An empirical study of machine learning techniques for affect recognition in human-robot interaction

2005 ◽  
Author(s):  
Changchun Liu ◽  
P. Rani ◽  
N. Sarkar
Keyword(s):  
Machine Learning ◽  
Empirical Study ◽  
Human Robot Interaction ◽  
Machine Learning Techniques ◽  
Affect Recognition ◽  
Robot Interaction ◽  
Learning Techniques

scholarly journals Redefining Safety in Light of Human-Robot Interaction: A Critical Review of Current Standards and Regulations

2021 ◽  
Vol 3 ◽  
Author(s):  
Alberto Martinetti ◽  
Peter K. Chemweno ◽  
Kostas Nizamis ◽  
Eduard Fosch-Villaronga
Keyword(s):  
Machine Learning ◽  
Human Robot Interaction ◽  
Cloud Services ◽  
Machine Learning Techniques ◽  
Physical Interaction ◽  
Robot Interaction ◽  
Learning Techniques ◽  
Standards And Regulations ◽  
Physical Impact ◽  
Definition Of

Policymakers need to consider the impacts that robots and artificial intelligence (AI) technologies have on humans beyond physical safety. Traditionally, the definition of safety has been interpreted to exclusively apply to risks that have a physical impact on persons’ safety, such as, among others, mechanical or chemical risks. However, the current understanding is that the integration of AI in cyber-physical systems such as robots, thus increasing interconnectivity with several devices and cloud services, and influencing the growing human-robot interaction challenges how safety is currently conceptualised rather narrowly. Thus, to address safety comprehensively, AI demands a broader understanding of safety, extending beyond physical interaction, but covering aspects such as cybersecurity, and mental health. Moreover, the expanding use of machine learning techniques will more frequently demand evolving safety mechanisms to safeguard the substantial modifications taking place over time as robots embed more AI features. In this sense, our contribution brings forward the different dimensions of the concept of safety, including interaction (physical and social), psychosocial, cybersecurity, temporal, and societal. These dimensions aim to help policy and standard makers redefine the concept of safety in light of robots and AI’s increasing capabilities, including human-robot interactions, cybersecurity, and machine learning.

scholarly journals A Survey on Emotion Recognition for Human Robot Interaction

2021 ◽  
Vol 28 (2) ◽  
pp. 125-146
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Human Robot Interaction ◽  
Machine Learning Techniques ◽  
Emotional States ◽  
Robot Interaction ◽  
Artificial Intelligent ◽  
Learning Techniques ◽  
Human Counterpart ◽  
Recent Developments

With the recent developments of technology and the advances in artificial intelligent and machine learning techniques, it becomes possible for the robot to acquire and show the emotions as a part of Human-Robot Interaction (HRI). An emotional robot can recognize the emotional states of humans so that it will be able to interact more naturally with its human counterpart in different environments. In this article, a survey on emotion recognition for HRI systems has been presented. The survey aims to achieve two objectives. Firstly, it aims to discuss the main challenges that face researchers when building emotional HRI systems. Secondly, it seeks to identify sensing channels that can be used to detect emotions and provides a literature review about recent researches published within each channel, along with the used methodologies and achieved results. Finally, some of the existing emotion recognition issues and recommendations for future works have been outlined.

scholarly journals The Scientific Study of Passive Thinking: The Methodology of Mind Wandering Research

2020 ◽  
Author(s):  
Samuel Murray ◽  
Zachary Irving ◽  
Kristina Krasich
Keyword(s):  
Machine Learning ◽  
Empirical Study ◽  
Eye Tracking ◽  
Mind Wandering ◽  
Self Report ◽  
Machine Learning Techniques ◽  
Objective Measures ◽  
Learning Techniques ◽  
Self Reports ◽  
Direct Sensitivity

In this chapter, we survey methodological challenges in the empirical study of mind wandering and provide a metaphysical framework that begins to address these challenges. We argue that mind wandering is a passive manifestation of agency—passive because people cannot mind wander on command and a manifestation of agency because the onset, progression, and content of mind wandering often exhibits direct sensitivity to personal concerns and plans. To measure passive thinking, researchers must ask, “Is your mind wandering?” Worries about this self-report methodology have encouraged researchers to develop “objective” measures of mind wandering through eye tracking and machine learning techniques. These “objective” measures, however, are validated in terms of how well they predict self-reports, which means that purportedly objective measures of mind wandering retain a subjective core. To assuage worries about self-report (and, ultimately, vindicate objective measures of mind wandering), we offer a metaphysical account of mind wandering that generates several predictions about its causes and consequences. This account also justifies different methods for measuring mind wandering.

Compound or phrase or in between? Testing linguistic criteria for compoundhood in English

2020 ◽  
Vol 13 (2) ◽  
pp. 250-281
Author(s):  
Patrick Ziering ◽  
Lonneke van der Plas
Keyword(s):  
Machine Learning ◽  
Empirical Study ◽  
European Parliament ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Nominal Compounds ◽  
Definition Of

In this paper, we present an empirical study on the definition of compounds in English, the graded nature of the phenomenon and its correlations with the commonly used linguistic criteria for compoundhood. We create a resource that includes a diverse set of nominal compounds identified by two trained independent annotators in sentences from the proceedings of the European Parliament. In addition, the annotators provide ratings on the compoundhood of the identified compounds, and ratings for the applicability of six prominent linguistic criteria of compoundhood for each item. We show the controversy of defining compounds in practice by comparing the annotations of two annotators, and the graded nature of compoundhood. By measuring the correlation between compoundhood and the six diverse linguistic criteria using machine learning techniques, we show that some linguistic criteria are stronger predictors of compoundhood than others.

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