠊពᛮỴᐃࢆせࡍࡿ go/nogo ࣃࣛࢲ࢖࣒࡟ᇶ࡙࠸ࡓㄒ⏘⏕ㄢ㢟୰ࡢ⬻ᶵ⬟ࢿࢵࢺ࣮࣡ࢡࢆ᳨ウࡋࡓࠋ

2㸬᪉ἲㄒ⏘⏕ㄢ㢟ࡣࠊgo/nogo ࣃࣛࢲ࢖࣒࡟ᇶ࡙ࡁࠊgo/nogoྛ 30 ヨ⾜ࢆᵓᡂࡋࡓࠋㄢ㢟ࡢὶࢀࢆᅗ 1 ࡟♧ࡍࠋㄢ㢟

࡟ࡣ 2 ✀㢮ࡢ่⃭ࢆ⏝࠸ࡓ㸦S1ࠊS2㸧ࠋS1 ࡟ࡣ NTT ࢹ࣮ࢱ࣮࣋ࢫࡼࡾᢳฟࡋࡓぶᐦᗘࡢ㧗࠸ᬑ㏻ྡモࢆ᥇⏝ࡋࠊS2 ࡣࣅ࣮ࣉ㡢࡜ࡋࡓࠋࣅ࣮ࣉ㡢ࡣ㧗ప 2 ✀ࢆ⏝࠸ࠊ⿕㦂⪅࡟ㄒ⏘⏕ࡢ go/nogo ࢆ࿨௧ࡋࡓࠋྠ୍ㄢ㢟୰ࡢ
S1-S2 㛫㝸ࡣ➼ࡋࡃタᐃࡋࡓࠋ඲࡚ࡢ⫈ぬ่⃭ࡣࢫࣆ
࣮࢝ࢆ㏻ࡋ࡚෌⏕ࡋࠊ⿕㦂⪅ẖ࡟᭱㐺࡞㡢㔞࡟ㄪ⠇ࡋࡓࠋᐇ㦂࡟ࡣ೺ᖖ࣎ࣛࣥࢸ࢕࢔ዪᛶ 2 ྡ㸦ᖺ㱋㸸29 ṓࠊ
35 ṓ㸧ࡀཧຍࡋࡓࠋ⿕㦂⪅࡟ࡣཱྀ㢌࠾ࡼࡧ᭩㠃࡛ㄝ᫂ࢆ⾜ࡗࡓࠋ⿕㦂⪅ࡣࠊㄢ㢟ࢆ㏻ࡋ࡚㛢║≧ែ࡛࠶ࡗࡓࠋ
Ⓨヰ⾜ືࡢไᚚ࡟⓶㉁㸫῝㒊⬻ᶵ⬟ࢿࢵࢺ࣮࣡ࢡࡀ
㛵୚ࡍࡿ࡜࠸࠺௬ㄝࢆࡶ࡜࡟ࠊㄢ㢟୰ࡢ⬻Ἴࢆᅜ㝿
10-20 ἲ㟁ᴟ㓄⨨ࡢ FzࠊCzࠊPzࠊO1ࠊO2 ࠿ࡽࠊ512ࢧࣥࣉࣜࣥࢢ࿘Ἴᩘ࡛ィ ࡋࠊゎᯒࡋࡓࠋලయⓗ࡟ࡣࠊ㐠ື‽ഛࡸᣢ⥆ⓗὀព࡜࠸ࡗࡓ⓶㉁ᶵ⬟ࢆ཯ᫎࡍࡿ஦
㇟㛵㐃㟁఩(ERPs)࡜ࠊどᗋࢆྵࡴ῝㒊⬻ᶵ⬟ࡢάືᗘࢆ཯ᫎࡍࡿᚋ㢌㒊D2(10-13Hz)ࣃ࣮࣡[2]ࡢ┦㛵ᛶࡢ᳨ウ࡟ࡼࡾࠊ⓶㉁㸫῝㒊⬻ᶵ⬟ࢿࢵࢺ࣮࣡ࢡࡢᶵ⬟ࡢ᥎ᐃࢆヨࡳࡓࠋ ERPs ࠾ࡼࡧᚋ㢌㒊D2 ࣃ࣮࣡ࡣࠊgo/nogo࡛ศࡅࡓྛ 30 ヨ⾜ࡢ㟁఩ࡲࡓࡣࣃ࣮࣡ࢆ่⃭ࡢ࣐࣮࣮࢝ࢆᇶ‽࡟ຍ⟬ᖹᆒࡋ࡚ゎᯒࡋࡓࠋᅗ㸯ㄢ㢟ࡢὶࢀ㸬
Fig. 1 A task stream.

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第15回人間情報学会講演会ポスターセッション（2013.9.19）;ʅsϮͿ

66;ʅsͿ6

increaseɲϮincreasedecreaseCz

decrease

6

CNV*




time (msͿ







time (msͿᅗ㸰 all GO ࢱࢫࢡ࡟࠾ࡅࡿᚋ㢌㒊D2 ࣃ࣮࣡㸦ᕥ㸧࡜ ERPs㸦ྑ㸧ࡢຍ⟬ᖹᆒ⤖ᯝ㸬
Fig. 2 Grand averaged waveforms of occipital alpha2-band power (left) and ERPs (right) during all go task.

ᅗ㸱 go/nogo ࢱࢫࢡ࡟࠾ࡅࡿᚋ㢌㒊D2 ࣃ࣮࣡ࡢຍ⟬ᖹᆒ⤖ᯝ㸬
Fig. 3 Grand averaged occipital alpha-2 band power for verb-generation tasks.

ᅗ㸲 go/nogo ࢱࢫࢡ࡟࠾ࡅࡿ ERPs ࡢຍ⟬ᖹᆒ⤖ᯝ㸦ᕥ㸸᚟ၐࢱࢫࢡ㸪ྑ㸸ືモ⏘⏕ࢱࢫࢡ㸧㸬
Fig. 4 Grand averaged ERPs for verb-generation tasks (left) and simple noun-repeat task (right).

