**Funding:** This research was supported by the Hyundai Motor Group, the Knowledge Service Industry Core Technology Development Program funded by the Ministry of Trade, Industry, and Energy of Korea (No. 20003519), the Basic Science Research Program of the National Research Foundation of Korea funded by the Ministry of Science, ICT, and Future Planning (No. 2021R1A2C1005433), the BK21 Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No.<PHONE_NUMBER>084), and the Korea Institute of Police Technology (KIPoT) grant funded by the Korea government (KNPA) (No. 092021C26S03000, Development of infrastructure information integration and management technologies for real time traffic safety facility operation).

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Kookmin University (protocol code: KMU-202104-HR-264; date of approval: 2 June 2021).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Acknowledgments:** The authors thank Junghwan Ryu, Taesan Kim, and Joonghoo Park for builing a vehicle with a data collection system and Youngdong Kwon and Myengkyu Lee for setting representative emotions and GUI design.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Abbreviations**

The following abbreviations are used in this manuscript:

#### **Appendix A**

The part describes terminologies and variables used in the main text. Table A1 contains details of terminologies and variables.

**Table A1.** Deficition of terminologies and variables used on the main text.

#### **References**

## *Article* **Identification of Video Game Addiction Using Heart-Rate Variability Parameters**

**Jung-Yong Kim 1, Hea-Sol Kim 1, Dong-Joon Kim 2,\*, Sung-Kyun Im 2 and Mi-Sook Kim 3**

**Abstract:** The purpose of this study is to determine heart rate variability (HRV) parameters that can quantitatively characterize game addiction by using electrocardiograms (ECGs). 23 subjects were classified into two groups prior to the experiment, 11 game-addicted subjects, and 12 non-addicted subjects, using questionnaires (CIUS and IAT). Various HRV parameters were tested to identify the addicted subject. The subjects played the *League of Legends* game for 30–40 min. The experimenter measured ECG during the game at various window sizes and specific events. Moreover, correlation and factor analyses were used to find the most effective parameters. A logistic regression equation was formed to calculate the accuracy in diagnosing addicted and non-addicted subjects. The most accurate set of parameters was found to be pNNI20, RMSSD, and LF in the 30 s after the "being killed" event. The logistic regression analysis provided an accuracy of 69.3% to 70.3%. AUC values in this study ranged from 0.654 to 0.677. This study can be noted as an exploratory step in the quantification of game addiction based on the stress response that could be used as an objective diagnostic method in the future.

**Keywords:** HRV parameter; game addiction; *League of Legends*; stress response; sensitivity; specificity; logistic regression

#### **1. Introduction**

The game industry is growing, with a market size of more than US \$123.4 billion worldwide. South Korea is ranked fifth in the world, with 6.7% of the world market share [1], and accounts for 55.8% of Korea's content industry exports in 2018 [2]. Ryu and Lee [3] stated that such booming of the game industry has a positive influence on society, including stress management, the realization of the ideal self, and physical ability improvement. In particular, in the current COVID-19 environment, online games are recognized as a complementary means of social distancing [4,5]. However, Internet game players are not protected from becoming addicted to gaming. This addiction problem could adversely affect personal life as well as family and society, and has become a serious public health issue. Byun and Lee [6] found that Internet addiction is closely related to the increased frequency and duration of Internet use, and leads to anxiety, fear, depression, and obsessive-compulsive disorder, with adolescents being vulnerable target users. Koepp et al. [7] observed that dopamine is secreted from the brains of addicted adolescents with a similar pattern to that of drug addiction.

Adverse effects on adolescents have been studied by many authors [8–10]. In particular, it is notable that the most influential factor causing Internet addiction is stress due to excessive competition, and that adolescents exposed to excessive stress sources were readily immersed in the Internet [11]. Adolescents often experience alienation or loneliness when they are addicted to Internet games [12]. They relieve the stress related to daily life

**Citation:** Kim, J.-Y.; Kim, H.-S.; Kim, D.-J.; Im, S.-K.; Kim, M.-S. Identification of Video Game Addiction Using Heart-Rate Variability Parameters. *Sensors* **2021**, *21*, 4683. https://doi.org/10.3390/ s21144683

Academic Editors: Yvonne Tran and Ki H. Chon

Received: 15 April 2021 Accepted: 28 June 2021 Published: 8 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

and loneliness by using internet games, which were easily accessible [13]. The higher the level of stress, the more they tended to fall into game addiction [14]. According to a study by Lee [15], game addiction prevents adolescents from coping with stress sources properly, causing various psychological problems and stress responses. Likewise, the literature indicates that Internet game addiction and mental stress are closely related.

In recent years, heart rate variability (HRV) has been used in many studies to evaluate stress levels [16–19]. Since stress affects the autonomic nervous system (ANS), HRV controlled by the ANS is often referenced as a stress indicator. A number of studies on HRV parameters have been conducted in this regard. Taelman et al. [20] and Vuksanovi´c and Gal [21] observed that the mean of the NN interval, which is often expressed as the RR interval, and the standard deviation of all NN intervals (SDNN) decreased significantly under mental stress. Taelman et al. [20] and Tharion et al. [22] showed that pNN50 (percentage of successive RR intervals greater than 50 ms) is significantly decreased under stress. Papousek et al. [23] and Traina et al. [24] reported an increase in the low-frequency power range (LF), a decrease in the high-frequency power range (HF), and a significant increase in the LF/HF ratio when subjects experience stress. Park et al. [25] tested the newly developed measuring system to examine electrocardiograms (ECGs) and found a consistent increase in HR and SDNN as the level of addiction increased. At the same time, the LF and LF/HF parameters showed an obvious increasing trend at a high level of addiction.

On the other hand, Hafeez et al. [26] used EEGs to classify game addicts and nonaddicts using cluster analysis and pattern discrimination. They introduced a statistical method to quantify the addiction phenomenon, and Hafeez et al. [27] and Kim et al. [28] identified the theta and theta/alpha parameters of the right occipital region as the discriminating variables between addicts and non-addicts. Likewise, the attempt to quantify the particular characteristics of addiction is an ongoing topic for researchers. If such a numerically quantifiable approach can be successful and assist physicians in identifying an addicted patient, they will be able to treat the patient more efficiently and objectively. Therefore, in this study, the authors are challenged to search for a quantifiable indicator of addiction in ECG response by investigating various HRV parameters. The purpose of this study is to extract quantitative HRV parameters that characterize the particular stress response of game addicts. To achieve this research goal, an exhaustive approach was performed by testing all the candidate parameters collected using window sizes of 30, 60, 90, and 120 s.

#### **2. Methods**

#### *2.1. Subjects*

A total of 23 male students participated in the experiment. The mean age was 23 years (±3 years). Eleven participants were addicted, and 12 of them were non-addicted. They were categorized using the Compulsive Internet Use Scale (CIUS) by Meerkerk et al. [29], and the Internet Addiction Test (IAT) by Young and De Abreu [30]. Based on CIUS, subjects with 2.5 or higher were categorized as addicted, and those with scores less than 1.5 were categorized as non-addicted [31]. An IAT score of 50 or higher has been used to classify the game-addicted by many researchers [10,32–35]. In this study, a subject was categorized as a game addict only when the subject met both the IAT and CIUS standards. For nonaddicted subjects, an IAT score of 40 or lower was required. 14 addicted subjects and 14 non-addicted subjects were selected. 3 addicted subjects and 2 non-addicted subjects were discarded due to a technical error in the measurement system. Controlling the compounding effect of gender in this study, only male participants were tested in this study. Alcohol consumption was prohibited for 24 h before the start of the experiment, and smoking and coffee consumption were prohibited for 1 h before the start of the experiment. A fee was paid to the participants. The experiment was conducted in accordance with the regulations under consideration by the Institutional Review Board of Hanyang University in the Republic of Korea (IRB approval number: HYU-2019-08-004-1).

#### *2.2. Apparatus*

The questionnaires used to categorize subjects into two groups prior to the experiment were the CIUS by Meerkerk et al. [29] and the IAT by Young and De Abreu [30,36].

*League of Legends* by Riot Games Inc. (Los Angeles, CA, USA) was chosen for the experiment. This game was one of the most frequently played games among internet game players [37], and the frequent battles in the game made players experience a simulated life and death situation associated with probable stress reactions.

For data collection, an auxiliary channel of QEEG-64FX by LAXTHA Inc. (Daejeon, Republic of Korea) was used for ECG measurements (Figure 1). A data collection program called Telescan was used. The data sampling rate was set to 500 Hz. The experiment was conducted in a room equipped with a computer, a table, and a chair, where other external stimuli were restrained.

**Figure 1.** ECG measurement equipment. (**a**) Top view of experimental set-up, (**b**) The experimental scene.

#### *2.3. Experimental Design*

The experiment was designed to test HRV parameters to determine whether they could differentiate subjects into two groups: addicted and non-addicted. A betweensubjects design was used in this study. The independent variables were the addiction status of the group, and the dependent variables were 14 parameters, including 7 time-domain variables and 7 frequency-domain variables. The time-domain parameters are NN interval average (RR interval average), SDNN, SDSD, pNNI50, pNNI20, RMSSD, and heart rate average (Table 1). The frequency-domain parameters are LF, HF, LF/HF ratio, LFnu, HFnu, total power, and VLF (Table 2). This study observed specific events during gameplay, including a "killed event", when a player's character was killed by an opponent, and a "killing event", when the player killed an opponent. The data collection window sizes for these events were 30 s, 60 s, 90 s, and 120 s, respectively, to consider the possible delay of the response.

