ISASE 2023

Electric Dermal Activity Judges Funny Comedian Videos
Yuki KOSUGE* and Shogo OKAMOTO** Tokyo Metropolitan University, 6-6 Asahigaoka, Hino-shi, Tokyo 191-0065, Japan

Abstract: The need for technology to predict subjective experiences based on physiological signals has increased. Our study
investigated the relationship between the comicalness of comedian videos and skin conductance responses recorded while
watching videos. Through a user study involving eleven participants, subjective scores of a video's funniness were predicted bymultiple linear regression analysis using five types of feature values computed from skin conductance responses. We found thatthe most influential feature is the number of local peaks in the skin conductance response per unit of time. The correlation
coefficient between the observed and predicted funniness scores is 0.53. These findings are useful for the objective evaluationof entertainment experiences.
Keywords: Emotion, Skin conductance, Funniness, Comedy

1. INTRODUCTION

2. METHODS

A growing number of researchers have been studying
methods to estimate human emotions based on
physiological signals to provide services related to users’
feelings. For example, an ordering system recommends
food items to customers from their facial expressions andeye motions [1], and a therapist system infers users’emotions from facial expressions and voices to support
their mental states [2]. Previous studies aimed to detectlaughter based on facial expressions, voices, andelectromyograms related to laughter behaviors, rather
than to estimate subjective fun [3]–[6]. In some situations,sensors for measuring these signals are unavailable.
Furthermore, funniness does not always lead to laughing.
Therefore, as a novel approach to previous investigations,
we used skin conductance. Skin conductance is a measureof arousal and represents the change in the electrodermal
activity of the skin due to sweating associated with arousal
such as fear, surprise, and pleasure. Skin conductance isrelatively free from privacy issues compared with facial
pictures and voices. Hence, skin conductance is preferredin some scenarios.
The objective of this study was to predict subjective
funniness scores reported by viewers after watchingcomedy videos based on the features of skin conductance.
In particular, we searched for features that were effectivefor prediction.
This study was approved by the institutional review
board of the Hino Campus, Tokyo Metropolitan
University (H22-014).

2.1 Stimuli: Comedians’ Talk Movies
We used a total of ten Japanese comedy videos, each ofwhich was two to six minutes long. To include videos witha variety of funniness levels, three or four comedians wereselected from each of the three levels of popularity: very,moderately, and mildly popular comedians.
2.2 Participants
A total of eleven university students and lecturers intheir 20s and 30s participated in the experiment providing
informed consent, and were unaware of the experiment'spurpose.
2.3 Apparatus
The skin conductance was measured using a dermal
electrical activity sensor (AP-U030m II, Nihon Suntech,Japan) and an amplifier (MaPI720CA, Nihon Suntech,Japan). These were connected to a data acquisition device(NI USB-6211, National Instrument Corp., TX) andcontrolled by the Data Acquisition Toolbox in MATLAB(MATLAB 2021a, Mathworks, MA). The responses weremeasured at a sampling frequency of 100 Hz. Videos wereplayed for the participants on a 21-inch display placed 60
cm away from and on headphones. A similar system wasadopted in [7, 8].
2.4 Procedures
Individual participants attached electrodes to the secondand fourth fingers and rested for ten minutes. Before

1Figure 1: Example of the temporal changes in skinconductance responses while watching a
comedy video. Inverse triangles indicate thelocal peaks, and red lines indicate the peaks’
amplitudes.

Figure 2: Observed and estimated funniness scores.

viewing each video, the participant rested forapproximately 1–2 min until their skin conductanceresponse was judged to have reached a steady state. They
watched six or seven randomly selected videos in arandom order. For each participant, the rating of the firstvideo was fixed at five as a reference.
They then scored the funniness of each succeeding
video using a nine-point scale. The participants wereencouraged to use a wide range of scores to reflect thedifferences in funniness between the videos.

Table 1: Partial regression coefficients to predict
funniness scores.
FeatureRegression
coefficient

p value

Coefficient of variationof peak amplitude

0.240.098

Density of peaks

0.43

0.0063

0.32

0.041

0.29

0.042

0.18

0.19

Number of moderate
peaks
Integral of skinconductance response
Altitude center of skinconductance response