3㸬⤖ᯝᐇ㦂ࡢ⤖ᯝࠊᚋ㢌㒊D2 ࣃ࣮࣡࠾ࡼࡧ ERPs ࡢ่⃭࡟౫Ꮡࡋࡓኚㄪࡀㄆࡵࡽࢀࡓࠋᅗ 2 ࡟඲ 30 ヨ⾜ go ࢱࢫࢡ࡟࠾ࡅࡿᚋ㢌㒊D2 ࣃ࣮࣡ࡢຍ⟬ᖹᆒ⤖ᯝࢆࠊᅗ 3, 4
࡟ go ࢱࢫࢡ࠾ࡼࡧ nogo ࢱࢫࢡྛ 30 ヨ⾜࡟࠾ࡅࡿ⤖ᯝࢆ♧ࡍࠋᚋ㢌㒊D2 ࣃ࣮࣡࡜ ERPs ࡣ S1 ᚋࠊS2 ࡟ྥࡅ࡚ῶᑡࡋࡓ㸦ᅗ 2-4㸧ࠋῶᑡࡢᚋࠊᚋ㢌㒊D2 ࣃ࣮࣡࠾ࡼࡧ ERPs ࡣ෌ࡧቑ኱ࢆ♧ࡋࡓࠋࡑࡢࢱ࢖࣑ࣥࢢࡣ
go/nogo ࢱࢫࢡࡢ᪉ࡀ all go ࢱࢫࢡࡼࡾ㐜ࢀࡓࠋgo/nogoࢱࢫࢡ࡟࠾ࡅࡿ᮲௳࡟ࡼࡿᕪ࡟╔┠ࡍࡿ࡜ࠊS2 ┤ᚋࡢ
D2 ࣃ࣮࣡࠾ࡼࡧ ERPs ࡢቑ኱ࡣ nogo ࡛ࡼࡾ኱ࡁ࠿ࡗ
ࡓࠋࡲࡓࠊP300 ᚋࡢ ERPs ࡣ go ࡛ࡣቑ኱ࡋࡓࡀࠊnogo࡛ࡣ኱ࡁ࡞ቑ኱ࢆㄆࡵ࡞࠿ࡗࡓࠋ

4㸬⪃ᐹ

㢌㒊D2 ࣃ࣮࣡ࡢῶᑡࡣࠊㄒ⏘⏕࡟ྥࡅ࡚ࡢ㐠ື‽ഛ࡜ᣢ⥆ᛶὀពࢆ཯ᫎࡋ࡚࠸ࡿ࡜⪃࠼ࡽࢀࡿࠋࡲࡓࠊS2 ┤ᚋࡢᚋ㢌㒊D2 ࣃ࣮࣡ࡢᛴቑࡣࠊᣢ⥆ᛶὀព࠿ࡽࡢゎᨺࢆ♧ࡋ࡚࠸ࡿྍ⬟ᛶࡀ⪃࠼ࡽࢀࠊㄒ⏘⏕࡟⓶㉁㸫どᗋࢩࢫࢸ࣒㸦⓶㉁㸫῝㒊⬻ᶵ⬟ࢿࢵࢺ࣮࣡ࢡ㸧ࡀ㛵୚ࡍࡿࡇ࡜ࡀ᥎ᐹࡉࢀࡓࠋ        ᩥ⊩
[1] S.E. Petersen, P.T. Fox, M.I. Posner, M. Mintun,
M.E. Raichle, “Positron emission tomographic studies of the cortical anatomy of single-word processing,”
Nature vol. 331, pp.585-589, 1988.
[2] N. Sadato, S. Nakamura, T. Oohashi, E. Nishina, Y.
Fuwamoto, A. Waki, Y. Yonekura, “Neural networks
for generation and suppression of alpha rhythm: a
PET study,” Neuroreport vol.9, pp.893-897, 1998.