#### *2.4. Procedure*

Positive electrode was placed in the V1 location (between the right rib 3 and 4), and the negative electrode was placed in the left infraclavicular fossa according to the standard limb guidance method [39]. The experimental procedure was briefly explained to the subject, and the ECG sensors were attached and tested to ensure that stable signals were obtained for 1 min while the subjects were relaxing. A "normal game", which is a practice game that does not affect the player's score, was played for familiarization; a "ranked game", which is a competing game affecting the player's score, was played for 30–40 min. For players' immersion in the game, the ranked game was played based on the individual skill level. Subjects played a "normal game" once and a "ranked game" twice, while the ECG was obtained. Subjects were not informed about the addiction test score; thus, they

did not know whether they were categorized in the addicted group or not. The detailed experimental procedure is shown in Figure 2.

**Table 1.** Time-domain variables for heart rate variability [38].

**Table 2.** Frequency-domain variables of heart rate variability [38].

**Figure 2.** Experimental process.

#### *2.5. Data Analysis*

The data were analyzed in batches using Python, and time series analysis and frequency analysis were performed at the same time. The parameters used for time series analysis were extracted by using the Christov ECG R-peak segmentation algorithm. The extracted parameters were NN interval average, SDNN, RMSSD, pNNI50, pNNI20, SDSD, and heart rate average. The signal was also extracted and transformed into frequency parameters using the fast Fourier transform. Welch's periodogram was applied to estimate the spectral properties of the HRV signals, using a Hanning window. VLF (power in verylow-frequency ranges, 0.0033–0.04 Hz), LF (power in low-frequency ranges, 0.04–0.15 Hz), HF (Power in high-frequency ranges, 0.15–0.4 Hz), and total power (Power in all the frequency ranges, ≤0.4) were obtained by the sum of the power in the relevant frequency range of the spectrum. Based on these power values, the values of LF/HF ratio, LFnu, and HFnu were calculated.

Normality was tested by using Kolmogorov–Smirnov test for individual data set. The dataset with a low normality value was graphically examined to ensure an adequate level of normality. During the process, illegal outliers were treated. The *t*-test was performed (*p* < 0.1) to find the parameters and window size that statistically differentiate two groups: the addicted and non-addicted. The statistical analysis was an exhaustive process used to identify the set of most effective parameters and the window size. A correlation analysis was also performed to determine the redundancy of parameters, and a factor analysis was performed to choose the main parameters representing the characteristics of each group. Finally, a logistic regression analysis was conducted to test the sensitivity and specificity of the statistical model in identifying addicted or non-addicted subjects based on the current experimental data. The analysis process is illustrated in Figure 3. Statistical analysis was performed using SPSS Statistics 24.

**Figure 3.** Data analysis process.

#### **3. Results**

An elimination process was used to sort out the best combination of parameters out of 14 parameters from 4 window sizes through statistical analyses.

#### *3.1. The t-Test Results between Groups by Window Size*

There were no significant differences in average parameter values between the addicted and non-addicted groups for the entire window sizes during the experiment (*p* > 0.1).

#### *3.2. The t-Test Results between Groups after Specific Event*

There was no significant difference of HRV parameters between groups for window sizes of 30 s, 60 s, 90 s, and 120 s after "killing events" (*p* > 0.1). However, as shown in Tables 3 and 4, the HRV parameters measured for window sizes of 30 s and 60 s after "killed events" showed a significant difference in some parameters between the two groups. In particular, pNNI20 and LF showed a significant difference (*p* < 0.05), and a marginally significant difference was observed for SDSD, RMSSD, and total power (*p* < 0.1).

#### *3.3. Correlation Analysis and Factor Analysis with HRV Parameters*

A correlation analysis was performed to examine the redundancy of the parameters in differentiating between the two groups. LF and pNNI20 with significant *p*-values (*p* < 0.05) in the *t*-test indicated a low correlation coefficient (0.264). Both could be used to improve statistical power in differentiating the two groups. On the other hand, SDSD and RMSSD showed a correlation coefficient of 1.000, and the total power and LF indicated a coefficient of 0.958. Thus, only one parameter was used to build the statistical model. Therefore, the correlation analysis suggested that the combination of the [pNNI20, LF, SDSD] or [pNNI20, LF, RMSSD] parameter set could be the best combination of parameters with the least redundancy.

Factor analysis was also performed to examine whether the selected parameters covered various factors of the data (Figure 4). The parameters with high eigenvalues for Factor 1 were RMSSD, SDSD, pNNI\_50, and pNNI\_20, and the parameters with high eigenvalues for Factor 2 were LF, total power, and SDNN. That is, the [pNNI20, LF, SDSD] or [pNNI20, LF, RMSSD] parameter set from the correlation analysis (Table 5) were found to have the highest eigenvalues for both Factor 1 and Factor 2 (Table 6). Therefore, the final combination of parameters for statistical modeling was [pNNI20, LF, RMSSD] or [pNNI20, LF, SDSD]. In logistic regression modeling, [pNNI20, LF, RMSSD] was arbitrarily selected to test the model performance in this study because both RMSSD and SDSD were highly correlated with each other (r = 1.000).

**Figure 4.** Factor analysis results.

**Table 3.** The *t*-test results for data from 30 s window size after "killed event"; mean (±standard deviation).

\*\* *p* < 0.05, \* *p* < 0.1.

**Table 4.** The *t*-test results for data from 60 s window size after "killed event"; mean (±standard deviation).

\* *p* < 0.1.

**Table 5.** Correlation coefficients among heart rate variability parameters.

\*\* Correlation is significant at the 0.01 level (both sides).

**Table 6.** The eigenvalues from factor analysis.

#### *3.4. Logistic Regression Models*

Logistic regression models were developed using the selected parameters. A total of 15 mathematical equations were designed to test the maximum sensitivity and specificity of the parameters using natural logarithms and squares. In terms of identifying the addicted group, the sensitivity was computed, and ranged from 0.324 to 0.400; the specificity ranged from 0.828 to 0.922. The overall accuracy ranged from 67.7% to 70.3%. The model with the highest specificity of 0.922 was constructed using pNNI20, ln(RMSSD), and LF. The model with the highest sensitivity of 0.400 was obtained using ln(pNNI20), (RMSSD)2, and ln(LF). The model with the highest overall accuracy of 70.3% was obtained using pNNI20, ln(RMSSD), and LF. The second-highest overall accuracy model (69.7%) was obtained using ln(pNNI20), ln(RMSSD), and (LF)2. The results are summarized in Table 7.

**Table 7.** Summary results of four logistic regression models with the highest accuracy.

#### *3.5. Characteristics of Distributions Affecting the Sensitivity and Specificity*

The true positive rate (sensitivity) was less than 0.4 in the above analysis, which is not good enough to provide a diagnosis of addiction for medical treatment. Such a relatively low sensitivity could be a part of the outcome based on the logistic regression model to maximize the total accuracy. To see the characteristics of the probability distribution of the data, Figures 5–7 are shown under the assumption of a normal distribution. As shown, there is a substantial overlap between distributions that could make either sensitivity or specificity low. From the observations, the criterion beta used for decision-making seemed to be biased to a conservative standard rather than a liberal one, considering that the specificity was much higher than the sensitivity. For example, for Model 1, with a maximum accuracy of 72.3%, the cut-off point associated beta value was set to 0.523, and the sensitivity and specificity were computed as 0.324 and 0.953, respectively. If a different cutoff value was then used, such as 0.372 in Model 2, the sensitivity and specificity can be computed as 0.656 and 0.703, respectively, with 67.3% accuracy.

#### *3.6. Area under the Curve (AUC) Values*

Figure 8 shows the ROC curves of the four models. The AUC value of 0.677 was for Model 1, 0.655 for Model 2, 0.673 for Model 3, and 0.654 for Model 4. According to Hosmer and Lemeshow's study [40], models having an AUC value of 0.5 or less have no discriminating power. A model can be considered acceptable only if the AUC value is between 0.7 and 0.8, and a model has excellent discriminating power if the AUC value is between 0.8 and 0.9. Thus, the AUC value of the current logistic regression model is close to the acceptable level, but further refinement is required for the model to be acceptable.

**Figure 5.** Probability density distribution of RMSSD parameter.

**Figure 6.** Probability density distribution of LF parameter.

**Figure 7.** Probability density distribution of pNNI20 parameter.

**Figure 8.** Receiver operating characteristic (ROC) curves of four representative models.

#### **4. Discussion**

The study showed that "being killed" in a virtual situation generated a greater signal response among the addicted subjects than non-addicted subjects. Klimt et al. [41] mentioned that a shift in self-perception would occur while enjoying the game and identifying oneself with the game character or when playing games experiencing flow or psychological mastery. Turkay and Kinzer [42] stated that the customization process of avatars by players could greatly influence players to identify themselves as game characters. Therefore, it is reasonable to think that such an affective attachment with an avatar could psychologically influence the players, and this phenomenon could be even more severe among addicted subjects than non-addicted ones.

