Applications of Affective Computing in Human-Robot Interaction: state-of-art and challenges for manufacturing

The introduction of collaborative robots aims to make production more flexible, promoting a greater interaction between humans and robots also from physical point of view. However, working closely with a robot may lead to the creation of stressful situations for the operator, which can negatively affect task performance. In Human-Robot Interaction (HRI), robots are expected to be socially intelligent, i.e., capable of understanding and reacting accordingly to human social and affective clues. This ability can be exploited implementing affective computing, which concerns the development of systems able to recognize, interpret, process, and simulate human affects. Social intelligence is essential for robots to establish a natural interaction with people in several contexts, including the manufacturing sector with the emergence of Industry 5.0. In order to take full advantage of the human-robot collaboration, the robotic system should be able to perceive the psycho-emotional and mental state of the operator through different sensing modalities (e.g., facial expressions, body language, voice, or physiological signals) and to adapt its behaviour accordingly. The development of socially intelligent collaborative robots in the manufacturing sector can lead to a symbiotic human-robot collaboration, arising several research challenges that still need to be addressed. The goals of this paper are the following: (i) providing an overview of affective computing implementation in HRI; (ii) analyzing the state-of-art on this topic in different application contexts (e.g., healthcare, service applications, and manufacturing); (iii) highlighting research challenges for the manufacturing sector

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 324)

This bibliography lists 200 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during May, 1989. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

A Classification Model for Sensing Human Trust in Machines Using EEG and GSR

Today, intelligent machines \emph{interact and collaborate} with humans in a way that demands a greater level of trust between human and machine. A first step towards building intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable machines to estimate human trust level in real-time. In this paper, two approaches for developing classifier-based empirical trust sensor models are presented that specifically use electroencephalography (EEG) and galvanic skin response (GSR) measurements. Human subject data collected from 45 participants is used for feature extraction, feature selection, classifier training, and model validation. The first approach considers a general set of psychophysiological features across all participants as the input variables and trains a classifier-based model for each participant, resulting in a trust sensor model based on the general feature set (i.e., a "general trust sensor model"). The second approach considers a customized feature set for each individual and trains a classifier-based model using that feature set, resulting in improved mean accuracy but at the expense of an increase in training time. This work represents the first use of real-time psychophysiological measurements for the development of a human trust sensor. Implications of the work, in the context of trust management algorithm design for intelligent machines, are also discussed.Comment: 20 page

Multimodal adaptive interfaces for 3D robot-mediated upper limb neuro-rehabilitation: An overview of bio-cooperative systems

n/

Space Station Freedom automation and robotics: An assessment of the potential for increased productivity

This report presents the results of a study performed in support of the Space Station Freedom Advanced Development Program, under the sponsorship of the Space Station Engineering (Code MT), Office of Space Flight. The study consisted of the collection, compilation, and analysis of lessons learned, crew time requirements, and other factors influencing the application of advanced automation and robotics, with emphasis on potential improvements in productivity. The lessons learned data collected were based primarily on Skylab, Spacelab, and other Space Shuttle experiences, consisting principally of interviews with current and former crew members and other NASA personnel with relevant experience. The objectives of this report are to present a summary of this data and its analysis, and to present conclusions regarding promising areas for the application of advanced automation and robotics technology to the Space Station Freedom and the potential benefits in terms of increased productivity. In this study, primary emphasis was placed on advanced automation technology because of its fairly extensive utilization within private industry including the aerospace sector. In contrast, other than the Remote Manipulator System (RMS), there has been relatively limited experience with advanced robotics technology applicable to the Space Station. This report should be used as a guide and is not intended to be used as a substitute for official Astronaut Office crew positions on specific issues

An Approach to Quantify Workload in a System of Agents

The role of humans in aviation and other domains continues to shift from manual control to automation monitoring. Studies have found that humans are often poorly suited for monitoring roles, and workload can easily spike in off-nominal situations. Current workload measurement tools, like NASA TLX, use human operators to assess their own workload after using a prototype system. Such measures are used late in the design process and can result in ex- pensive alterations when problems are discovered. Our goal in this work is to provide a quantitative workload measure for use early in the design process. We leverage research in human cognition to de ne metrics that can measure workload on belief-desire-intentions based multi-agent systems. These measures can alert designers to potential workload issues early in design. We demonstrate the utility of our approach by characterizing quantitative differences in the workload for a single pilot operations model compared to a traditional two pilot model

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 325)

This bibliography lists 192 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during June, 1989. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Comparing Types Of Adaptive Automation Within A Multi-tasking Environment

Throughout the many years of research examining the various effects of automation on operator performance, stress, workload, etc., the focus has traditionally been on the level of automation, and the invocation methods used to alter it. The goal of the current study is to instead examine the utilization of various types of automation with the goal of better meeting the operator’s cognitive needs, thus improving their performance, workload, and stress. The task, control of a simulated unmanned robotic system, is designed to specifically stress the operator’s visual perception capabilities to a greater degree. Two types of automation are implemented to support the operator’s performance of the task: an auditory beep aid intended to support visual perception resources, and a driving aid automating control of the vehicle’s navigation, offloading physical action execution resources. Therefore, a comparison can be made between types of automation intended to specifically support the mental dimension that is under the greatest demand (the auditory beep) against those that do not (the driving automation). An additional evaluation is made to determine the benefit of adaptively adjusting the level of each type of automation based on the current level of task demand, as well as the influence of individual differences in personality. Results indicate that the use of the auditory beep aid does improve performance, but also increases Temporal Demand and Effort. Use of driving automation appears to disengage the operator from the task, eliciting a vigilance response. Adaptively altering the level of automation to meet task demands has a mixed effect on performance and workload (reducing both) when the auditory beep automation is used. However, adaptive driving automation is clearly detrimental, iv causing an increase in workload while decreasing performance. Higher levels of Neuroticism are related to poorer threat detection performance, but personality differences show no indication of moderating the effects of either of the experimental manipulations. The results of this study show that the type of automation implemented within an environment has a considerable impact on the operator, in terms of performance as well as cognitive/emotional stat