S2 ๓ࡢ ERPs ࡟ぢࡽࢀࡓ㝶కᛶ㝜ᛶ㟁఩(CNV)࡜ᚋ

−  −

第15回人間情報学会講演会ポスターセッション（2013.9.19）

ேయ⾲㠃࡜᭹እഃࡢ ‵ᗘẚ㍑࡟ࡼࡿࢿࢵࢡ࢙࢘࢔ࡢຠᯝ ᐃ㧗ᶫ ῟஧ 1ຍ⸨ ඃ㈗ 1 ࣟ࣌ࢬ ࢠ࣮࣒ࣚ 1

1 㟷ᒣᏛ㝔኱Ꮫ⌮ᕤᏛ㒊᝟ሗࢸࢡࣀࣟࢪ࣮Ꮫ⛉ ࠛ252-5258 ⚄ዉᕝ┴┦ᶍཎᕷ୰ኸ༊ῡ㔝㎶ 5-10-1 O Ჷ 220 ᐊᴫせ⫼ᬒ࡜┠ⓗ㸸ேయࢆ┤᥋෭༷ࡍࡿࢿࢵࢡ࢙࢘࢔ࡣࠊẼೃⓗᛌ㐺ᛶࢆಖࡕ࡞ࡀࡽ┬࢚ࢿຠᯝࡀ኱ࡁࡃࠊᗈࡃᬑཬࡍ
ࡿࡇ࡜ࡀᮇᚅࡉࢀ࡚࠸ࡿࠋࡋ࠿ࡋ࡞ࡀࡽࠊ⿦⨨ࢆ㛗᫬㛫౑⏝ࡍࡿ㝿ࡢᐃ㔞ⓗ࡞ホ౯ࡣ༑ศ࡞ࡉࢀ࡚࠸࡞
࠸ࠋᮏ✏࡛ࡣࠊⓎờ᫬㛫ࢆ᥎ᐃࡍࡿࡇ࡜࡛ࢿࢵࢡ࢙࢘࢔ࢆホ౯ࡍࡿ᪉ἲࢆᥦ᱌ࡍࡿࠋ
᪉ἲ㸸 ᑠᆺ ‵ᗘࢭࣥࢧࢆேయ⾲㠃㸦IN㸧࡜᭹እഃ㸦OUT㸧࡟⿦╔ࡋࠊ ‵ᗘࢆィ ࡍࡿࠋIN ࡢ‵ᗘ࡜ OUTࡢ‵ᗘࢆẚ㍑ࡍࡿࡇ࡜࡛Ⓨờ≧ែࢆ᥎ᐃࡍࡿࠋࢿࢵࢡ࢙࢘࢔⿦╔᫬࡜㠀⿦╔᫬࡛ࠊⓎờᮇ㛫ࢆẚ㍑ࡋࢿ
ࢵࢡ࢙࢘࢔ࡢ᭷ຠᛶࢆホ౯ࡍࡿࠋ⿕㦂⪅ࡣ 1 ே࡛ࠊィ ࡣᮅᐙࢆฟࡿ┤๓࠿ࡽኤ᪉࡟ᐙ࡟ᖐࡿࡲ࡛ࡢ༑
ᩘ᫬㛫⾜࠺ࠋ
⤖ᯝ㸸 8 ᭶ࡢ࠺ࡕ 21 ᪥ศࡢィ ࢹ࣮ࢱࢆᚓࡓࠋィ ࢹ࣮ࢱࡢ࠺ࡕẼೃࡀఝ࡚࠾ࡾࠊ࠿ࡘࠊ኱ࡲ࠿࡞⾜ືᒚṔࡀఝ㏻ࡗࡓ᪥࡛ࠊ⿦╔᪥࡜㠀⿦╔᪥ࢆẚ㍑ࡋࡓ࡜ࡇࢁࠊ⿦╔᪥ࡢ᪉ࡀ᥎ᐃⓎờᮇ㛫ࡣ▷ࡃ࡞ࡗࡓࠋ
⪃ᐹ㸸 ࢿࢵࢡ࢙࢘࢔ࡢ⿦╔᪥࡜㠀⿦╔᪥ࡢࢹ࣮ࢱẚ㍑࡛ࡣࠊẼೃࡀఝ࡚࠸ࡿࠊ⾜ືᒚṔࡀఝ㏻ࡗ࡚࠸ࡿ࡜࠸࠺
ᐃᛶⓗ࡞᮲௳࠿ࡽẚ㍑᳨ドࡍࡿࢹ࣮ࢱࢆ㑅ᢥࡋ࡚࠾ࡾࠊ༑ศ࡟බᖹ࡞ẚ㍑ࡀ࡛ࡁ࡚࠸ࡿ࡜ࡣゝ࠸㞴࠸ࠋᐃ㔞ⓗ࡞ᣦᶆࢆᇶ࡟ẚ㍑ࡍ࡭ࡁ᪥ࢆ㑅ᢥࡍࡿᚲせࡀ࠶ࡿࠋࡲࡓࠊ⿕㦂⪅ࡢయㄪ࡟ࡼࡗ࡚Ⓨờࡢࡋ᫆ࡉࡣ
ᙳ㡪ࢆཷࡅࡿࡓࡵࠊࢹ࣮ࢱᩘࢆቑࡸࡋࠊ⤫ィⓗ࡟ẚ㍑ࡍࡿᚲせࡀ࠶ࡿࠋ⤖ㄽ㸸 ࢿࢵࢡ࢙࢘࢔ࡢ⿦╔᪥࡜㠀⿦╔᪥࡜࡛ࠊ᥎ᐃⓎờᮇ㛫ࢆ⏝࠸ࡓⓎờࡢࡋ᫆ࡉࢆẚ㍑ࡋࠊ⿦╔᪥ࡢ᪉ࡀⓎờࡋ࡟ࡃ࠸࡜࠸࠺⤖ᯝࢆᚓࡓࠋࡋ࠿ࡋ࡞ࡀࡽࠊẚ㍑᳨ドࡍࡿ᪥࡟ࡕࡢ㑅ᢥ᪉ἲࡣᐃᛶⓗ࡞᮲௳㸦Ẽೃࠊ⾜ືᒚṔ㸧ࢆ⏝࠸࡚࠸ࡿࡓࡵࠊཝᐦ࡟බᖹ࡞ẚ㍑ࡀ࡛ࡁ࡚࠸ࡿ࡜ࡣゝ࠸㞴࠸ࠋ௒ᚋࡢㄢ㢟࡜ࡋ࡚ࠊẼೃᣦᶆ࡟ࡘ࠸࡚ࡣࠊ ‵ᗘࡢࡳ࡛ࡣ࡞ࡃࠊ‵ᗘࠊᨺᑕࠊẼ ࠿ࡽ࡞ࡿᬬࡉᣦᩘ㸦WBGT: Wet Bulb Globe
Temperature㸧ࡢᑟධࢆ᳨ウࡋ࡚࠸ࡿࠋࡲࡓࠊ⾜ືᒚṔ࡟ࡘ࠸࡚ࡣࠊຍ㏿ᗘࢭࣥࢧᦚ㍕ࡢከᶵ⬟ᦠᖏᆺ➃ᮎࢆ⏝࠸ࡓ୍᪥ࡢ㐠ືᙉᗘࡢィ ࢆ᳨ウࡋ࡚࠸ࡿࠋ
࣮࣮࢟࣡ࢻ㸸