Regarding the model building, three different statistical methods were used to select the parameters to build the best logistic regression model. Through the *t*-test, the pNNI20 and LF parameters were selected because they showed the most significant results (*p* < 0.05) in differentiating the two groups 30 s after the "being killed" event. This means that both time-domain and frequency-domain parameters could be effective in statistically discriminating between the two groups. The total power parameter showed a significant *p*-value (<0.072); however, it was not selected for the final logistic model because it was highly correlated with the LF parameter (r = 0.958) to avoid redundancy. In addition, the RMSSD (or SDSD) parameter was used for the logistic regression model because it showed the highest eigenvalue (0.86) of Factor 1 in the factor analysis. The LF parameter with a significant *p*-value in the *t*-test also showed the highest eigenvalue (0.715) for Factor 2, which was used for the final logistic regression model.

The final parameters selected in this study were found to be associated with the stress response based on previous studies. Bernardi et al. [43] evaluated HRV parameters under the mentally stressful situation of a subject performing arithmetic while speaking or reading, and they observed the increased power of LF when subjects were hurrying to perform the calculation task. Huang et al. [44] found that RMSSD and the combination of various variables had a positive correlation with mental fatigue induced by mental stress. According to a study by Jang et al. [45], RMSSD was also found to have a marginal correlation with tension (r = 0.268, *p* = 0.039), depression (r = 0.356, *p* = 0.005), fatigue (r = 0.259, *p* = 0.041), and frustration (r = 0.304, *p* = 0.018). Lee et al. [46] observed changes in HRV during physical and mental stress in patients with depression, and they reported a significant increase in RMSSD during the stress period compared with the rest period. Mallinani et al. [47] explained that increased sympathetic activity could be functionally characterized by an increase in the LF component in terms of LF–HF balance. Kim et al. [48] reviewed the function of HRV parameters and concluded that low parasympathetic activity was frequently related to a decrease in HF and an increase in LF.

To investigate the efficacy of the regression model in diagnosing game addiction patients, the AUC values were calculated and compared with the reference values. The computed AUC value in this study ranged from 0.654 to 0.677, which is known to have insufficient accuracy for field applications. This indicated that the increased stress response of the addicted during a "killed event" was statistically meaningful, but it might not fully reflect the symptom of addiction that the players were experiencing. Regarding the sensitivity and specificity score, the sensitivity was computed and ranged from 0.324 to 0.400, and the specificity ranged from 0.828 to 0.922 based on the logistic regression model with the default cut-off point used as a decision criterion. However, the values could change when different cut-off points were used. For now, the AUC value was less than 0.7, which could expect only less-than-accurate decision-making. Therefore, it is necessary to test the model performance under various experimental conditions. At any rate, it is important to understand the nature of HRV parameters among addicted game players, who have been very responsive to stressful stimuli, which was worthwhile to investigate further for quantification of addictive symptoms during game playing.

#### **5. Conclusions**

In this study, the difference in HRV parameters between the addicted and non-addicted group was measured during game playing, and it was found that pNNI20, RMSSD, and LF reflected the difference in stress response sensitively for a window size of 30 s after a "being killed" event. To identify the difference between the game-addicted and non-addicted subjects, the AUC score was computed and found to be less than accurate. The quantification of the psychophysiological response of the addictive game was a challenging task, as was shown in this study, but it is worth pursuing the prevention and rehabilitation of addicted patients in the future. For further study, various types and greater numbers of subjects need to be tested for better representation of the addiction symptoms. Additional mathematical exploration using artificial intelligence techniques could be another option for analyzing bio-information with a high level of variability and probable irregularity. It would also be intriguing to examine and compare the HRV parameters to other psychophysiological signals to identify the unknown patterns of game addiction.

**Author Contributions:** J.-Y.K., H.-S.K., D.-J.K., S.-K.I. and M.-S.K. drafted parts of the manuscript and reviewed and edited the full manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Ministry of Education and the Ministry of Science and ICT, grant numbers No.NRF-2018R1D1A1B07050786 and No. 2020-0-01343.

**Institutional Review Board Statement:** This experiment was conducted in accordance with the regulations under consideration by the Institutional Review Board of Hanyang University in the Republic of Korea (IRB approval number: HYU-2019-08-004-1).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** This research was partly supported by a National Research Foundation of Korea (NRF) grant funded by the Ministry of Education (No.NRF-2018R1D1A1B07050786, Development of the algorithm to identify the EEG pattern of game addicts) and the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-01343, Artificial Intelligence Convergence Research Center (Hanyang University ERICA)).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

statement from the Councils on Cardiovascular Nursing, Clinical Cardiology, and Cardiovascular Disease in the Young: Endorsed by the International Society of Computerized Electrocardiology and the American Association of Critical-Care Nurses. *Circulation* **2004**, *110*, 2721–2746.

## *Article* **Individual's Social Perception of Virtual Avatars Embodied with Their Habitual Facial Expressions and Facial Appearance**

**Sung Park 1,\*, Si Pyoung Kim 2 and Mincheol Whang 3**

**Abstract:** With the prevalence of virtual avatars and the recent emergence of metaverse technology, there has been an increase in users who express their identity through an avatar. The research community focused on improving the realistic expressions and non-verbal communication channels of virtual characters to create a more customized experience. However, there is a lack in the understanding of how avatars can embody a user's signature expressions (i.e., user's habitual facial expressions and facial appearance) that would provide an individualized experience. Our study focused on identifying elements that may affect the user's social perception (similarity, familiarity, attraction, liking, and involvement) of customized virtual avatars engineered considering the user's facial characteristics. We evaluated the participant's subjective appraisal of avatars that embodied the participant's habitual facial expressions or facial appearance. Results indicated that participants felt that the avatar that embodied their habitual expressions was more similar to them than the avatar that did not. Furthermore, participants felt that the avatar that embodied their appearance was more familiar than the avatar that did not. Designers should be mindful about how people perceive individuated virtual avatars in order to accurately represent the user's identity and help users relate to their avatar.

**Keywords:** virtual avatar; virtual human; virtual character; embodied conversational agent; social interaction; empathy

#### **1. Introduction**

Humans communicate with others via verbal and non-verbal communication. Through dyadic social interaction, people elicit the other's intention and emotion [1]. Facial expressions represent non-verbal communication channels [2]. The face is the most recognizable region and has unique characteristics that represent an individual [3]. Humans are born with an innate capability to sense and perceive the most important person (i.e., mother) at the early stage of life. Infants are known to discriminate facial features starting at two months after birth [4], and they also prefer facial features over other shapes and forms [5]. Hiding one's face implies the concealment of one's identity. For example, covering a face with a mask may be considered negative social behavior [6].

The rapid advancement of VR (Virtual Reality) technology facilitates the introduction of expressive services tailored to the metaverse. Virtual experiences using HMD (Head-Mounted Display) are now prevalent in households due to video games. In addition, the AR (Augmented Reality) industry is growing through mobile platforms with the availability of engaging entertainment services. Naturally, virtual avatars, a conduit that connects the virtual world to the user, have gained much attention. Many users are interested in projecting or extending their identities through avatars in the internet's social landscape.

**Citation:** Park, S.; Kim, S.P.; Whang, M. Individual's Social Perception of Virtual Avatars Embodied with Their Habitual Facial Expressions and Facial Appearance. *Sensors* **2021**, *21*, 5986. https:// doi.org/10.3390/s21175986

Academic Editor: Stefanos Kollias

Received: 20 August 2021 Accepted: 4 September 2021 Published: 6 September 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

There are various ways to express oneself through a virtual avatar. The most direct way is to apply one's physical characteristics to an avatar that embodies the user's facial appearance or proportions [7]. Studies are also considering the application of a user's habitual expressions based on facial muscle movement [8]. A virtual avatar with the user's unique signature may elicit social responses such as perceived similarity and familiarity.

#### *1.1. Habitual Facial Expressions and Facial Appearance*

The human face consists of 20 facial muscles. Humans communicate through an interplay of these muscles, which produce expressions. Facial expressions enable social communication, which abides by shared rules [9]. They are a powerful source of visual information that embodies the individual's emotions, behavioral predisposition, and intention [10]. Humans can infer the interaction partner's psychological state through facial expressions and identify their traits [11]. In psychology, an individual's traits are, by definition, their habitual pattern of thoughts or affect.

Facial expressions are individual behavioral habits that consist of patterned muscle movement. Such patterns include unique muscle characteristics (e.g., the intensity of the movement of each facial muscle). As a result of these individual differences, people can reliably discriminate themselves from others [8].

On the other hand, facial appearance provides a person's unique identity from the physical features, specifically face and head. Although the perception of appearance relies on many environmental factors (e.g., head pose, lighting conditions), there are descriptive characteristics of a particular individual, such as the location of the eye, nose, and mouth. In our study, we used such facial landmarks to identify critical regions of the face by defining their coordinates (x,y) on the facial image.

Visual perception plays an integral part in facial recognition, which also applies to recognizing oneself. The easiest way to look at oneself is through a mirror. Being able to recognize one's own face is one of the critical prerequisites of self-consciousness and selfidentity. Only humans and a few animals may recognize themselves through a mirror [12]. For humans, this ability develops at the age of two. This ability correlates with empathic and altruistic behavior.

Humans feel a sense of closeness to familiar entities. They also feel more intimate with objects that they are repeatedly exposed to, even without interacting with these (i.e., mere exposure) [13]. An object to which a person is familiarized through repetitive exposure may elicit positive responses [14,15]. For example, stimuli such as names [16] or photos [17] may elicit positive responses after repeated exposure. This phenomenon may also be observed with facial perception. When participants viewed a specific face repetitively, they described it as more familiar, similar, and attractive than those who did not [18].

