Detecting and Classifying Human Touches in a Social Robot Through Acoustic Sensing and Machine Learning
| Autor: | Fernando Alonso-Martín, Juan José Gamboa-Montero, José Carlos Castillo, Miguel A. Salichs, Álvaro Castro-González |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Engineering Microphone touch interaction Robótica e Informática Industrial 02 engineering and technology Machine learning computer.software_genre Biochemistry Robot learning Acoustic sensing Article Analytical Chemistry Machine Learning human-robot interaction 020901 industrial engineering & automation contact microphone 0202 electrical engineering electronic engineering information engineering Humans Computer vision Electrical and Electronic Engineering Instrumentation Social robot acoustic sensing Gestures business.industry Robotics Acoustics Atomic and Molecular Physics and Optics machine learning Gesture recognition Touch Contact microphone Robot 020201 artificial intelligence & image processing Artificial intelligence Touch interaction business Human-robot interaction computer Gesture |
| Zdroj: | e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid instname Sensors (Basel, Switzerland) Sensors; Volume 17; Issue 5; Pages: 1138 |
| DOI: | 10.3390/s17051138 |
| Popis: | An important aspect in Human-Robot Interaction is responding to different kinds of touch stimuli. To date, several technologies have been explored to determine how a touch is perceived by a social robot, usually placing a large number of sensors throughout the robot's shell. In this work, we introduce a novel approach, where the audio acquired from contact microphones located in the robot's shell is processed using machine learning techniques to distinguish between different types of touches. The system is able to determine when the robot is touched (touch detection), and to ascertain the kind of touch performed among a set of possibilities: stroke, tap, slap, and tickle (touch classification). This proposal is cost-effective since just a few microphones are able to cover the whole robot's shell since a single microphone is enough to cover each solid part of the robot. Besides, it is easy to install and configure as it just requires a contact surface to attach the microphone to the robot's shell and plug it into the robot's computer. Results show the high accuracy scores in touch gesture recognition. The testing phase revealed that Logistic Model Trees achieved the best performance, with an F-score of 0.81. The dataset was built with information from 25 participants performing a total of 1981 touch gestures. The research leading to these results has received funding from the projects: Development of social robots to help seniors with cognitive impairment (ROBSEN), funded by the Ministerio de Economia y Competitividad; and RoboCity2030-III-CM, funded by Comunidad de Madrid and cofunded by Structural Funds of the EU. Publicado |
| Databáze: | OpenAIRE |
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