ࢿࢵࢡ࢙࢘࢔ Ⓨờ≧ែ᥎ᐃࡣࡌࡵ࡟

Ⓨờ≧ែࡢ᥎ᐃ

㏆ᖺࠊ┬࢚ࢿࣝࢠ࣮ᨻ⟇ࡢ᥎㐍࡟ࡼࡾኟᏘ࣭෤Ꮨࡢ
㟁ຊ౑⏝ไ㝈ࡀᇳ⾜ࡉࢀࠊࡑࡢ⤖ᯝࠊ㇏ᐩ࡞㟁ຊ࡟ࡼࡾᐇ⌧ࡉࢀ࡚࠸ࡓᛌ㐺⎔ቃࡀᔂቯࡋࠊ೺ᗣ༴ᶵࠊປാ
⏕⏘ᛶపୗ࡞࡝ࡢၥ㢟ࡀ⯆ࡗ࡚࠸ࡿࠋ
ࡇࡢ≧ἣୗ࡛ࠊேయ࡟┤᥋స⏝ࡍࡿࡓࡵ࡟ࠊ┬࢚ࢿࣝࢠ࣮࡛ຠ⋡ࡼࡃேయࢆ෭༷࡛ࡁࡿ࢙࢘࢔ࣛࣈࣝᒁᡤ
෭ᬮᡣࢩࢫࢸ࣒㸦ࢿࢵࢡ࢙࢘࢔㸧ࡀᯈ⏕ࡽ࡟ࡼࡗ࡚㛤Ⓨࡉࢀ[1]ࠊᬑཬࡋࡘࡘ࠶ࡿࠋࡇࢀࡲ࡛࡟ࠊ20 ྡ⛬ᗘࡢ⿕㦂⪅࡟ࡼࡿ࢔ࣥࢣ࣮ࢺホ౯[2]ࡸࠊ5 ྡࡢ⿕㦂⪅࡟ࡼ
ࡿⓎờ㛤ጞᐊ ࡢㄪᰝ[3]࡟ࡼࡾࡑࡢ᭷ຠᛶࡀ♧ࡉࢀ࡚࠸ࡿࠋḟẁ㝵࡜ࡋ࡚ࠊࡼࡾ᪥ᖖ࡟㏆࠸≧ἣ࡛ࠊ༑ศ
࡞ᩘࡢ⿕㦂⪅࡟ࡼࡿຠᯝ ᐃࢆ⾜࠺ᚲせࡀ࠶ࡿࠋࡑࡢ
ࡓࡵ࡟ࡣࠊ⿕㦂⪅ࡀ᪥ᖖ⏕άࢆ࠾ࡃࡾࡘࡘࡶ㛗᫬㛫࡟Ώࡗ࡚㐃⥆ࡋ࡚Ⓨờ≧ែࢆ ᐃࡍࡿࡇ࡜ࢆྍ⬟࡜ࡍࡿᡭἲࡀᚲせ࡛࠶ࡿࠋ
ᮏ✏࡛ࡣࠊ⿕㦂⪅ࡢ᪥ᖖ⏕άࢆ㑧㨱ࡋ࡞࠸᪉ἲ࡛ࠊ
㛗᫬㛫࡟Ώࡗ࡚ࡢ ᐃࢹ࣮ࢱࢆඖ࡟Ⓨờ/㠀Ⓨờࢆุᐃࡍࡿ᪉ἲࢆᥦ᱌ࡍࡿࠋලయⓗ࡟ࡣࠊᑠᆺ ‵ᗘࢭࣥࢧࢆேయ⾲㠃࡜᭹ࡢእഃ࡟タ⨨ࡋࠊࡑࢀࡽࡢ‵ᗘࡢẚ
㍑࠿ࡽேయࡀⓎờ≧ែ࡟࠶ࡿ࠿࡝࠺࠿ࢆ᥎ᐃࡍࡿࠋࡲ
ࡓࠊᥦ᱌ᡭἲ࡟ࡼࡾ᥎ᐃࡉࢀࡓⓎờࡢุᐃ⤖ᯝ࡟ᇶ࡙࠸࡚ࠊ1 ᪥ࡢ࠺ࡕ࡛ࡢⓎờࡋ࡚࠸ࡓᮇ㛫ࡢ๭ྜࢆ♧ࡍⓎờ⋡ࢆ⟬ฟࡍࡿࠋࡑࡋ࡚ࠊⓎờ⋡ࢆ⏝࠸࡚ࠊࢿࢵࢡ
࢙࢘࢔ࡢ᭷ຠᛶࢆ᳨ドࡍࡿࠋ Ⓨờࡢ✀㢮ே㛫ࡀ࠿ࡃờࡣ኱ࡁࡃ㸱ࡘ࡟ศ㢮࡛ࡁࡿ[4]ࠋ
⇕ᛶⓎờࡣࠊ࿘ᅖࡢẼ ࡸ㐠ື࡟ࡼࡾୖ᪼ࡋࡓయ
ࢆୗࡆࡿࡓࡵ࡟࠿ࡃờ࡛ࠊ඲㌟ࡢయ⾲㠃࠿ࡽฟࡿࠋ
࣭⢭⚄ᛶⓎờࡣࠊࢫࢺࣞࢫࡸ⥭ᙇࠊ୙Ᏻࠊ⯆ዧ࡜࠸ࡗࡓ⢭⚄ࡢ≧ែ࡟కࡗ࡚࠿ࡃờ࡛ࠊᡭᤸࡸ㊊ᗏ࠿ࡽฟࡿࠋ
࣭࿡ぬᛶⓎờࡣࠊ㎞ࡳ࣭㓟࿡ࡢᙉ࠸ࡶࡢࢆ㣗࡭ࡓ᫬࡟ฟࡿờ࡛ࠊ㢌ࡸ㢦ࢆ୺࡜ࡋ࡚඲㌟࠿ࡽฟࡿࠋࡇࡢ௚ࠊ
ࢥ࣮ࣄ࣮࡞࡝ࡢ่⃭ࡢᙉ࠸ࡶࡢࡣෆ⮚࡟㈇ᢸࢆ࠿ࡅࡿࡓࡵయ ࡀୖ᪼ࡍࡿࠋ࿡ぬᛶⓎờࡣࠊࡇࡢయ ୖ᪼ࢆᢚ࠼ࡿࡓࡵ࡟࠿ࡃờ࡛࠶ࡾࠊ ⇕ᛶⓎờ࡜⌮⏤ࡣఝ࡚࠸ࡿࠋᮏ◊✲࡛ὀ┠ࡍࡿࡢࡣẼೃⓗᛌ㐺ᛶ࡟㛵㐃ࡢ῝࠸⇕ᛶⓎờ࡛࠶ࡿࡀࠊࡇࢀࢆ௚ࡢཎᅉ࡟ࡼࡿⓎờ࡜᏶඲࡟ศ㞳ࡋ࡚ ᐃࡍࡿࡇ࡜ࡣ㞴ࡋ࠸ࠋࡑࡇ࡛ࠊᐇ㦂࡛ࡣ⿕㦂⪅࡟⢭⚄ᛶࡸ࿡ぬᛶⓎờࡢᙳ㡪ࡀฟ࡞࠸ࡼ࠺ᣦ♧ࡍࡿ࡜࡜ࡶ࡟ࠊ༑ศ࡞ ᐃࢹ࣮ࢱࢆࡶ࡜࡟⤫ィⓗ࡟Ⓨờࢆホ౯ࡍࡿᚲせࡀ࠶ࡿࠋ࣭᭹ෆእࡢ‵ᗘᕪࢆ⏝࠸ࡓⓎờ≧ែࡢ᥎ᐃ
ኟᏘ࣭෤Ꮨࡣ෭ᬮᡣ࡟ࡼࡾᒇෆ࡜ᒇእࡢẼೃࡀ኱ࡁࡃ␗࡞ࡿࠋேࡣ᪥ᖖ⏕άࡢ୰࡛ᒇෆ࡜ᒇእࢆ⾜ࡁ᮶ࡍࡿࡓࡵࠊࡑࡢேࡢ࿘ᅖࡢẼೃࡣ୙つ๎࡟ኚືࡍࡿࠋࡇ
ࡢࡼ࠺࡞≧ἣ࡛ࡣࠊ⎔ቃഃࡢẼೃࢆ ᐃࡋࡘࡘ⿕㦂⪅ࡢᒇෆእࡢฟධࡾࡢグ㘓ࢆ࡜ࡿࡼࡾࡣࠊ࢙࢘࢔ࣛࣈࣝ