Humans belong to social circles of varying size. Individuals have a higher chance of getting exposed to a member in the same group than to a member in a different group. When exposed to identical situations, people in the same group tend to exhibit similar responses. The more members express different responses, the lesser the probability of sustaining the group [19].

Exhibiting a similar response to an identical stimulus is related to empathy. In a dyadic interaction, an empathic response is manifested by mimicking the other's facial expressions or gestures [20]. Sustaining a similar expression or empathic response for a long time results in the repeated utilization of the respective muscles responsible for empathic expressions. Repetitive use of certain muscles affects bone structure and as a result, leads to an appearance that is similar to that of the significant other [21].

Furthermore, perceived similarity is known to entail a positive face-to-face interaction. People are predisposed to think that in dyadic socialization, a part of their partner's attitude, values, and beliefs is similar to theirs [22,23]. People tend to like and trust people who have a similar physical appearance more than those who do not [24].

#### *1.2. Virtual Avatar*

The term avatar is derived from a Sanskrit word and connotes the incarnation of a deity. In modern society, the user's mental model of an avatar is that it is an alter ego of the user that can interact with other virtual avatars in a virtual world [25]. Recently, the need for a virtual avatar has not only come from games, movies, advertisements, and remote collaboration but has extended to medical practice and crime investigation. Research, design, and development explore the avatar model and how it can imitate users in real time. Realistic animation is possible by depicting the movement based on bone and muscle structure, considering the real-world laws of physics.

In general, the more similar the illustration of a virtual avatar is to the user, the more immersive their experience [26,27]. Nevertheless, a very realistic but imperfect depiction of a user may lead to negative feelings [28]. Virtual characteristics that reach a certain point of human likeness tend to elicit a feeling of eeriness.

Much research has been conducted on the interaction channels of virtual avatars. There has been much attention on non-verbal expressions such as the gaze, the facial expression, and gestures of an avatar. For example, minute movements of the pupil add a sense of immersion and social presence. Studies found that participants perceived a higher level of social presence when communicating via richer media than through a text-based medium [29–31].

In a virtual environment, users may use their virtual avatar to represent themselves. Users tend to prefer an avatar that embodies their unique and exclusive characteristics that differentiate them from the others. Some people prefer an avatar that is similar to themselves, while others prefer their avatar to be an idealized version of themselves. Users who adopted such avatars reported higher satisfaction and attachment [32]. Users are more motivated to use avatars that have a facial appearance similar to theirs than those that do not [24].

However, the majority of avatar illustrations and expressions do not consider the individual's facial characteristics. Applying individualized facial habits or appearances does not require sophisticated technology and is viable with the current computer systems available to the mass. However, software that can animate such virtual avatars needs to be developed with investment and resources.

Another reason why individuated avatars are not prevalent involves the users. Many users do not recognize their own facial habits and would have trouble customizing the facial characteristics by themselves. It would be necessary for the application to capture and analyze the user's facial movements and suggest a personalized avatar for approval before use. The users may feel that this is a hassle, not to mention that there is resistance from users against taking a video of their own face. Most importantly, research lacks an understanding of common elements applicable to individuated virtual avatars. Specifically, we do not clearly understand the social effects of personalized virtual avatars with individualized features. Would people prefer avatars with their appearance or habitual expressions? Would people perceive a similarity between the avatar and themselves? Would people be able to relate to the avatar and use it for their profile in a social networking service?

#### *1.3. Research Goal*

Humans have universally recognizable expressions. Ekman found a universal relationship between facial muscle movements and specific emotions (e.g., happiness, sadness, anger, fear, surprise, disgust, interest) [33]. Despite the universality, individual differences exist in the *intensity* of each muscle movement. Researchers also found that the asymmetrical measures of facial regions identify stable individual differences [34].

A facial habit results from a habitual personal pattern that exhibits a unique individual signature. Facial recognition based on these individual differences in expression analyzes the movement pattern of facial muscles to discriminate individuals [8].

Another factor to consider is the individual's appearance. The perception of a form is necessary to identify an object [35]. The holistic form is a pivotal component required to distinguish an individual [36].

In summary, our research aims to evaluate the perceived social effect of a virtual avatar using two markers: (1) habitual facial expressions captured through the *intensity* of muscle movement and (2) facial appearance identified using facial *landmarks*. The research hypotheses are summarized accordingly in Table 1. We added the third hypothesis because both facial habit and facial appearance involve the facial muscle, and therefore, an interaction may occur. Thus, we intend to analyze whether facial habits (independent variable) have a different effect on the social constructs (dependent variables) depending on facial appearance (independent variable).

**Table 1.** Research hypotheses.

In short, the study aims to evaluate people's social perception of an avatar that embodies the unique and individual characteristics of the user. We planned to investigate the interaction of the two independent variables (facial appearance, facial habit) and their respective main effects.

#### **2. Methods**

#### *2.1. Participants*

Forty-five university students were recruited as participants. The participants' average age was 23.78 years (SD = 2.88), with 20 males and 25 females. We recommended that the participants get sufficient sleep the day before the experiment. We selected participants with a corrective vision of 0.7 or above to ensure the participants' reliable recognition of visual stimuli. All participants were briefed on the purpose and procedure of the experiment and signed a consent form. Participants were given participation fees as compensation.

#### *2.2. Materials*

#### 2.2.1. Video Stimulus

The current study used a video stimulus to elicit participants' facial responses to produce data to create an individuated avatar. We used video materials known to evoke emotions, which were empirically verified by an experiment conducted in and provided by Stanford University (*n* = 411, [37]).

For each emotional state (positive and negative), we selected two candidate stimuli from Stanford's materials [37]. We conducted a manipulation check on all candidate materials. With regard to the positive stimuli, participants perceived the two video stimuli as positive. The results did not show a significant difference from those of the Stanford study. However, there was no significant change in the facial expression of participants when the negative stimuli were exposed. In a follow-up questionnaire, participants reported having a negative emotional state but did not display a negative facial expression. Since the current experiment requires valid participant data on emotional expression to be applied to a virtual avatar, we decided not to include stimuli evoking a negative emotional state.

#### 2.2.2. Video Analysis

We used Open Face, which is open-source software that enables face recognition with deep neural networks [38]. We used AU (Action Units) as the basic unit for appraisal from the Facial Action Code System (FACS) [39]. Figure 1 depicts the process. We first normalized the facial region from the participants' videos. The video was organized as a sequence of images of fixed size (200 × 200 pixels). From this image sequence, we elicited the intensity of AU movement and the 68 facial landmarks (see Figure 2). The landmarks extract the coordinates (x,y) of key facial regions (e.g., the eye, eyebrows, nose, lips, and chin). The movement and intensity of AU were identified from the AU vector data in HOG (Histograms of Oriented Gradients) [40]. We elicited the individual's habitual expression data from the AU movement intensity. We elicited the individual's facial appearance from the landmark data.

**Figure 1.** The analytical process of identifying individual muscle movements and facial appearance.

**Figure 2.** The 68 facial landmarks used to identify the participant's facial appearance.

#### 2.2.3. Virtual Avatar

We designed two baseline avatars, male and female, to embody the participant's expressive habits and facial appearances (see Figure 3). For the female model, we modified a public model available from an open source [41]. To visualize the muscle movement, we produced AU-based blend shapes using the animation software Maya (Autodeck). We used blend shapes that morphed the lower face of the virtual avatar for a more natural look. Table 2 shows the relationship between blend shapes and facial muscles.

**Figure 3.** The baseline virtual avatar models in the study.

We used the Unity 3D engine to render and animate the virtual avatar [42]. Figure 4 depicts the two versions of the avatar with the participant's facial signature (facial appearances, habitual expression) applied. How participants viewed such variations and what was measured will be explained in Section 2.3 (Experiment Procedure).

**Figure 4.** An example of the baseline virtual human morphed based on the participant's (**a**) facial appearances and (**b**) habitual expression.

#### 2.2.4. Subjective Appraisal of Social Constructs

The current study investigated participants' perceptions (similarity, familiarity, attraction, liking, and involvement) of virtual avatars. All constructs involve the subjective appraisal by participants rather than an objective quantitative measurement. Table 3 depicts their operational definition. Each construct was measured on a 7-point Likert scale. For example, the seven items of similarity were *slightly*, *somewhat*, and *extremely* toward both ends (dissimilar and similar) with *neutral* in the middle.

**Table 3.** The operational definition of the social constructs of interest.

Similarity connotes the degree to which the user sees themselves as similar with the avatar. Some research includes attitudinal similarity (e.g., personality, attitude, belief system) in the definition [18,43]. However, in this study, we limited the definition to only include the physical likeliness to the participant and formulated the survey question accordingly. We purposely designed the study to eliminate interaction with the virtual avatar to investigate the effect of its mere presence without any convoluted variables that may arise from interactions. Since there is no interaction with the virtual avatar, it is extremely difficult to validly assess attitudinal similarity.

It is important to emphasize that we investigated *perceived* similarity as opposed to actual similarity. Researchers have made a clear distinction between the two constructs [44]. *Actual* similarity is measurable and quantifiable using standardized personality assessment. As the paper will discuss later, the relationship between similarity and attraction is critical. Some research studies suggest that only perceived similarity is a prerequisite to eliciting attraction [45–47]; other research emphasizes the importance of actual similarity [48]. In this study, mainly for consistency with other perceived constructs, we investigated the perceived similarity.

