Effective emotional expressions with emotion expression humanoid robot WE-4RII

Miwa, Hiroyoshi; Itoh, Kazuko; Matsumoto, Mitsuharu; Zecca, Massimiliano; Takanobu, Hideaki; Rocella, S.; Carrozza, M.C.; Dario, P.; Takanishi, Atsuo

doi:10.1109/iros.2004.1389736

Cited by 95 publications

(58 citation statements)

References 6 publications

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“…These properties are available by birth in humans but to build these modalities in humanoid robots, they should be equipped with expressive animated heads. For example WE-4RII developed at Waseda (Miwa et al, 2004). When humanoid robots look at the opposing user, then user understand that robot is interacting with him.…”

Section: Interactionmentioning

confidence: 99%

A review on humanoid robots

Mahum¹,

Butt²,

Ayyub³

et al. 2017

Int. j. adv. appl. sci

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Humanoid robots with artificial intelligence is very captivating field for people, since the robots are introduced. For humanoid robots we can introduce new ideas without any limitations and constraints but in reality there are limitations to implement them. In this paper we are discussing different humanoid robots with their practical applications. Based on their implementation structure detailed comparison of their characteristics is performed and on this bases some limitations and future work has been discussed.

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Section: Interactionmentioning

confidence: 99%

A review on humanoid robots

Mahum¹,

Butt²,

Ayyub³

et al. 2017

Int. j. adv. appl. sci

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“…With emotion recognition ability, machines such as computers, robots, toys and game consoles will have the capability to perform in such a way as to influence the user in adaptive ways relevant for the client's mental condition. This is the key knowledge in recently proposed new ideas such as emotional computers, emotion-sensing smart phones and emotional robots [4]. Over the last decade, most studies have focused on emotional symptoms in facial expressions.…”

Section: Introductionmentioning

confidence: 99%

“…This helped to grow face-detection and face-tracking algorithms in the early 1990s. At the same time, human-computer interaction (HCI) and affective computing (AC) research began [4].…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Emotion Detection for Real-Time Embedded Systems

et al. 2018

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Abstract:Recently, real-time facial expression recognition has attracted more and more research. In this study, an automatic facial expression real-time system was built and tested. Firstly, the system and model were designed and tested on a MATLAB environment followed by a MATLAB Simulink environment that is capable of recognizing continuous facial expressions in real-time with a rate of 1 frame per second and that is implemented on a desktop PC. They have been evaluated in a public dataset, and the experimental results were promising. The dataset and labels used in this study were made from videos, which were recorded twice from five participants while watching a video. Secondly, in order to implement in real-time at a faster frame rate, the facial expression recognition system was built on the field-programmable gate array (FPGA). The camera sensor used in this work was a Digilent VmodCAM -stereo camera module. The model was built on the Atlys TM Spartan-6 FPGA development board. It can continuously perform emotional state recognition in real-time at a frame rate of 30. A graphical user interface was designed to display the participant's video in real-time and two-dimensional predict labels of the emotion at the same time.

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“…Consciousness has been studied extensively in brain science, neural science, psychology, philosophy, others academic fields and recently in robotics [1][2][3][4][5][6][7][8][9]. Various robots that can communicate with consciousness and feeling functions have been reported by many researchers and companies [10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Learning Efficiency of Consciousness System for Robot Using Artificial Neural Network

Shoubaky

Sharari²

2014

J. Mechatron. Electr. Power Veh. Technol.

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This paper presents learning efficiency of a consciousness system for robot using artificial neural network. The proposed conscious system consists of reason system, feeling system and association system. The three systems are modeled using Module of Nerves for Advanced Dynamics (ModNAD). Artificial neural network of the type of supervised learning with the back propagation is used to train the ModNAD. The reason system imitates behaviour and represents self-condition and other-condition. The feeling system represents sensation and emotion. The association system represents behaviour of self and determines whether self is comfortable or not. A robot is asked to perform cognition and tasks using the consciousness system. Learning converges to about 0.01 within about 900 orders for imitation, pain, solitude and the association modules. It converges to about 0.01 within about 400 orders for the comfort and discomfort modules. It can be concluded that learning in the ModNAD completed after a relatively small number of times because the learning efficiency of the ModNAD artificial neural network is good. The results also show that each ModNAD has a function to imitate and cognize emotion. The consciousness system presented in this paper may be considered as a fundamental step for developing a robot having consciousness and feelings similar to humans.

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Effective emotional expressions with emotion expression humanoid robot WE-4RII

Cited by 95 publications

References 6 publications

A review on humanoid robots

A review on humanoid robots

Facial Expression Emotion Detection for Real-Time Embedded Systems

Learning Efficiency of Consciousness System for Robot Using Artificial Neural Network

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