−  −

第15回人間情報学会講演会ポスターセッション（2013.9.19）

ᅗ㸯‵ᗘࢭࣥࢧ࣮ࣟ࢞㸦KN ࣛ࣎ࣛࢺ࣮ࣜࢬ㸧࡞ ‵ᗘィࢆ⿕㦂⪅࡟⿦╔ࡋࠊ࿘ᅖࡢẼೃࢆᖖ᫬ ᐃࡋ࡚࠾ࡃ࡜⿕㦂⪅ࡢ㈇ᢸࡀᑡ࡞ࡃ࡚ࡼ࠸ࠋ
ᮏ◊✲࡛ࡣࠊᅗ 1 ࡟♧ࡍᑠᆺࡢ ‵ᗘࢭࣥࢧ࣮ࣟ࢞
ࢆ㤳࠿ࡽୗࡆࠊ⿕㦂⪅࿘ᅖࡢẼೃࢆ ᐃࡍࡿࠋࡇࡢࢭ
ࣥࢧࡣ༑ศ࡟ᑠࡉࡃࠊ㤳࠿ࡽୗࡆࡓ≧ែ࡛ୗ╔ࡢෆഃ࡟ྞࡿࡋ࡚ࡶ࡯ࡰ㐪࿴ឤࡀ࡞࠸ࠋࡑࡇ࡛ࠊୗ╔ࡢෆഃ࡟ࡶ ‵ᗘ࣮ࣟ࢞ࢆタ⨨ࡋࠊ⬗య࿘ࡾࡢయ⾲㠃௜㏆ࡢᗘ࡜‵ᗘࢆ ᐃࡍࡿࠋᐃࡋࡓࢹ࣮ࢱࡢ࠺ࡕࠊ᫬้ t ࡟࠾ࡅࡿࠊ⿕㦂⪅ࡢ᭹ࡢእࡢ ᗘࢆ Tout(t)ࠊ‵ᗘࢆ Hout(t)ࠊୗ╔ࡢෆഃ㸦ࡘࡲࡾయ⾲㠃㸧ࡢ ᗘࢆ Tin(t)ࠊ‵ᗘࢆ Hin(t)࡜ࡍࡿࠋண
ഛᐇ㦂࠿ࡽࠊTin ࡣ Tout ࡢ኱ࡁࡉ࡜ࡣ㛵ಀ࡞ࡃࠊ30Υ
<Tin<35Υ࡛࠶ࡗࡓࠋࡲࡓࠊờࢆ࠿࠸࡚࠸࡞࠸ሙྜࡣࠊ
Hin ࡣ Hout ࡜ྠ఩┦࡛㑄⛣ࡍࡿࡇ࡜ࡀ☜ㄆࡉࢀࡓࠋࡘࡲ
ࡾࠊᒇእ࠿ࡽ෭ᡣࡢຠ࠸ࡓᒇෆ࡟⛣ືࡋࡓሙྜࠊToutࡣୗࡀࡿࡀ Tin ࡣኚືࡋ࡞࠸ࠋᑐࡋ࡚ࠊHout ࡢ㝆ୗ࡟ྜࢃࡏ࡚ Hin ࡣ㝆ୗࡍࡿࠋ୍᪉ࠊờࢆ࠿࠸ࡓሙྜࡣࠊHoutࡢ್࡟㛵ಀ࡞ࡃ Hin ࡀୖ᪼ࡋࠊ኱᢬ࡢሙྜ Hout < Hin ࡜࡞ࡗࡓࠋࡑࡇ࡛ࠊⓎờࢆุᐃࡍࡿ஧ࡘࡢุᐃჾࢆ௨ୗ
ࡢࡼ࠺࡟ᐃ⩏ࡍࡿࠊ­ 1  H out d H in sweating®
¯ 0  H out ! H in not sweating­ 1  0 d D * 'H out d 'H in sweating
SC2 (t ) ®
not sweating¯ 0  others

SC1 (t )

͐(1)
͐(2)

ణࡋࠊ'Hout ࠊ'Hin ࡣࡑࢀࡒࢀࠊ༢఩᫬㛫ᙜࡓࡾࡢᕪ
ศ್ࢆ⾲ࡋࠊDࡣㄪᩚ್࡛ 1.0~2.0 ࡢ್ࢆ࡜ࡿࠋ
ḟ࡟ࠊ඲ ᐃᮇ㛫㸦Tall㸧ࡢ࠺ࡕⓎờࡋ࡚࠸ࡓᮇ㛫ࡢ๭ྜࢆ⾲ࡍⓎờ⋡ࢆ௨ୗࡢࡼ࠺࡟ᐃ⩏ࡍࡿࠊSR1¦ SC (t ) ¦1 *100

͐(3)

1

0 d t d Tall

SR20  t  Tall

¦ SC (t ) ¦ 1 *100ࠋ   ͐(4)

2

Tall

Tall

SR1ࠊࡣ୍᪥ࡢෆ࡛Ⓨờࡋ࡚࠸ࡓᮇ㛫ࡀ㛗࠸ࡇ࡜⾲ࡋࠊSR2 ࡣࠊ୍᪥ࡢෆ࡛ờࢆ࠿ࡁጞࡵࡓᅇᩘࡀ࡝ࡢ⛬ᗘ࠿ࢆ⾲ࡋ࡚࠸ࡿࠋᐇ㦂 ᪉ἲ
⿕㦂⪅ࡢ 1 ᪥ࡢ⾜ືࢆࢥࣥࢺ࣮ࣟࣝࡍࡿࡇ࡜ࡸண
ࡍࡿࡇ࡜ࡣ୙ྍ⬟࡛࠶ࡿࠋࡑࡇ࡛ࠊ7 ᭶୰᪪㹼8 ᭶ᮎࡲ࡛ࠊ࡛ࡁࡿ㝈ࡾẖ᪥ ‵ᗘࢭࣥࢧࢆ⿦╔ࡋィ ࢆ⾜࠺ࠋࡲࡓࠊࢿࢵࢡ࢙࢘࢔ࢆ⿦╔ࡋ࡚ࡢࢹ࣮ࢱࡶྲྀᚓࡍࡿࠋ
ࡑࡋ࡚ࠊ୺࡟⿕㦂⪅࿘ᅖࡢẼೃ࡜኱ࡲ࠿࡞⾜ືᒚṔࢆࡶ࡜࡟ࠊࢿࢵࢡ࢙࢘࢔⿦╔᪥࡜㠀⿦╔᪥ࢆ㑅ᢥࡋࠊࡑࢀࡒࢀࡢࢹ࣮ࢱࢆẚ㍑ࡍࡿࠋᅗ 2 ࢿࢵࢡ࢙࢘࢔⿦╔᪥ࡢ

‵ᗘ

ᐃ⤖ᯝ

ᅗ 3 ࢿࢵࢡ࢙࢘࢔㠀⿦╔᪥ࡢ

‵ᗘ

ᐃ⤖ᯝ

 ⤖ᯝ
ᅗ 2,3 ࡟ࢿࢵࢡ࢙࢘࢔⿦╔᪥/㠀⿦╔᪥ࡢ ᐊᗘࡢᐃ⤖ᯝࢆ♧ࡍࠋࡲࡓࠊ⟬ฟࡋࡓⓎờ⋡ࢆ⾲ 1 ࡟♧ࡍࠋⓎờᮇ㛫ࢆ⾲ࡍ SR1 ࡟࠾࠸࡚ࡣࢿࢵࢡ࢙࢘࢔㠀⿦╔᫬ࡢ᪉ࡀⓎờࡋ࡚࠸ࡓᮇ㛫ࡀከ࠸࡜࠸࠺⤖ᯝ࡟࡞ࡗࡓࠋ
ờࢆ࠿ࡁጞࡵࡓᅇᩘࢆ⾲ࡍ SR2 ࡛ࡣ୧⪅࡛኱ࡁ࡞㐪࠸ࡀࡳࡽࢀ࡞࠿ࡗࡓࠋ
⾲ 1 Ⓨờ⋡⿦╔(Aug. 22th)㠀⿦╔(Aug. 23h)
SR1
8.1%
48.3%
SR2
25.4%
26.3%