Perceived familiarity was measured to assess the degree to which participants were familiar with the virtual avatar that had the participant's facial characteristics applied. In interpersonal and social science literature, this construct connotes "being knowledgeable" or acquainted with a person [18,49] or a concept [50,51]. That is, a priori knowledge is necessary to measure perceived familiarity. For example, in psychology, after an interaction (e.g., phone call, discussion) with a person, the participant felt subjective familiarity with the person similar to what they would feel with a close friend [49]. Other studies measured familiarity using objective quantitative measures, such as the amount of exposure to a person's photo and not just focusing on perception [18].

Some studies use the terms perceived familiarity and resemblance (perceived similarity) interchangeably [49]; however, we measured the two constructs (perceived similarity and perceived familiarity) independently. The literature suggests that the two constructs correlate and have a causal relationship, with attraction as a mediating variable [18]. In our study, we minimized interaction with the virtual humans (e.g., conversation) to test the mere exposure effect.

Since the pioneering work of Byrne [52] (for a review of attraction as a research paradigm, see [53]), researchers have investigated interpersonal attraction in relationships [54]. Researchers widely accept Newcomb's definition of attraction as the most comprehensive one, and it is defined as follows: "Attraction refers to any direction orientation (on the part of one person toward another) which may be described in terms of sign and intensity" (Page 6) [55].

Studies on attraction generally investigate the relationship between the independent variables (e.g., attitudinal similarity, physical attractiveness) and the attraction response as a dependent variable. It is critical to note that attraction is distinguished from attractiveness, i.e., characteristics (e.g., attractive personality, good looks) that attract others [56]. In our study, we obtained the participant's perceived attraction (dependent variable) to the virtual avatar, which varied according to different facial features (independent variable). The intensity of attraction depends on many factors such as their relationship (e.g., parent–child, wife–husband) and the duration of interaction (e.g., long-term, first acquaintance) [57].

Perceived liking, as a construct, is defined as the degree to which the participant likes or dislikes the other person in a dyad. A causal pattern consists between the perception of being liked and liking the other [58]. Compared to attraction, perceived liking has a corresponding place on a like–dislike spectrum, whereas attraction is located on an attraction–repulsion spectrum [59].

In psychology, involvement connotes *approach* predispositions (e.g., empathy, sympathy, challenge) as opposed to distance, which refers to *avoidance* predispositions (e.g., antipathy, irritation, boredom) [24]. The two constructs are unipolar. Involvement refers to the degree to which the participants relate to and empathize with the virtual avatar. Since empathy is mainly dependent on the task and context [60,61]), we provided the context that the virtual agent would be used in a profile for a social networking service.

#### *2.3. Procedure*

Figure 5 outlines the experiment procedure. The experiment was conducted twice, with an interval of one week between the two sessions (i.e., Session #1 and Session #2).

In the first experiment, the participants were briefed about the purpose of the experiment and the procedures. Then, participants viewed the two affective stimuli from the display in a relaxed position (see Figure 6). Participants were guided not to force any expression but display the natural expression felt from the viewing. The web camera on display recorded a video of the participant's facial responses for 90 s. Then, the participants left the experiment after a brief explanation of the second experiment session.

**Figure 5.** The experiment consists of two sessions, with one week in between for each participant.

**Figure 6.** The experiment environment.

In between the two sessions, we produced the following four virtual avatars for the second experiment session based on the data acquired from the participants:

For an avatar without any habitual facial expression applied (2 and 4), AU movement based on the literature was applied instead [39]. For an avatar without any facial appearance applied (3 and 4), the original baseline appearance of the avatar was used (Figure 3).

Then, the participants viewed these virtual avatar stimuli. The study used a 2 × 2 within-subject design. There were two levels of habitual expression (applied or not) and facial appearance (applied or not), respectively.

Every participant viewed all four virtual avatar types. The order of the virtual avatar was randomized using a Latin square to counter the potential learning and fatigue effect. After viewing the avatar for 30 s, the participant responded to a subjective questionnaire.

Interaction with the virtual human was limited to mere exposure as opposed to an interactive one (e.g., conversation). The strength of the subjective response was contingent on the nature of the task [62] and may have elicited a confounding effect, which would be difficult to identify.

#### *2.4. Statistical Analysis*

To understand the effects of the two independent variables (habitual facial expression, facial appearance), we conducted a two-way ANOVA on the participant's subjective evaluation of the four avatars.

Data from participants who did not exhibit any facial expressions during the experiment were excluded during the acquisition process. The exclusion criteria are outlined as follows. First, we divided the non-expression interval and the expression interval. The latter was defined based on the average expression data. The intensities of AU 6 (Cheek raiser) and AU 12 (Lip corner puller) during the expression interval were compared to those of the non-expression interval. If the intensity during the expression interval was less than the non-expression interval or non-existent, we excluded the participant's data. The Latin square factors were tested to examine whether the order affected the dependent variable. The Latin square order did not affect data, so all results were collapsed over these variables.

#### **3. Results**

#### *3.1. Similarity*

The results of analysis of subjective perception involving similarity are as follows. Figure 7 depicts participants' responses to the different avatars that varied according to two factors (facial habit and facial appearance). The *Y*-axis indicates the average of subjective Likert ratings. The results showed no significant interaction between Facial Habit × Facial Appearance, F(1, 163) = 2.517, *p* > 0.11. Of particular importance, the results showed that Facial Habit had a significant main effect, F(1, 81) = 5.182, *p* < 0.05. On the other hand, Facial Appearance had no significant main effect, F(1, 81) = 0.576, *p* > 0.44.

#### *3.2. Familiarity*

The results of analysis of subjective perception involving familiarity are as follows. Figure 8 depicts participants' responses to the different avatars that varied according to two factors (facial habit and facial appearance). The *Y*-axis indicates the average of subjective Likert ratings. The results showed no significant interaction between Facial Habit × Facial Appearance, F(1, 163) = 0.004, *p* > 0.94. Of particular importance, the results showed that Facial Appearance had a significant main effect, F(1, 81) = 4.182, *p* < 0.05, whereas Facial Habit had no significant effect, F(1, 81) = 0.966, *p* > 0.32.

**Figure 8.** Subjective appraisal of perceived similarity.

#### *3.3. Attraction*

The results of the analysis of subjective perception involving attraction are as follows. Figure 9 depicts participants' responses to the different avatars that varied according to two factors (Facial Habit and Facial Appearance). The *Y*-axis indicates the average of subjective Likert ratings. The results showed no significant interaction between Facial Habit

× Facial Appearance, F(1, 163) = 2.3, *p* > 0.13. Both Facial Appearance, F(1, 81) = 0.047, *p* > 0.82, and Facial Habit, F(1, 81) = 0.631, *p* > 0.42, had no significant main effect.

**Figure 9.** Subjective appraisal of perceived attraction.

#### *3.4. Liking*

The results of analysis of subjective perception involving liking are as follows. Figure 10 depicts participants' responses to the different avatars that varied according to two factors (Facial Habit and Facial Appearance). The *Y*-axis indicates the average of subjective Likert ratings. There was no significant interaction between Facial Habit × Facial Appearance, F(1, 163) = 1.165, *p* > 0.28. Both Facial Appearance, F(1, 81) = 0.004, *p* > 0.94, and Facial Habit, F(1, 81) = 2.133, *p* > 0.14, had no significant main effect.

**Figure 10.** Subjective appraisal of perceived liking.

#### *3.5. Involvement*

The results of analysis of subjective perception related to involvement are as follows. Figure 11 depicts participants' responses to the different avatars that varied according to two factors (Facial Habit and Facial Appearance). The *Y*-axis indicates the average of subjective Likert ratings. The results showed no significant interaction between Facial Habit × Facial Appearance, F(1, 163) = 0.221, *p* > 0.63. Both Facial Appearance, F(1, 81) = 0.055, *p* > 0.81, and Facial Habit, F(1, 81) = 0.221, *p* > 0.63, had no significant main effect.

**Figure 11.** Subjective appraisal of perceived involvement.

#### *3.6. The Correlations between Social Perceptions*

We conducted a bivariate correlation analysis to understand the relationship among participant's social perceptions of the virtual avatars (see Table 4). The results show a significant correlation in all pairs of the analysis. The correlation between perceived attraction and liking was the highest (r = 0.695, *p* < 0.01) (see Figure 12). The implications of the correlation results will be discussed, integrating results from other analyses.

**Figure 12.** The correlational relationship between social constructs. \*\*\* *p* < 0.001

#### *3.7. Data Categorization*

Thus far, we identified that facial habit had a main effect on similarity, while facial appearance had a main effect on familiarity. However, these variables had no effects on attraction. As discussed in the operational definitions, attraction is based on a person's liking for the other, and perceived liking in the initial stage of interaction may lead to feelings of attraction [58]. Our results also show that among the constructs, perceived attraction and liking have the highest correlation (r = 0.695, *p* < 0.01).

However, attraction is a much larger and multifaceted construct [63]. Based on the pioneering work by Byrne [64], both perceived similarity and liking lead to attraction, and many researchers have attempted to understand the exact interplay and different weights of the two on attraction [44]. Therefore, we conducted a two-way ANOVA on the sum of perceived liking and similarity (i.e., data categorization) of the four avatar conditions (see Figure 13). The *Y*-axis indicates the addition of the Likert ratings of perceived liking and similarity.

**Figure 13.** Subjective appraisal of the sum of similarity and liking.

The results showed that Facial Habit had a significant main effect, F(1, 81) = 4.836, *p* < 0.05, whereas Facial Appearance had no significant main effect, F(1, 81) = 0.610, *p* > 0.69. Furthermore, there was no significant interaction between Facial Habit × Facial Appearance, F(1, 163) = 2.467, *p* > 0.12.