2.5 Data Analysis.
A total of 68 samples were obtained in the experiments.
Of these, we excluded cases of invalid measurements inwhich the contact state of the electrode changed owing tohand motion. Furthermore, samples in which no peaks ofskin conductance response were observed were removed
due to inability to compute their feature values. Finally, 53
samples were used for the latter analysis.
From the measured skin conductance responses, 16features were computed for each video for eachparticipant. All feature variables were normalized using zscores for the individual participants. Multiple linear
regression analysis was performed using the 16 features
as explanatory variables to estimate funniness scores. A
stepwise method was used to select the effective variables
to maximize R2 values. Five types of features wereselected, as explained below.
To calculate the features, as shown in Figure 1, wedefined the peaks of the skin conductance response as
satisfying the following conditions: Those greater than the right and left-side local
minimums by 0.01 μS
 The duration of the peak, during which the skinconductance response values are greater than half thepeak value, range 2 to 20 s.
The five skin conductance features adopted in the analysis

were as follows: Coefficient of variation of peak amplitude of skinconductance responses: ratio of the standard deviationof peak amplitude values to their mean.
 Density of skin conductance response peaks: thenumber of peaks in the skin conductance response in
the video divided by the video duration.
 Number of moderate peaks: the number of moderatepeaks while watching a video. Moderate peaks weredefined as those whose peak amplitudes were includedin the top 25%–50% within the peaks of individuals.
 Integral of skin conductance response: integral value ofskin conductance response while viewing a video.
 Altitude center of skin conductance response: first ordermoment of skin conductance, which was calculated by:
1𝒕T 𝒄
1/∑
𝑐
where 𝑡0, Δ𝑡/𝑇, 2Δ𝑡/𝑇, … , 𝑇Δ𝑡 /𝑇, 1

Tis acolumn vector of normalized time stamp withΔ𝑡 being
the sampling period: 0.01 s. 𝑇 is the length of the video
in seconds. The size of 𝒕 is 𝑇/Δ𝑡 1. 𝒄 is a column
vector of skin conductance response value, and 𝑐 is the𝑘th element of 𝒄.

2[3] Tomohisa Ikeda, Hidenori Itamura, Keiko Ikenobu,
and Shinya Morishita. An objective classification of“laughter” using a three-point Measurement System

3. RESULTS
Figure 2 shows a scatter plot of the observed andpredicted funniness scores, where the correlation
coefficient 𝑅 is 0.53 and the adjusted 𝑅 is 0.20.
Table 1 shows the partial regression coefficients for thefive features of multiple linear regression analysis. The
significant features were density of peaks, number ofmoderate peaks, and the integral of skin conductance
response. Of these, the density of the peaks had thegreatest influence on prediction.

of Face, Throat, and Belly. Japanese Journal of Laughterand Humor Research, Vol. 19, pp. 75–85, 2012.
[4] Khiet P Truong and David A van Leeuwen. Automatic
detection of laughter. In Ninth European Conference onSpeech Communication and Technology, pp. 485–488,
2005.
[5] Shogo Fukushima, Yuki Hashimoto, Takashi Nozawa,and Hiroyuki Kajimoto. Laugh enhancer using laughtrack synchronized with the user’s laugh motion. InCHI’10 Extended Abstracts on Human Factors in
Computing Systems, pp. 3613–3618. 2010.
[6] Sarah Cosentino, Salvatore Sessa, Weisheng Kong, DZhang, Atsuo Takanishi, and Nadia Bianchi-Berthouze.
Automatic discrimination of laughter using distributedsEMG. In 2015 International Conference on Affective
Computing and Intelligent Interaction, pp. 691–697.
IEEE, 2015.
[7] Ibuki Tara, Shogo Okamoto, Yasuhiro Akiyama, andHidetaka Ozeki, Timing of vibratory stimuli to the upper
body for enhancing fear and excitement of audio-visualcontent, International Journal of Affective Engineering,
vol. 22, 2023.
[8] Tatsuki Makioka, Shogo Okamoto, and Ibuki Tara, Fearmagnified by vibratory stimuli to the upper-body at
predictive horror scenes, Proceedings of IEEE Global
Conference on Consumer Electronics, pp. 570-572,
2022.

4. DISCUSSION
The subjectively reported funniness was estimated byusing the features of skin conductance responses with thecorrelation coefficient between observation andestimation being R = 0.53. The density of skinconductance response peaks was the most effective andsignificantly negative, suggesting that the fewer the peaksin the time-series of skin conductance response while
watching a video, the funnier the video was. By contrast,
the greater the number of moderate peaks, the funnier was
the video. These analyses suggest that videos withmoderately funny scenes felt more comical than thosecontaining many mildly funny scenes. The integral of theskin conductance response positively influenced
subjective funniness, indicating that enjoyable comedy
videos result in high activity in skin conductance.
5. CONCLUSION
Our study attempted to infer the subjective funniness of
the audiovisual content based on skin conductance
responses. Our results concluded that the density of theskin conductance response peaks, number of moderatepeaks, and integral of skin conductance response while
watching a video are significant predictors of funniness.
REFFERENCES[1] Yuko Akatsu. Proposal-Type Order System using “Smart
Recommend” Technology (OKI’s DX Solutions toSocial Issues). Oki Technical Review, Vol. 88, No. 2, pp.
54–57, 2021.
[2] Mary Czerwinski, Javier Hernandez, and Daniel
McDuff. Building an AI That Feels: AI systems withemotional intelligence could learn faster and be more
helpful. IEEE Spectrum, Vol. 58, No. 5, pp. 32–38,
2021.

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