࠾ࢃࡾ࡟
㛗᫬㛫࡟࠾ࡅࡿⓎờุᐃࢆ⾜࠺᪉ἲ࡜ࡋ࡚ࠊ
ࠕ⿕㦂⪅ࡢయ⾲㠃㸦IN㸧࡜᭹እഃ㸦OUT㸧࡟ ‵ᗘࢭࣥࢧࢆ㓄⨨ࡋࠊIN ࡜ OUT ࡢ‵ᗘࢆẚ㍑ࡍࡿࡇ࡜࡛Ⓨờ≧ែࢆ᥎ᐃࡍࡿ᪉ἲࢆᥦ᱌ࡋࡓࠋࢿࢵࢡ࢙࢘࢔ࡢ⿦╔᪥࡜㠀⿦╔᪥࡜࡛Ⓨờ⋡ࢆẚ㍑ࡋࡓ࡜ࡇࢁࠊ⿦╔᪥ࡢⓎờ⋡ࡀᑠࡉࡃ࡞ࡿࡇ࡜ࢆ☜ㄆࡋࡓࠋ௒ᚋࡢㄢ㢟ࡣࠊ୍᪥ࡢ
Ẽೃࡸ㐠ືᙉᗘࢆᐃ㔞ⓗ࡟⾲⌧ࡋࠊࡼࡾබᖹ࡞≧ἣ࡛Ⓨờ⋡ࡢẚ㍑ホ౯ࢆ⾜࠺ࡇ࡜࡛࠶ࡿࠋ

ᩥ⊩
[1] ᯈ⏕ Ύ, “ᛌ㐺࣭┬࢚ࢿࣄ࣮࣐ࣗࣥࣇ࢓ࢡࢱ࣮࡟
ᇶ࡙ࡃ࢚ࣥࢪࢽ࢔ࣜࣥࢢࡢᴫᛕ”, 2013 ᖺᗘ⢭ᐦᕤᏛ఍⛅Ꮨ኱఍ࢩ࣏ࣥࢪ࣒࢘㈨ᩱ㞟, pp.20-21[2] 㕥ᮌ ᗣྖ, “ࢿࢵࢡࢡ࣮࣮ࣛࠊᑠᆺ✵ㄪᶵࠕ࣌ࣝ
ࢳ࢙ྩࠖࡢ㛤Ⓨ”, NATURE INTERFACE, No. 50,
pp.26-27, 2011[3] ᕝཎ 㟹ᘯ, 㧗ᶫ ᖿ㞝, 㧗ᶫ ⿱ᶞ, “ࢿࢵࢡࢡ࣮࣮ࣛࢆ⏝࠸ࡓ㢕㒊෭༷࡟ࡼࡿసᴗ⪅ࡢ⏕⌮࣭ᚰ⌮࣭ࣃࣇ࢛࣮࣐ࣥࢫ࡬ࡢᙳ㡪”, 2013 ᖺᗘ⢭ᐦᕤᏛ఍⛅Ꮨ኱఍ࢩ࣏ࣥࢪ࣒࢘㈨ᩱ㞟, pp.20-21[4] ᑠᕝ ᚨ㞝, “Ⓨờࡢᶵᗎ-⮫ᗋ᳨ᰝࡢࡓࡵࡢᇶ♏
▱㆑-”, ་Ꮫࡢ࠶ࡺࡳ, 140 ᕳ 6 ྕ, pp.413-417,
1987−  −

第15回人間情報学会講演会ポスターセッション（2013.9.19）↓ព㆑ୗ࡟࠾ࡅࡿ࿴㡢ࡢ✀㢮ࡢ㐪࠸ࡢㄆ▱࡟ࡘ࠸࡚
ᕝཎ㟹ᘯ 1∦᱒⚈㞞 2

1 ᨺ㏦኱Ꮫ ኱Ꮫ㝔ᩥ໬⛉Ꮫ◊✲⛉ ࠛ261-8586 ༓ⴥ┴༓ⴥᕷ⨾὾༊ⱝⴥ 2-11
2 ᝟ሗ㏻ಙ◊✲ᶵᵓ ⬻᝟ሗ㏻ಙ⼥ྜ◊✲ࢭࣥࢱ࣮ ࠛ656-0871 ኱㜰ᗓ྿⏣ᕷᒣ⏣ୣ 1-4

ᴫせ⫼ᬒ࡜┠ⓗ㸸࿘Ἴᩘࡢ㐪࠸ࡸࣁ࣮ࣔࢽ࣮ࡢ㐪࠸࡟ࡼࡿ MMN ࡢฟ⌧࡟ࡘ࠸࡚ࠊ࠸ࡃࡘ࠿ࡢ⪃ᐹࡀ࠶ࡿࠋᮏ◊✲࡛ࡣࠊ㐓⬺่⃭࡜ࡋ࡚ࡢ࿴㡢ࡢ✀㢮ࡢᕪ␗࡟ࡼࡾ MMN ࡟᣺ᖜ࡟ኚ໬ࡀ㉳ࡇࡿ࠿ ERP ィ ࡟ࡼࡾ᳨ウࡋࡓࠋ᪉ἲ㸸ᶆ‽่⃭ཬࡧ㐓⬺่⃭ࢆ A major chord (S)㸪A minor chord (D1)㸪௚ࡢ major chord (D2)㸪major chord ࡜ minor
chord ࡢ୰㛫ㄪࡢ࿴㡢(D3)㸪୙༠࿴㡢(D4) 㸪↓㡢 (D5)࡟タᐃࡋ㸪่⃭㛫㝸ࡣ 500 ࣑ࣜ⛊࡛ᥦ♧ࡋࡓ㸬⤖ᯝ㸸ᶆ‽่⃭࡜ྠࡌㄪࡢ㐓⬺࿴㡢่⃭࡟࠾ࡅࡿ MMN ᣺ᖜࣆ࣮ࢡࡣ௚ࡢ㐓⬺่⃭ࡢࣆ࣮ࢡࡼࡾᑠࡉࡃࠊࣈࣛࣥ
ࢡࡢ㐓⬺่⃭࡛ࡣࠊMMN ࡣぢࡽࢀ࡞࠿ࡗࡓࠋ
⪃ᐹ㸸MMN ࡣ㸪⪺ࡇ࠼ࡿ㡢ࡢᛶ㉁࡟ᑐࡍࡿឤぬ᝟ሗฎ⌮ࢆ཯ᫎࡋ࡚࠸ࡿ࡜ࡉࢀ㸪ࡑࡢ᣺ᖜࡣᶆ‽่⃭࡟ࡼࡾᙧᡂࡉࢀࡓグ᠈࡜㐓⬺่⃭ࡢᕪ␗࡟ࡼࡾቑᖜࡍࡿ࡜ࡉࢀ࡚࠸ࡿ㸬ᮏ◊✲ࡢ⤖ᯝࡣ㸪major chord ࡜௚ࡢ major chord ࡢ㡢⥴ᛶ㉁ࡢᕪࡣᑠࡉ࠸ࡇ࡜ࢆ♧၀ࡋ࡚࠸ࡿ㸬⤖ㄽ㸸↓ព㆑ୗ࡟࠾࠸࡚㸪㐓⬺่⃭࡜ࡋ࡚ࡢ␗࡞ࡿ࿴㡢࡟ᑐࡋ MMN ࡀㄏ㉳ࡉࢀࡿࡇ࡜ࢆ☜ㄆࡋࡓ㸬࠶ࡿ major
chord ࡟ᑐࡋ௚ࡢ major chord ࢆ㐓⬺่⃭࡜ࡋࡓ࡜ࡁࡢ MMN ࡢ᣺ᖜࡀᑠࡉ࠸ࡇ࡜ࡼࡾ㸪࿴㡢㛫ࡢㄆ▱ୖࡢ㡢⥴ᛶ㉁ࡢᕪࡣᑠࡉ࠸ࡇ࡜ࡀ♧၀ࡉࢀࡓ㸬࣮࣮࢟࣡ࢻ㸸