The research investigated the participant's social perception (similarity, familiarity, attraction, liking, and involvement) of virtual avatars engineered with the participant's unique facial signature (facial appearance, facial habit). In summary, the participants perceived significant similarity to an avatar with habitual expression applied compared to the avatar that did not (*p* < 0.05). In addition, habitual expressions also significantly affected the sum of perceived similarity and perceived liking (*p* < 0.05). The participants perceived familiarity with the avatar with facial appearance applied compared to the avatar that did not (*p* < 0.05).

#### **4. Discussion and Conclusions**

To our knowledge, this is the first research to reveal that participants can perceive similarity to a virtual human that had their characteristic facial movements (i.e., habitual pattern), which has significant implications for the design of virtual agents. The virtual human community had long researched the effects of virtual agent realism. The consensus is that behavioral realism is more critical than visual realism in eliciting believability [27]. The suspension of disbelief refers to the deliberate avoidance of critical thinking, whereas a reality check involves deciding what is possible or not in the real world [65,66]. Thus, behavioral realism is more socially engaging and believable than visual realism [27].

In the context of this study, the effect of perceived similarity of a virtual agent to oneself is consistent with research findings on believability. Specifically, while participants did not perceive similarity in virtual avatars to which their facial appearance were applied (i.e., visual realism), they perceived similarity in virtual avatars to which their facial habits were applied (i.e., behavioral realism). This implies that designs may go beyond anthropomorphic design. For example, future research may conduct studies using animalinspired avatars with facial features (e.g., eyes) and see if participants can perceive similarity to these avatars when their facial movements are applied.

There is much empirical evidence that similarity, as a social construct, elicits attraction [44], and this relationship is regarded as "one of the most robust relationships in all of the behavioral sciences (p. 281)" [67]. Researchers found a positive linear relationship between similarity and attraction (i.e., the law of attraction) [68]. However, the various virtual avatars had no significant effect on attraction. This may be due to interaction being limited to one-time mere exposure. We purposely limited interaction to exclude variables (e.g., perception of personality) that may influence the perceived measures, which may have been brought on by prolonged interaction. Perceived similarity is influenced not only by physical appearance [69] but also attitude [70] and personality [71]. Future studies may add a persona to the virtual avatar to test the complexities of perceived similarity.

The study's limitation in understanding the effects of an individuated avatar on attraction is apparent. Since perceived attraction is a multifaceted construct, it typically requires more interaction, building up from initial liking [58]. Future studies may investigate the degree of attraction as a function of time or when participants interact with the individuated virtual avatar. The perceived relationship also influences attraction; thus, future studies need to address the relationship between the avatar (e.g., companion, butler, assistant) and the participant carefully.

Nevertheless, through data categorization, we found that habitual expressions had a main effect on the sum of perceived similarity and perceived liking (*p* < 0.05). Since the interplay between perceived similarity and liking leads to attraction [64], these results suggest that an individuated avatar may elicit attraction with prolonged interaction.

Additionally, the individualized virtual avatars had no significant effect on perceived involvement. Although we provided the context that the virtual agent would be used as part of a profile for a social networking service, we also acknowledge that many users do not use profiles similar to their appearance. Future studies should cluster the participants based on who use or intend to use avatars with a similar appearance as an alter ego and assess their perceived responses accordingly.

The perceived familiarity with a virtual avatar to which the participant's facial appearance was applied may be due to the participant's repetitive exposure to their reflections in mirrors or still photos of themselves. Repetitive exposure elicits familiarity [13]. On the other hand, people may not be familiar with their habitual expressions during various emotional states.

Finally, the study is limited in that the virtual avatars were designed based on only positive emotional expressions. Future research on individualized virtual avatars should also include negative or complex emotions.

**Author Contributions:** S.P.: methodology, validation, formal analysis, investigation, writing, review, editing; S.P.K.: conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing, visualization, project administration; M.W.: conceptualization, methodology, writing, review, supervision, funding acquisition. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2020R1A2B5B02002770).

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Sangmyung University (protocol code BE2017-20, approved at 22 September 2017).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the subjects to publish this paper.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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**Abstract**

The red flour beetle *Tribolium castaneum* has emerged as the genetically tractable model insect for population genetics, functional genomics, and evolutionary studies. This agricultural pest is notorious for its potential to severely damage stored products*. T. castaneum* has developed resistance to almost all insecticides. The reports of insecticide resistance from different parts of the world show that sustained insecticide usage has only aggravated the problem. As insecticides continue to be the mainstay of pest control programs, it is essential to identify the factors influencing insecticide resistance for implementing effective pest-management strategies. The development and progression of insecticide resistance in *T. castaneum* is thus an escalating global issue requiring immediate solutions. Several studies have investigated the multiple resistance mechanisms found in *T. castaneum*, such as reduced cuticular penetration, increased metabolic detoxification, and targetsite insensitivity. The availability of Whole Genome Sequence and recent advances in Next Generation Sequencing technology has furthered a geneticist's grasp of resistance study in *Tribolium*. The strategic containment of this organism calls for an in-depth understanding of resistance development. The review mainly focuses on different kinds of resistance mechanisms and genes mediating insecticide resistance. Also, it exhaustively explores the *CYP450* gene superfamily in *Tribolium* to emphasize its role in governing resistance*.* The consolidated insights from this study will facilitate further research on identifying biological targets, thereby developing novel control strategies for effective insect control.

**Keywords:** Insecticide resistance, Resistance Mechanisms, Detoxification genes, *CYP450* gene superfamily, *Tribolium castaneum*

#### **1. Introduction**

The global population is expected to cross 9.1 billion by the year 2050 and food production is projected to rise to 70% to feed this growing population [1]. Many of the fastest-growing populations are in developing countries, several of which are already facing moderate or severe food insecurity and a shortfall in food supply. One in every six children suffer from hunger in developing countries [2] and the proportion of undernourishment has been steadily increasing since 2015 [3]. The increasing trend globally of food insecurity attests to the fact that severe food deprivation or hunger is a real threat, and this scenario nullifies the ambitious "zero hunger target" by 2030. The severity of "food insecurity" underscores the immense challenge in

attaining safe, nutritious, and sufficient food for all people [3]. Tackling problems of food insecurity demand intensive food production. However, increasing food production alone will not be a viable solution to achieve the "zero hunger target" by 2030 or for meeting the growing demand for food.

The pre-harvest and post-harvest issues combined with insect infestation represent a very strong limitation in optimal food production, causing mass losses of grains. After harvest, food grains undergo a series of processes such as threshing, cleaning, drying, storage, processing, and transportation before it reaches the consumer. It has been identified that food losses in the post-harvesting chain start at the time of harvest and continue up to food marketing at the consumer's end [4–6]. Grain losses may also take place due to technical limitations such as inadequate stock management facilities, improper packaging, and insufficient infrastructure.

In many countries, 15% of food grains are lost during or after harvest [7]. The Food and Agricultural Organization (FAO) estimated post-harvest grain loss at 40% and cereal loss at 30% in India [8]. The post-harvest losses account for onfarm, processing, and storage loss. Studies attribute massive grain loss in developing countries to manual operations in different stages of harvesting, which causes 15% loss on the field, 13–20% loss at processing, and 15–25% storage loss [9]. Several studies show that insects are the main contributor to storage loss in the food supply chain [10–12], which accounts for 10–20% of storage loss [13].

A diverse community of stored product species are associated with different environments where farmers store grains and cereals; from farm bins to processing facilities, to feed mills, to flour mills, to retailer stores [14–16]. Among this complex pest system, 600 species from Coleoptera and 70 species from Lepidoptera can cause substantial losses by eroding the quality of grains [17]. Coleoptera is the largest order of insects with over 250,000 described species and contains in its fold some of the most notorious stored grain pests. In these, *T. castaneum* requires special attention because of the significant harm that it can have on stored products. It attacks a large variety of stored and processed commodities and is the most harmful insect in the pest complex for its ability to inflict severe damage on stored products. Curtailing *Tribolium* infestations in the supply chain would be one critical step that can help strengthen food quality, reduce storage loss, and improve food security.

#### **2.** *Tribolium castaneum* **and its damage**

The red flour beetle, *Tribolium castaneum*, is an important model organism and a common inhabitant of milled cereal products, stored flour, and fungus-infested grain [18–21]. *T. castaneum* causes severe damage in flour mills and wherever dried foods and cereal products are stored or processed. They rank among the most harmful pests inhabiting grain storage facilities and processing facilities [16, 22, 23]. These insects frequently leave storage locations and migrate across heterogeneous landscapes on a daily, seasonal, or irregular basis to find new mates and resources [24]. The movement of grains from producers to consumers generates a complex network of grain storage and transportation that facilitates the dispersal of pests and pathogens associated with the grain [25]. The dispersal of the *T. castaneum,* through transportation and storage networks, allows them to find suitable habitats where they feed and reproduce, ultimately exploiting the resource patches of grain.

