A ConvNet-Based Approach Applied to the Gesticulation Control of a Social Robot

Arias, Edisson; Encalada, Patricio; Tigre, Franklin; Granizo, Cesar; Gordón, Carlos; García, Marcelo V.

doi:10.1007/978-3-030-32022-5_18

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…Euclid achieved 7/10 for aesthetical authenticity, which is the highest frequency in the subset and 7/10 for realistic speech. These results are particularly encouraging as Euclid achieved, on average, high ratings across all five examination categories of the dataset and higher in functional accuracy than [11], recent robotic mouth prototype for RHRs.…”

Section: Figure 10 Rhr Junko Chihiramentioning

confidence: 73%

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A Novel Speech to Mouth Articulation System for Realistic Humanoid Robots

Strathearn

2021

J Intell Robot Syst

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A significant ongoing issue in realistic humanoid robotics (RHRs) is inaccurate speech to mouth synchronisation. Even the most advanced robotic systems cannot authentically emulate the natural movements of the human jaw, lips and tongue during verbal communication. These visual and functional irregularities have the potential to propagate the Uncanny Valley Effect (UVE) and reduce speech understanding in human-robot interaction (HRI). This paper outlines the development and testing of a novel Computer Aided Design (CAD) robotic mouth prototype with buccinator actuators for emulating the fluidic movements of the human mouth. The robotic mouth system incorporates a custom Machine Learning (ML) application that measures the acoustic qualities of speech synthesis (SS) and translates this data into servomotor triangulation for triggering jaw, lip and tongue positions. The objective of this study is to improve current robotic mouth design and provide engineers with a framework for increasing the authenticity, accuracy and communication capabilities of RHRs for HRI. The primary contributions of this study are the engineering of a robotic mouth prototype and the programming of a speech processing application that achieved a 79.4% syllable accuracy, 86.7% lip synchronisation accuracy and 0.1s speech to mouth articulation differential.

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Section: Figure 10 Rhr Junko Chihiramentioning

confidence: 73%

“…The human mouth consists of a fine network of muscular fibres surrounding the upper and lower jaw structure of the mouth and cheek/neck bones [11]. The, orbicularis oris muscle group indicated in Fig .…”

Section: Human Mouth Muscle Configurationmentioning

confidence: 99%

A Novel Speech to Mouth Articulation System for Realistic Humanoid Robots

Strathearn

2021

J Intell Robot Syst

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“…The communication protocol is made up of a character sequence that indicates the desired motor, speed, and direction of the motors. 45 The algorithm to control the robot movements is presented in Algorithm 1. Here, the hexapod robot initially is in a stop position.…”

Section: Robot Platformsmentioning

confidence: 99%

“…The communication protocol is made up of a character sequence that indicates the desired motor, speed, and direction of the motors. 45…”

Section: Brain–computer Interface Architecturementioning

confidence: 99%

Versatile implementation of a hardware–software architecture for development and testing of brain–computer interfaces

Martinez-Ledezma

Barrón-Zambrano

Diaz-Manríquez

et al. 2020

International Journal of Advanced Robotic Systems

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Brain–computer interfaces (BCI) have been focused on improving people’s lifestyles with motor or communication disabilities. However, the utilization of this technology has found news applications, such as increasing human capacities. Nowadays, several researchers are working on probing human capabilities to control several robotic devices simultaneously. The design of BCI is an intricate work that needs a long time to its implementation. For this reason, an architecture to design and implement different types of BCIs is presented in this article. The architecture has a modular design capable of reading various electroencephalography (EEG) sensors and controlling several robotic devices similar to the plug-and-play paradigm. To test the proposed architecture, a BCI was able to manage a hexapod robot and a drone was implemented. Firstly, a mobile robotic platform was designed and implemented. The BCI is based on eye blinking, where a single blinking represents a robot command. The command orders the robot to initiate or stops their locomotion for the hexapod robot. For the drone, a blink represents the takeoff or landing order. The blinking signals are obtained from the prefrontal and frontal regions of the head by EEG sensors. The signals are then filtered using temporal filters, with cutoff frequencies based on delta, theta, alpha, and beta waves. The filtered signals were labeled and used to train a classifier based on the multilayer perceptron (MLP) model. To generate the robot command, the proposal BCI used two models of MLP to ensure the classifier prediction. So, when the two classifiers make the same prediction, within a defined time interval, send the signal to the robot to start or stop its movement. The obtained results show that it is possible to get high precision to control the hexapod robot with a precision of 91.7% and an average of 81.4%.

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