Estimating Children’s Personalities Through Their Interaction Activities with a Tele-Operated Robot

2020 ◽  
Vol 32 (1) ◽  
pp. 21-31
Author(s):  
Kasumi Abe ◽  
Takayuki Nagai ◽  
Chie Hieida ◽  
Takashi Omori ◽  
Masahiro Shiomi ◽  
...  
Keyword(s):  
Big Five ◽  
Eye Contact ◽  
Face Image ◽  
Big Five Inventory ◽  
The Face ◽  
Close Distance

Based on the little big-five inventory, we developed a technique to estimate children’s personalities through their interaction with a tele-operated childcare robot. For personality estimation, our approach observed not only distance-based but also face-image-based features when a robot interacted with a child at a close distance. We used only the robot’s sensors to track the child’s positions, detect its eye contact, and estimate how much it smiled. We collected data from a kindergarten, where each child individually interacted for 30 min with a robot that was controlled by the teachers. We used 29 datasets of the interaction between a child and the robot to investigate whether face-image-based features improved the performance of personality estimation. The evaluation results demonstrated that the face-image-based features significantly improved the performance of personality estimation, and the accuracy of the personality estimation of our system was 70% on average for the personality scales.

Assessing Personality With Multi-Descriptor Items

2019 ◽  
Vol 35 (1) ◽  
pp. 117-125
Author(s):  
Johannes Schult ◽  
Rebecca Schneider ◽  
Jörn R. Sparfeldt
Keyword(s):  
University Students ◽  
Construct Validity ◽  
Big Five ◽  
Reliability And Validity ◽  
Personality Inventory ◽  
The Other ◽  
Short Version ◽  
Big Five Inventory ◽  
Personality Inventories

Abstract. The need for efficient personality inventories has led to the wide use of short instruments. The corresponding items often contain multiple, potentially conflicting descriptors within one item. In Study 1 ( N = 198 university students), the reliability and validity of the TIPI (Ten-Item Personality Inventory) was compared with the reliability and validity of a modified TIPI based on items that rephrased each two-descriptor item into two single-descriptor items. In Study 2 ( N = 268 university students), we administered the BFI-10 (Big Five Inventory short version) and a similarly modified version of the BFI-10 without two-descriptor items. In both studies, reliability and construct validity values occasionally improved for separated multi-descriptor items. The inventories with multi-descriptor items showed shortcomings in some factors of the TIPI and the BFI-10. However, the other scales worked comparably well in the original and modified inventories. The limitations of short personality inventories with multi-descriptor items are discussed.

Measurement invariance of a new Big Five Inventory across five countries

2011 ◽  
Author(s):  
Markus Sommer ◽  
Martin Arendasy ◽  
Elke Gruber ◽  
Fritz Mayr
Keyword(s):  
Measurement Invariance ◽  
Big Five ◽  
Big Five Inventory

Network analysis of the big five inventory: Application in the context of a twin and family study

2014 ◽  
Author(s):  
Daniel A. Briley ◽  
Jennifer L. Tackett ◽  
K. Paige Harden ◽  
Elliot M. Tucker-Drob
Keyword(s):  
Network Analysis ◽  
Big Five ◽  
Family Study ◽  
Big Five Inventory

Big Five Inventory–2--Dutch Version

2020 ◽  
Author(s):  
Jaap J. A. Denissen ◽  
Rinie Geenen ◽  
Christopher J. Soto ◽  
Oliver P. John ◽  
Marcel A. G. van Aken
Keyword(s):  
Big Five ◽  
Dutch Version ◽  
Big Five Inventory

Research on Improved Gamma Transform Face Image Preprocessing Fusion Algorithm under Complex Lighting Conditions

2020 ◽  
Vol 13 ◽  
Author(s):  
Xiaolin Tang ◽  
Xiaogang Wang ◽  
Jin Hou ◽  
Huafeng Wu ◽  
Ping He
Keyword(s):  
Edge Detection ◽  
Face Image ◽  
Light Sources ◽  
Fusion Algorithm ◽  
Edge Information ◽  
Image Preprocessing ◽  
Lighting Conditions ◽  
Weighted Fusion ◽  
The Face ◽  
Sobel Edge Detection

Introduction: Under complex illumination conditions such as poor light sources and light changes rapidly, there are two disadvantages of current gamma transform in preprocessing face image: one is that the parameters of transformation need to be set based on experience; the other is the details of the transformed image are not obvious enough. Objective: Improve the current gamma transform. Methods: This paper proposes a weighted fusion algorithm of adaptive gamma transform and edge feature extraction. First, this paper proposes an adaptive gamma transform algorithm for face image preprocessing, that is, the parameter of transformation generated by calculation according to the specific gray value of the input face image. Secondly, this paper uses Sobel edge detection operator to extract the edge information of the transformed image to get the edge detection image. Finally, this paper uses the adaptively transformed image and the edge detection image to obtain the final processing result through a weighted fusion algorithm. Results: The contrast of the face image after preprocessing is appropriate, and the details of the image are obvious. Conclusion: The method proposed in this paper can enhance the face image while retaining more face details, without human-computer interaction, and has lower computational complexity degree.

Whos Chatting?: Interplay between Personality and WhatsApp Use

2015 ◽  
Vol 4 (4) ◽  
Author(s):  
Himanshu Rajput
Keyword(s):  
Logistic Regression ◽  
Multiple Regression ◽  
Big Five ◽  
Personality Assessment ◽  
The Internet ◽  
Big Five Inventory ◽  
Significant Relationships ◽  
Indian Context ◽  
High Penetration ◽  
The Relationship

Smartphone-based messaging applications have shown phenomenal growth with the proliferation of the internet coupled with the high penetration of smartphones into masses. The current study is an attempt to understand the relationship between the individuals personality and their use of WhatsApp, a popular smartphone-based messaging application in Indian context. For personality assessment the study takes Big Five Inventory. A questionnaire consisting items on individual WhatsApp use and Big Five Inventory was administered to students in an Indian University. Multiple regression and logistic regression revealed significant relationships between personality and WhatsApp usage and use of its different inbuilt functions.

The Big Five Inventory–2 in China: A Comprehensive Psychometric Evaluation in Four Diverse Samples

Assessment ◽  
2021 ◽  
pp. 107319112110082
Author(s):  
Bo Zhang ◽  
Yi Ming Li ◽  
Jian Li ◽  
Jing Luo ◽  
Yonghao Ye ◽  
...  
Keyword(s):  
Big Five ◽  
Discriminant Validity ◽  
Psychometric Evaluation ◽  
Good Reliability ◽  
Future Directions ◽  
Big Five Inventory ◽  
Education Levels ◽  
Lower Education ◽  
Content Coverage ◽  
Test Retest Reliability

The Big Five Inventory-2 (BFI-2) has received wide recognition since its publication because it strikes a good balance between content coverage and brevity. The current study translated the BFI-2 into Chinese, evaluated its psychometric properties in four diverse Chinese samples (college students, adult employees, adults treated for substance use, and adolescents), and compared its factor structure with those obtained from two U.S. samples. Across two studies, the Chinese BFI-2 demonstrated good reliability (Cronbach’s α and test–retest reliability), structural validity, convergent/discriminant validity, and criterion-related validity at the domain level. At lower levels of analyses, some facets and negatively worded items functioned better among participants with higher than those with lower education levels. Implications, limitations, and future directions are discussed.

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