The adult females of *T. castaneum* lay eggs on the flour, complete their life cycle, and deplete the nutritional quality of grains over time. In the case of serious infestation, the flour becomes adulterated with a pungent odor, diminished in nutritional and market value [26, 27]. In addition to direct feeding*, T. castaneum* contaminates the food products through molting and excretion, which makes the

#### *Review of Insecticide Resistance and Its Underlying Mechanisms in* Tribolium castaneum *DOI: http://dx.doi.org/10.5772/intechopen.100050*

product commercially undesirable. Depending on the level of infestation, the grain can be rejected or downgraded [28]. Product deterioration can also result from the production of quinones secreted from glands on the thorax and abdomen [29–31] leading to significant loss of quality and economic loss. The customer demand for infestation-free flour/grain has increased widely, raising the stakes of *T. castaneum* management in grain storage facilities. In a way, consumer demand for infestationfree products has been a fillip to the use of insecticides such as organophosphates and pyrethroids during storage.

Thus, a wide variety of insecticides has been applied as a primary strategy for *Tribolium* control by targeting the insect's neurological sites, including voltagegated ion channels and acetylcholine system, causing irreversible disruption of neurological function, resulting in insect mortality. It has brought down the infestation rate, ensured long-term protection of stored commodities, and is relatively convenient to apply [32, 33]. But the incessant application of insecticides in storage facilities has accelerated the development of insecticide resistance in *T. castaneum* and resulted in the formation of particular resistant alleles in succeeding generations. The occurrence of insecticide resistance in *T. castaneum* found in grains and cereals during storage and shipping was recorded in many countries. The first instance of insecticide resistance was reported in *Tribolium* between 1959 and the early 1960s [34, 35]. Halisack and Beeman [36] applied discriminating doses of malathion to *T. castaneum* populations collected from cereal storages in the US and detected 20-fold resistance in 31 of 36 *T. castaneum* populations. In Canada, 54 strains of *T. castaneum* showed resistance to malathion at an LC99.9 value of 0.012 mg a.i/cm2 [37]. The populations of *T. castaneum* collected from flour mills in the USA were exposed to discriminating doses of malathion to measure their resistance status. Of 28 strains, 93% of the *T. castaneum* population tolerated the discriminating doses of malathion [38]. The resistance status of Egyptian populations of *T. castaneum* was studied using the filter paper bioassay method against three contact insecticides and populations of *T. castaneum* were found to be more resistant against pirimiphos-methyl [39]. *T. castaneum* resistance is extended to pyrethroid insecticides, which is one of the most widely-used classes of insecticides in food and fodder houses as it is effective on a wide range of insects, has high efficacy at the minimum dose, and low toxicity on mammals [40–42]. Cases of pyrethroid resistance have been detected in *T. castaneum* populations from Pilot-Scale Warehouses [43] and peanut storage warehouses [44]. Several cases of resistance have been reported in different populations of *T. castaneum* collected from different countries across the world such as Italy [45], United States of America [46–50], Africa [51], Serbia [52], Bangladesh [53], Philippines [54], Pakistan [55, 56], Iran [57] Australia [58]. The occurrence of insecticide resistance in *T. castaneum* has been reported against various fumigants-methyl bromide and phosphine [59–66], synthetic pyrethroids, e.g., cypermethrin, deltamethrin, cyfluthrin, fenvalerate, and permethrin [67, 68], organophosphates [47, 52, 69, 70].

In the Indian context, the first cases of insecticide resistance were reported in 1971 by Bhatia et al. [71] who found *T*. *castaneum* collected from the Food Corporation of India*,* Delhi, to be resistant to malathion. Since then, high frequencies of insecticide resistance were recorded in *T. castaneum* collected from different storage facilities across India. Saxena et al. [72] monitored the dicholorvos resistance status of 13 samples from warehouses of the Food Corporation of India located at Mirzapur and Allahabad. The results revealed that strains from Allahabad exhibited more than ten-fold resistance compared to the Mirzapur strain. The *T. castaneum* population collected from different types of storage premises in Punjab varied in malathion resistance and was measured at a maximum in the populations of beetles collected from a public warehouse in Ropar [73].

Similarly, malathion resistance level in Indian populations of *T. castaneum* collected from thirteen different seed centres was tested and high levels of resistance were found in the Coimbatore strain. Eleven strains differed in terms of resistance levels in the range of 1.18 to 24.53 folds [74]. Insecticide resistance in *T. castaneum* has been studied in most Indian states vis-à-vis different insecticides such as malathion [75], dichlorvos [72], deltamethrin [76, 77], cypermethrin [78]. This rapid increase of resistance against different insecticide classes in India jeopardizes effective pest management strategies. The situation has only worsened with the recurring use of the same insecticide in grain storage facilities, which exert strong selection pressure on *T. castaneum* and hence reduce the efficacy of insecticides. The foregoing results confirm that the development and progression of insecticide resistance in *T. castaneum* is widespread and requires immediate solutions. Since insecticides exist as the mainstay in pest control programs, identifying the factors influencing insecticide resistance is essential in devising new and effective pest management strategies. This review presents a comprehensive picture of different resistance mechanisms and genes governing insecticide resistance in *T. castaneum*.

## **3. Insecticide resistance mechanisms in** *T. castaneum*

The emergence and spread of insecticide resistance in an insect population is a slow and gradual evolutionary process. Following the initial exposure to the insecticide, there is a latent period in which resistance genes are segregated and linked with other genes that contribute favorable conditions for resistance development. During the evolution of resistance under insecticide selection pressure, the target species show a noticeable increase of tolerance to the pesticide. In the next stage, insecticide resistance slowly develops, followed by a period of rapid development, during which many factors influence the selection of resistance to insecticides. Rapidly developing resistance results in explosive population growth of the pests in stored products that become almost impossible to control. It is challenging to detect the resistance mechanism because they emerge over evolutionary time. Many key factors such as intensive application of insecticides, control operations, mode of inheritance of resistance genes, change in fitness of individuals, and genetic background of insects influence resistance [79]. Despite species diversity and chemical diversity of insecticides, only three mechanisms are known to cause insecticide resistance in *T. castaneum:* i) Target site insensitivity, where changes in sensitivity of target site inhibit insecticide binding ii) Metabolic resistance, where the elevated quantity of enzymes lead to increased activities of metabolic detoxification iii) Lack of penetration, where cuticular thickening or cuticular modification prevents penetration of insecticides and render them bound to the target.

The advances in genomic research (e.g., transcriptomic sequencing and wholegenome sequencing) have made significant progress in understanding resistance mechanisms such as metabolic resistance, penetration resistance, and knockdown resistance in *T. castaneum.* An even more fascinating and rapidly advancing area of microbiome research that blends entomology with microbiology is the study of the potential of entire communities of bacteria, viruses, and fungi, that live within the insect hosts, to detoxify insecticides. Existing studies highlight candidate resistance mechanisms such as symbiont-mediated insecticide resistance in various insects and have documented the major bacterial taxa in the adaptation to detoxify xenobiotic compounds [80–83].

Researchers around the world have begun to evaluate the symbiotic associations in different pest populations, how they interact with their hosts and whether they have the potential to detoxify insecticides. Interestingly, bacterial symbionts have been

*Review of Insecticide Resistance and Its Underlying Mechanisms in* Tribolium castaneum *DOI: http://dx.doi.org/10.5772/intechopen.100050*

involved in insecticide degradation and resistance development in some insect pests, weeds, and nematodes. There are a growing number of reports where pest resistance to insecticides is not only due to the mechanisms within the pest genome but also due to the organisms in the microbiome community [84]. However, the microbial communities inhabiting *T. castaneum* and the unique intricate connection between symbionts and insecticide resistance have not yet been investigated. Many fundamental questions about the microbial shifts in response to insecticides and the functions of particular microorganisms in mediating resistance in *T. castaneum* remain unresolved.

#### **3.1 Target site insensitivity**

Insecticides such as organophosphates, carbamates, and pyrethroids produce neurotoxicity by inhibiting the enzyme acetylcholine esterase associated with the central nervous system [85–88]. These insecticides also affect other target sites such as voltage-gated sodium channels (VGSC) and gamma aminobutyric acid (GABA) receptors in the insect nervous system [89]. The DDT and pyrethroid insecticides primarily target VGSC in the nervous system [90]. Several potential insecticides such as cyclodienes and fipronil bind to the GABA receptor and block the receptor function [91]. Most commonly used insecticides primarily target different receptors on the nervous system (**Figure 1**).

Insecticide-resistant insects perform normal neurological functions despite the presence of insecticide because they have evolved insensitive acetylcholine receptors which provide resistance to organophosphate and carbamate insecticides. The reduced sensitivity of acetylcholinesterase to OP and carbamate insecticides has been studied in many resistant insect species of agricultural and veterinary importance [92–96]. The reduced target site sensitivity is a result of altered insecticide target molecules. There are mainly four types of target site insensitivity mechanisms observed in various insect species. These include a) Altered Acetylcholinesterase (AChE) resistance mechanism, which provides resistance to organophosphates and carbamates b) Knockdown resistance (*kdr*) mechanism which confers resistance to DDT and pyrethroids c) Reduced GABA receptor sensitivity mechanism, which causes resistance to phenylpyrazoles and cyclodienes and d) Altered nAChRs, which provide resistance to neonicotinoids [89, 97, 98].

