Non-invasive, non-contact based affective state identification

This paper discusses a study on detecting affective states of human subjects from their body’s electromagnetic (EM) wave. In particular, the affective states under investigation are happy, nervous, and sad which play important roles in Human-Robot Interaction (HRI)applications. A structured experimental setup was designed to invoke the desired affective states. These states are induced by exposing the subject to a specific set of audiovisual stimulations upon which the EM waves are captured from ten different regions of the subject’s body by using a handheld device called Resonant Field Imaging (RFITM). Nine subjects are randomly chosen and the collected data are then preprocessed and trained by Bayesian Network (BN) to map the EM wave to the corresponding affective states. Preliminary results demonstrate the ability of the BN to predict human affective state with 80.6% precision, and 90% accuracy

Electromagnetic based emotion recognition using ANOVA feature selection and bayes network

The paper discusses the development of emotion recognition system which can be applied to a wider range of human population. This is achieved by measuring the unique electromagnetic (EM) signal generated upon invoking certain emotions. A set of audio-visual stimulants is designed to invoke the desired emotions under study that are happy, sad and nervous. A set of questionnaire is developed to verify the stimulant effectiveness in invoking the emotion. The recognition of the emotion is deduced from the measured electromagnetic signals radiated from the human body by a handheld device called Resonant Field Imaging (RFITM). There are ten points of interest (POIs) on the body where the signals are measured to form the dataset which later fed into Bayes Network (BN) to classify the emotion. ANOVA test is run in selecting the best features to classify the emotions. The result after eliminating 6 from 10 POIs demonstrates the system performance is not compromised. The efficiency of ANOVA and BN in selecting the best features to model the emotion recognition system has successfully optimized the cost of the system and reduced the time to measure the signals quite significantly

In-the-loop emotion recognition system for Human Machine Interaction (HMI)

In the 21st century, there will be more machines developed either to complement or totally replace the jobs previously done by human since the machine can operate with high precision and accuracy. However many machines do not have ways to incorporate and respond to the emotion of the user. The efficacy of the system is further reduced if the machine has to take commands from human in order to operate. Thus, it is essential for a machine to understand the users’ feeling and react accordingly especially for Human Machine Interaction (HMI) applications. The existing emotion recognition systems have two major weaknesses which are the identification is limited to certain group of people and the hassle to wear the sensor by the user. Thus, an emotion recognition system is developed based on the machine learning technique that can be used throughout a wider range of human population as well as it is hassle free as there will not be any sensors attached to the body of the human subject. The audio-visual stimulants are used to invoke the desired emotions which are happy, sad and nervous taken from video-sharing website which consists of a ten minutes video for each session. After each session, the subject is given a set of questionnaire with Likert Scale of 4 to evaluate the audio-visual stimuli effectiveness to invoke the required emotion. The identification of the emotion is deduced from the measured electromagnetic (EM) signals radiated from the human body by a handheld device called Resonant Field Imaging (RFITM). From the dataset obtained consisting ten points of interests (POIs) on the human body, the signals are fed into Bayesian Network (BN) to classify the emotion under study. The classification of the EM dataset results accord 86% precision and 90.7% accuracy by using BN. A dedicated Graphical User Interface (GUI) is developed to display the corresponding classified emotion and as a medium to pass the coded emotion to the hybrid automata system. The hybrid automata system is selected as a framework to embody emotion in controlling the rehabilitation robot platform. The results obtained from the dataset show promising trends where the emotion recognition system is able to classify the type of emotions with high accuracy and the hybrid automata system is feasible in controlling the movement of the rehabilitation platforms’ end effector through a series of offline and online experiments. The limitation of the developed system is there are only three emotions under study due to the natural emotion of the post stroke patient who is undergoing rehabilitation therap

The influence of social cues in persuasive social robots on psychological reactance and compliance

People can react negatively to persuasive attempts experiencing reactance, which gives rise to negative feelings and thoughts and may reduce compliance. This research examines social responses towards persuasive social agents. We present a laboratory experiment which assessed reactance and compliance to persuasive attempts delivered by an artificial (non-robotic) social agent, a social robot with minimal social cues (human-like face with speech output and blinking eyes), and a social robot with enhanced social cues (human-like face with head movement, facial expression, affective intonation of speech output). Our results suggest that a social robot presenting more social cues will cause higher reactance and this effect is stronger when the user feels involved in the task at hand