ㄆ▱㸪࿴㡢㸪MMN㸪⬻Ἴࡣࡌࡵ࡟
㡢ᴦࢆ⫈࠸࡚࠸࡚㸪࿴㡢ࡢ㐍⾜࡟ࡼࡾẼศࡀኚ໬ࡍ
ࡿࡇ࡜ࡣከࡃࡢேࡀ⤒㦂ࡋ࡚࠸ࡿ㸬㏆ᖺ㸪࿴㡢ࡸ㡢⛬ࡢㄆ▱࡟㛵ࡋ࡚㸪⬻㟁ᅗ㸦EEG㸧㸪⬻☢ᅗ㸦MEG㸧㸪ᶵ⬟ⓗ☢Ẽඹ㬆⏬ീ㸦fMRI㸧ࢆ⏝࠸ࡓ⚄⤒⏕⌮Ꮫⓗᡭἲ࡟ࡼࡿ◊✲ࡀ㐍ࢇ࡛࠸ࡿ[1-2]㸬␗࡞ࡿ࿘Ἴᩘࡢ㡢ࡸ␗࡞ࡿࣁ࣮ࣔࢽ࣮࡟ᑐࡍࡿ㸪࣑ࢫ࣐ࢵࢳ㝜ᛶ㟁఩㸦mismatch negativity: MMN㸧ࡢㄏ㉳࡟㛵ࡍࡿ㆟ㄽࡶᒎ㛤ࡉࢀ࡚࠸ࡿ[3-4]㸬ᮏ◊✲࡛ࡣ㸪㐓⬺่⃭࡜ࡋ࡚ࡢ࿴
㡢㸦㡢ᴦ࡛඾ᆺⓗ࡟౑⏝ࡉࢀࡿㄪᛶ࿴㡢㸧࡟ᑐࡍࡿ
MMN ࡢ᣺ᖜ࡟ࡘ࠸࡚㸪஦㇟㛵㐃㟁఩ࡢィ ࢆࡍࡿࡇ࡜࡟ࡼࡾ⪃ᐹࡋࡓ㸬S
Major chord

D2

D3

D4

D5

Major chord

Neutral chord

DissonanceBlankᅗ 2 ࿴㡢่⃭ࡢ✀㢮㸬
Fig. 2 Stimuli of chords.
                           ⤖ᯝ
S(μV)

᪉ἲ
⬻Ἴࡣ㸪1 - 30 Hz ࡢᖏᇦ㏻㐣ࣇ࢕ࣝࢱࢆ౑⏝ࡋ㸪୧⪥ᮕཬࡧ㢠ࢆ㐃⤖ᇶ‽࡜ࡋ࡚ 10-20 ᅜ㝿ἲ࡟ࡼࡿ Cz
ࡼࡾ㖟-ሷ໬㖟㟁ᴟ࡛グ㘓ࡋࡓ㸬 ᐃグ㘓ჾ㸦NeXus 10
MKII㸪 Mind Media㸧ࡢࢧࣥࣉࣜࣥࢢ࣮ࣞࢺࡣ 512 Hz࡛㸪ศゎ⬟ࡣ 0.0122 μV ࡛࠶ࡗࡓ㸬஦㇟㛵㐃㟁఩㸦ERP㸧ࡣ㸪ྛᶆⓗ/㠀ᶆⓗ่⃭࠿ࡽ 500 ࣑ࣜ⛊ࡢ㛗ࡉ࡛ษࡾฟ
ࡋࡓἼᙧࢆ㸪ᶆⓗ/㠀ᶆⓗ่⃭࡟ศ㢮ᚋ㸪่⃭ᅇᩘศࡢຍ⟬ᖹᆒࢆ⾜࠸⏕ᡂࡋࡓ㸬⿕㦂⪅࡟ࡣ㸪࿴㡢⫈ྲྀ࡟ὀពࢆᡶࢃ࡞࠸ࡼ࠺ᣦ♧ࡋࡓ㸬ᶆ‽่⃭ཬࡧ㐓⬺่⃭࡟ᑐࡍࡿ஦㇟㛵㐃㟁఩ࡣ㸪ࡑࢀࡒࢀ㸪800 ᅇࡢᶆ‽่⃭࡜ 80 ᅇࡢ㐓⬺่⃭࡟ᑐࡍࡿࡶࡢࢆຍ⟬ᖹᆒࡍࡿࡇ࡜࡟ࡼࡾ⟬ฟࡋࡓ㸬่⃭㛫㝸ࡣ
500 ࣑ࣜ⛊ (Fig. 1)࡛࠶ࡾ㸪ᅗ 2 ࡟♧ࡍࡼ࠺࡞࿴㡢ࢆᶆ‽่⃭ཬࡧ㐓⬺่⃭࡟タᐃࡋࡓ㸬࿴㡢ࡣࣆ࢔ࣀࡢ㡢Ⰽ࡛ᥦ♧ࡋ㸪Fig. 2 ࡢ࿴㡢ࡣࡑࢀࡒࢀ㸪A major chord (S)㸪A minor chord (D1)㸪௚ࡢ major chord (D2)㸪major chord࡜ minor chord ࡢ୰㛫ㄪࡢ࿴㡢(D3)㸪୙༠࿴㡢(D4) 㸪↓㡢 (D5) ࡛࠶ࡿ㸬  

D1Minor chord

D1

S

-10

-10

-5

-5

0

0

5

5

10
-200

10
0

200
S

400

600

(ms)

-200

0

D4

200
S

-10

-10

-5

-5

0

0

5

5

10
-200

D2

400

600

400

600

400

600

D3

10
0

200

400

600

-200

0

200

S

D5

-10
-5
0
5
10
-200

500 msStandard
Deviant

0200

ᅗ 3 ྛ่⃭࡟ᑐࡍࡿ஦㇟㛵㐃㟁఩ࡢ౛㸬
Fig. 3 Examples of ERP to each stimuli.