#### **Figure 1.**

*Diagrammatic representation of pre and post synaptic neurons, showing the different target sites of most commonly used insecticide classes. Source: Adapted and modified from [85–91].*

#### *3.1.1 Altered acetylcholine esterase (AChE) resistance mechanism*

Acetylcholinesterase (AchE) is a vital enzyme required for regulating the neurotransmitter acetylcholine (ACh). It terminates the synaptic transmission by hydrolyzing acetylcholine into acetate and choline at cholinergic synapses in insects [99]. The inhibition of AchE increases the concentration of the acetylcholine at the synaptic cleft, which leads to a prolonged binding of ACh to its postsynaptic receptor. The high quantity of acetylcholine at the postsynaptic receptor causes neuroexcitation and produces intoxication symptoms such as tremors, convulsions, and eventually paralysis-related death. This enzyme is a target site of organophosphates and carbamates insecticides, which are bound to a serine residue on the active site of AchEs and convert the AchEs into their non-functional form. This causes the accumulation of acetylcholine at the nerve endings and disrupts nerve activity, resulting in paralysis and the death of insects [100].

Several Organophosphorous compounds have been used to protect agricultural commodities from insect infestation. But most of the insects have developed resistance against these insecticides due to insensitive AchE. Target insensitivity of AchE to insecticides occurs through the mutations in the active site of "*Ace*" genes that encode acetylcholinesterase enzyme, which is the most common reason for conferring insecticide resistance. Because of the incessant application of insecticides, different mutations are induced in the *Ace* genes either singly or in combination, which reduces the sensitivity of AchE to the insecticides [101, 102]. The first major research in insects was conducted on *Drosophila melanogaster* that mapped the *Ace* locus at the molecular level and the genomic sequencing effort confirmed that *Ace* encodes the acetylcholinesterase enzyme [103]. The existence of the *Ace* gene and its genome structure has been identified in many insects such as *Drosophila melanogaster* [104], *Musca domestica* [105], *Anopheles gambiae* [106], *Lucilia cuprina* [107], *Tribolium castaneum* [108], *Pieris rapae* [109]*, Sitobion avenae* [110], *Bemisia tabaci* [111], *Bombyx mori* [112]*, Aphis gossypii* [113], *Plutella xylostella* [114], *Blattella germanica* [115]*, Aedes aegypti* [116]. The gene sequence and genomic organization of *Ace* genes in different insects revealed that most insect species possess two *Ace* genes (*Ace* 1 and *Ace* 2) except *Drosophila melanogaster, M. domestica,* and *Lucilia cuprina.* The introduction of the point mutation in *Ace* genes through single amino acid substitution reduces the sensitivity of AChE to insecticides inhibition. These insensitive acetylcholinesterases impart resistance to carbamate and organophosphorus insecticides.

Recent evidence for resistance-conferring mutations in *Ace* genes has focussed on their involvement in insecticide resistance and their biochemical and physiological properties in different insects. Lu et al. [108] studied the genome organization, expression patterns, phylogenies, and three-dimensional models of two *Ace* genes in *T. castaneum* extensively to better understand the functional role of *Ace* genes and the molecular basis of insecticide resistance. The gene sequencing and comparative analysis of AChE1 and AchE2 genes (*Tcas Ace 1 & Tcas Ace 2*) in *Tribolium* revealed that both genes possess different features in the length of their genomic DNA, chromosome locations, and intron/exon organizations. Sequencing full-length cDNAs of AChE genes showed that AChE1 is distributed on chromosome 5 and AchE2 on chromosome 2. In addition, AChE1 consisted of one intron, whereas AchE2 consisted of five introns. Further, extensive protein simulation studies provided evidence that AChE1 has been associated with the hydrolysis of acetylcholine, whereas AChE2 has not been involved in the hydrolysis of acetylcholinesterase substrates. This novel finding prompted Lu et al. [108] to investigate the functional differences of two AChE genes in cholinergic, non-cholinergic neurotransmission, and insecticide resistance by gene-silencing in *T. castaneum*.

*Review of Insecticide Resistance and Its Underlying Mechanisms in* Tribolium castaneum *DOI: http://dx.doi.org/10.5772/intechopen.100050*

RNAi results of both *TcAce1* and *TcAce2* in *T. castaneum* larvae were consistent with the observations of protein modeling studies. Thus, the protein simulation studies of AchE coupled with RNAi experiments have proved that AChE1 is essential for cholinergic neurotransmission and is the target for anticholinesterase insecticides such as organophosphorous and carbamates, which disable the hydrolysis activity of AchE1and cause incapacitation. Whereas, AChE2 is not responsible for neurotransmission in *T. castaneum.* This study also suggested that genetic modifications of AchE1 are most likely responsible for the insensitivity of acetylcholinesterase to organophosphorus and carbamate insecticides. It is remarkable that target site insensitivity is due to different mutations (mainly point mutation) at the catalytic sites of AchEs and conferred resistance in *Drosophila melanogaster* [117], *M. domestica* [105], *Bactrocera oleae* [118], *Leptinotarsa decemlineata* [119], *Chilo auricilius* [120], *Apolygus lucorum* [121]. Sequencing of the gene encoding AchE has generated insights on different point mutations which causes the alteration of AchE genes and a decrease in the sensitivity of anti-cholinesterases insecticides inhibition. In addition, the efficacy of organophosphates and carbamates has been challenged by multiple mutations in the same AchEs of the insects [117, 122]. Thus, the point mutations and multiple mutations result in decreased hydrolytic efficiency of AchE and are associated with insecticide resistance.

#### *3.1.2 Knockdown resistance (kdr) mechanism*

Over the years, Pyrethroids have come to be the most sought-after class of insecticides for pest control in commercial and household environments because of their affordable and durable qualities [123]. However, their utility has been limited by the widespread development of insecticide resistance in many major pests. Pyrethroids are synthetic derivatives of pyrethrin, and the pyrethroids were classified into two groups namely class I and class II based on their physical characteristics and knockdown effect against insects. Class I pyrethroids contain a basic structure of cyclopropane carboxylic ester. These compounds include permethrin, resmethrin, phenothrin, bifenthrin, allethrin, tefluthrin, and tetramethrin. Class II pyrethroids contain a cyano group and these compounds include cypermethrin, deltamethrin, cyhalothrin, fenvalerate, cyfluthrin, fenpropathrin, flumethrin. The toxicity of pyrethroids was found to be 2250 times higher in insects than mammals due to their increased sodium channel sensitivity, lower body temperature, and smaller structure [124]. When an insect is intoxicated with non-cyano pyrethroids (class I), it produces strong excitatory action and tremors on the nervous system. The cyano pyrethroids trigger a quite different action, which includes salivation and choreoathetosis. It has been suggested that poisoning symptoms differ based on the cyano or non-cyano pyrethroids [125]. The pyrethrin and pyrethroid insecticides primarily target the VGSC in the nervous system. Pyrethroids and DDT produce their toxicity by binding onto the voltage-gated channels in axonal membranes, altering their gating properties, and the channels remain open for a long time. This causes a prolonged sodium influx, thereby depolarizing the axonal membrane and stimulating the neurons to produce repetitive discharges, finally resulting in paralysis [90, 126]. In insects, modification of voltage-gated sodium channel structure by point mutation or substitution causes insensitivity and reduces the binding affinity of the insecticides to protein.

Knockdown resistance (*kdr*) is one of the major mechanisms involved in resistance to all pyrethroids, pyrethrins, DDT, and its analogs [127]. The *kdr* resistance was first identified in the house fly [126]. Since then, *kdr* has been described in several insects against pyrethroids and an organochlorine class of insecticides [128]. Knockdown resistance occurs due to different point mutations in voltage-gated

sodium channels. These sodium channels are composed of a larger *α*-subunit (260 kDa) and smaller *β*-subunits (30–40 kDa). The pore-forming *α*-subunit has four homologous domains (I – IV), and each domain possesses six transmembrane helices (S1 – S6). The domains are joined together to form a central aqueous pore and the pore is lined by S5, S6 linkers, and S5, S6 helices. In each domain, the S4 segment is involved in voltage sensing, and a positively charged amino acid residue is embedded in every third position [129, 130]. In mammals, the sodium channel encodes nine genes [131] whereas, in insects, the sodium channel encodes only a single gene known as *para* [132, 133]. However, *para* undergoes an extensive alternative splicing process to increase the heterogeneity and functional diversity of sodium channel [133, 134]. These distinct variants of the *para* sodium channel in insects produce different levels of sensitivity to pyrethroids and DDT. The different amino acid substitutions in the para sodium channel variants of nerve membranes have been demonstrated in *M. domestica* [135, 136], *Blatella germanica* [137, 138], *Ctenocephalides felis* [139], *Drosophila melanogaster* [140] that render the loss of sensitivity to pyrethroids. The secondary mutation designated as *super kdr* has been identified mostly in the domain II region of the sodium channel and reported in *B. tabaci* [141], *Haematobia irritans* [142], *Plutella xylostella* [143] which confers enhanced resistance to pyrethroids. The occurrence of the *kdr* mutations in voltagegated sodium channels limits the efficacy of pyrethroids and it remains a threat to the control of *T. castaneum* [144]. This has been earlier reinforced by the functional expression studies of voltage-gated sodium channel paralytic A gene (*TcNa*v) of *T. castaneum.* RNAi-induced knockdown results reveal that the *TcNa*v gene of *T. castaneum* is a potential candidate to target for the future control of *T. castaneum* and lends support for the use of RNAi as a viable method for controlling this insect [139]. The results of this study provide convincing evidence which shows that pyrethroid resistance in *T. castaneum* correlated with the presence of point mutations in the sodium channel *para* gene of insect nervous membrane. Thus, the identification of *kdr* mutations provides insights into the resistance mechanism in *T. castaneum* and has also proven critical for designing new insecticides for insect control.