Assessing the effect of persuasive robots interactive social cues on users’ psychological reactance, liking, trusting beliefs and compliance

Research in the field of social robotics suggests that enhancing social cues in robots can elicit more social responses in users. It is however not clear how users respond socially to persuasive social robots and whether such reactions will be more pronounced when the robots feature more interactive social cues. In the current research, we examine social responses towards persuasive attempts provided by a robot featuring different numbers of interactive social cues. A laboratory experiment assessed participants’ psychological reactance, liking, trusting beliefs and compliance toward a persuasive robot that either presented users with: no interactive social cues (random head movements and random social praises), low number of interactive social cues (head mimicry), or high number of interactive social cues (head mimicry and proper timing for social praise). Results show that a persuasive robot with the highest number of interactive social cues invoked lower reactance and was liked more than the robots in the other two conditions. Furthermore, results suggest that trusting beliefs towards persuasive robots can be enhanced by utilizing praise as presented by social robots in no interactive social cues and high number of interactive social cues conditions. However, interactive social cues did not contribute to higher compliance

Assessing the effect of persuasive robots interactive social cues on users’ psychological reactance, liking, trusting beliefs and compliance

Research in the field of social robotics suggests that enhancing social cues in robots can elicit more social responses in users. It is however not clear how users respond socially to persuasive social robots and whether such reactions will be more pronounced when the robots feature more interactive social cues. In the current research, we examine social responses towards persuasive attempts provided by a robot featuring different numbers of interactive social cues. A laboratory experiment assessed participants’ psychological reactance, liking, trusting beliefs and compliance toward a persuasive robot that either presented users with: no interactive social cues (random head movements and random social praises), low number of interactive social cues (head mimicry), or high number of interactive social cues (head mimicry and proper timing for social praise). Results show that a persuasive robot with the highest number of interactive social cues invoked lower reactance and was liked more than the robots in the other two conditions. Furthermore, results suggest that trusting beliefs towards persuasive robots can be enhanced by utilizing praise as presented by social robots in no interactive social cues and high number of interactive social cues conditions. However, interactive social cues did not contribute to higher compliance

Investigating the effect of social cues on social agency judgement

To advance the research area of social robotics, it is important to understand the effect of different social cues on the perceived social agency to a robot. This paper evaluates three sets of verbal and nonverbal social cues (emotional intonation voice, facial expression and head movement) demonstrated by a social agent delivering several messages. A convenience sample of 18 participants interacted with SociBot, a robot that can demonstrate such cues, experienced in sequence seven sets of combinations of social cues. After each interaction, participants rated the robot's social agency (assessing its resemblance to a real person, and the extent to which they judged it to be like a living creature). As expected, adding social cues led to higher social agency judgments; especially facial expression was connected to higher social agency judgments

Technology-assisted emotion recognition for autism spectrum disorder (ASD) children: a systematic literature review

The information about affective states in individuals with autism spectrum disorder (ASD) is difficult to obtain as they usually suffer from deficits in facial expression. Affective state conditions of individuals with ASD were associated with impaired regulation of speech, communication, and social skills leading towards poor socio-emotion interaction. It is conceivable that the advance of technology could offer a psychophysiological alternative modality, particularly useful in persons who cannot verbally communicate their emotions as affective states such as individuals with ASD. The study is focusing on the investigation of technology-assisted approach and its relationship to affective states recognition. A systematic review was executed to summarize relevant research that involved technology-assisted implementation to identify the affective states of individuals with ASD using Preferred Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA) approach. The output from the online search process obtained from six publication databases on relevant studies published up to 31 July 2020 was analyzed. Out of 391 publications retrieved, 20 papers met the inclusion and exclusion criteria set in prior. Data were synthesized narratively despite methodological and heterogeneity variations. In this review, some research methods, systems, equipment and models to address all the related issues to the technology-assisted and affective states concerned were presented. As for the consequence, it can be assumed that the emotion recognition with assisted by technology, for evaluating and classifying affective states could help to improve efficacy in therapy sessions between therapists and individuals with ASD. This review will serve as a concise reference for providing general overviews of the current state-of-the-art studies in this area for practitioners, as well as for experienced researchers who are searching for a new direction for future works