ᅗ㸯 ่⃭ࡢᥦ♧㡰ᗎ㸬
Fig. 1 Time sequence of stimuli.

−  −

第15回人間情報学会講演会ポスターセッション（2013.9.19）
Fig. 3 ࡟㸪࠶ࡿ⿕㦂⪅ࡢྛヨ⾜࡟࠾ࡅࡿᶆ‽่⃭࡜㐓⬺่⃭࡟ᑐࡍࡿ஦㇟㛵㐃㟁఩ࢆ♧ࡍ㸬ᶆ‽่⃭㸦A
major㸧࡟ᑐࡍࡿ㐓⬺่⃭ D1 (A minor)㸪D3 (୰㛫ㄪࡢ࿴㡢)㸪D4 (୙༠࿴㡢)ࡢ MMN ࡢ᣺ᖜ㸦่⃭࠿ࡽ 100-250࣑ࣜ⛊࡟࠾ࡅࡿ㸪ᶆ‽่⃭ࡢ஦㇟㛵㐃㟁఩࡟ᑐࡋ࡚㝜ᛶ࡟᣺ࢀࡿ㐓⬺่⃭ࡢ஦㇟㛵㐃㟁఩ࡢᕪ㸧ࡣ࡯ࡰྠࡌ
኱ࡁࡉ࡛࠶ࡿࡀ㸪㐓⬺่⃭ D2 (A minor)ࡢ MMN ࡢ᣺ᖜࡣࡑࢀࡽࡼࡾᑠࡉࡃ㸪D5 (↓㡢)࡟ᑐࡍࡿ MMN ࡣ⏕ࡌ࡞࠿ࡗࡓ㸬
S

(μV)

D2

-10
MMN

-5
0

cognitive neuroscience, 21(11), 2230–44, 2009.
[4] Loveless, N. E., “Event-related brain potentials andhuman performance,” in Physiological Correlates of
Human Behavior, Vol 2: Attention and Performance,
pp. 79–97, Academic Press, London, 1983.
[5] Näätänen, R. “The role of attention in auditory
information processing as revealed by event-related
potentials and other brain measures of cognitive
function,” Behavioral and Brain Sciences, 13,
201-288, 1990.
[6] Näätänen, R. “Attention and Brain Function,”
Lawrence Erlbaum Associates. Hillsdale, NJ, 1992.
[7] ᕝཎ㟹ᘯ, ∦᱒⚈㞞, ͆ឤ᝟࡟ࡼࡿ࿴㡢ㄆ▱ࡢᕪ␗࡟㛵ࡍࡿ◊✲͇, 㟁Ꮚ᝟ሗ㏻ಙᏛ఍ HCG ࢩ࣏ࣥࢪ࣒࢘ 2012 ㄽᩥ㞟, pp. 280-283, 2012.

5
10
-200

0

200

400

600

(ms)

ᅗ 4 MMN ࡢᣦᶆ㸬
Fig. 4 Index of MMN.
-200

Sub 1
Sub 2
Sub 3

Index of MMN

-150

-100

-50
D1

D2

D3

D4

D5

0

ᅗ 5 ྛ㐓⬺่⃭࡟ᑐࡍࡿ MMN ᣦᶆ್ࡢẚ㍑㸬
Fig. 5 Comparison of MMN indexes to each deviant
stimulus.

⪃ᐹ࡜⤖ㄽᮏ◊✲࡛ࡣ㸪↓ព㆑ୗ࡟࠾࠸࡚㸪㐓⬺่⃭࡜ࡋ࡚ࡢ␗࡞ࡿ࿴㡢࡟ᑐࡋ MMN ࡀㄏ㉳ࡉࢀࡿࡇ࡜ࢆ☜ㄆࡋࡓ㸬
MMN ࡣ㸪⪺ࡇ࠼ࡿ㡢ࡢᛶ㉁࡟ᑐࡍࡿឤぬ᝟ሗฎ⌮ࢆ཯ᫎࡋ࡚࠸ࡿ࡜ࡉࢀ㸪ࡑࡢ᣺ᖜࡣᶆ‽่⃭࡟ࡼࡾᙧᡂ
ࡉࢀࡓグ᠈࡜㐓⬺่⃭ࡢᕪ␗࡟ࡼࡾቑᖜࡍࡿ࡜ࡉࢀ࡚
࠸ࡿ[5-6]㸬ᮏ◊✲ࡢ⤖ᯝࡣ㸪major chord ࡜௚ࡢ major
chord ࡢ㡢⥴ᛶ㉁ࡢᕪࡣᑠࡉ࠸ࡇ࡜ࢆ♧၀ࡋ࡚࠸ࡿ㸬࿴㡢ࡢ✀㢮࡟ᑐࡍࡿㄆ▱ࡀẼศ࡟ࡼࡾኚ໬ࡍࡿࡇ࡜ࡀ
ሗ࿌ࡉࢀ࡚࠸ࡿࡀ[7]㸪ࡢ᪉ἲࢆᛂ⏝ࡍࡿࡇ࡜࡟ࡼࡾຍ
㱋࡟క࠺ឤ᝟⾲⌧࡟㛵ࡍࡿ㞀ᐖ࡞࡝ࡢࢳ࢙ࢵࢡࡸ㜵Ṇᡭἲࢆ⪃᱌ࡍࡿࡇ࡜ࢆ௒ᚋࡢᒎᮃ࡜ࡍࡿ㸬        ᩥ⊩
[1] Koelsch, S., Fritz, T., & Schlaug, G., “Amygdala
activity can be modulated by unexpected chord
functions during music listening,” Neuroreport,
19(18), 1815–9, 2008.
[2] Janata, P. “The Cortical Topography of Tonal
Structures Underlying Western Music,” Science,
298(5601), 2167–2170, 2002.
[3] Brattico, E., Pallesen, K. J., Varyagina, O., Bailey, C.,
Anourova, I., Järvenpää, M., Eerola, T., et al. “Neuraldiscrimination of nonprototypical chords in musicexperts and laymen: an MEG study,” Journal of−  −

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ᴾ ᴾ ᴾ ᴾ ᴾ ҮТ৑ᴾ ᴾ ᴾ ᴾ ૼ᭗ᡮҮТఇࡸ˟ᅈᴾ