1,721,009 research outputs found
Enhancing Affective Robotics via Human Internal State Monitoring
No full textDuring the last years, many solutions have been proposed to achieve a natural Human-Robot Interaction (HRI) and Communication paving the way to new paradigms of under-standing and adaptation based on mutual affective perception. Especially in human-robot social interaction, it is helpful not only that people can understand the robot's behavioral state, but also robots possess the ability to detect, interpret and adaptively react to human affective responses. Typical approaches are able to assess humans' affective responses from the observation of overt behavior. However, there are cases in which the overt observable behaviors could not match with the internal states (e.g., people with diseases compromising normal emotional responses). In such cases, having an objective measure of the users' state from 'inside' is of paramount importance. This work presents an affect detection model able to provide a measure of the human affective state, with particular focus on the stress state, from the analysis of EEG users' activity during the interaction with a social humanoid robot endowed with diverse affective elicitation behaviors. We argue that monitoring the stress state of a human during HRI is necessary to adapt the robot behavior in a way to avoid possible counterproductive effects of its use
Can a robot elicit emotions? A Global Optimization Model to attribute mental states to human users in HRI∗
No full textIn this work, we are interested in investigating if a distinct personality of the robot may impact the emotional state of the users, which we propose to detect using neuroscience theories that allow us to classify emotions based on valence and arousal metrics derived from brain wave activity analysis. We devised an experimental research study in which EEG data was gathered while individuals interacted with a robot with different personalities. Support Vector Machine, Decision Tree, Random Forest, K-Nearest Neighbors, and Multi-Layer Perceptrons have all been trained using EEG-signal, valence, and arousal data. All proposed classifiers were subjected to a Global optimization Model (GOM) that used feature selection and hyper-parameter optimization techniques to improve classification results and address common issues that affect classifier accuracy when attempting to solve a supervised learning problem, such as bias-variance trade-off, dimensionality of the input space, and noise in the input data space. The findings of the experiments will be presented and debated
Enhancing competitiveness and innovation in the universities through digital technologies. The case study of the University of Naples "Parthenope
No full textCorrection to: Socially Assistive Robot for Providing Recommendations: Comparing a Humanoid Robot with a Mobile Application (International Journal of Social Robotics, (2018), 10, 2, (265-278), 10.1007/s12369-018-0469-4)
No full textIn the original publication, authors Silvia Rossi and Anna Tamburro’s affiliations have been incorrectly reversed and should to be read correctly as “Department of Electrical Engineering and Information Technology”
Working together: a DBN approach for individual and group activity recognition
No full textHuman activity recognition is gaining more and more the attention of researchers due to its applicability in many different fields such as health monitoring, smart environments, etc. Activity recognition solutions typically focus on the classification of single-user behavior. However, in a living or working environment, there are usually multiple inhabitants acting together, hence it makes sense to interpret the activities by considering the aggregated information from different subjects. In this paper, we address the problem of group activity recognition (GAR) in a hierarchical way by first examining individual person’s actions, reconstructed by correlating data coming from body-worn and external positioning sensors. We then aggregate this information by considering each individual as an input of a hierarchical deep belief network (DBN). This aims to extract common temporal/spatial dynamics at the level of group activity. We evaluated the proposed approach in a laboratory environment, where the participants labeled their daily activities using an app on a mobile phone. Collected data contributed to the creation of two datasets respectively containing labeled single and group activities. The experimental results evaluated on these datasets and on a public one demonstrated the effectiveness of the proposed model with respect to a support vector machine (SVM) baseline
Enhancing Affective Robotics via Human Internal State Monitoring
No full textDuring the last years, many solutions have been proposed to achieve a natural Human-Robot Interaction (HRI) and Communication paving the way to new paradigms of understanding and adaptation based on mutual affective perception. Especially in human-robot social interaction, it is helpful not only that people can understand the robot's behavioral state, but also robots possess the ability to detect, interpret and adaptively react to human affective responses. Typical approaches are able to assess humans' affective responses from the observation of overt behavior. However, there are cases in which the overt observable behaviors could not match with the internal states (e.g., people with diseases compromising normal emotional responses). In such cases, having an objective measure of the users' state from `inside' is of paramount importance. This work presents an affect detection model able to provide a measure of the human affective state, with particular focus on the stress state, from the analysis of EEG users' activity during the interaction with a social humanoid robot endowed with diverse affective elicitation behaviors. We argue that monitoring the stress state of a human during HRI is necessary to adapt the robot behavior in a way to avoid possible counterproductive effects of its use
The SPECTRA Project: Biomedical Data for Supporting the Detection of Treatment Resistant Schizophrenia
No full textThe SPECTRA project aims at supporting the clinician in the detection of patients suffering from a specific subclass of Schizophrenia (SZ), classified as Treatment Resistant Schizophrenia (TRS) patients. This kind of SZ patients are difficult to diagnose and have enormous difficulty. Early diagnosis may improve their quality of life. In this paper, we describe the results of our study on the identification of the typology of biomedical data that should be considered for training machine learning algorithms for the classification of TRS/nonTRS patients suffering from schizophrenia
Emphasizing with a Robot with a Personality
No full textIn recent decades, socially assisted robots (SAR) have found applications in various operational contexts where the elicitation of empathy is crucial for facilitating human-robot interaction, as it plays a pivotal role in building trust and rapport. However, a significant challenge lies in the complexity of empathy, as there is no universally applicable method for its elicitation. Different individuals express and experience empathy in diverse ways. This study delves into the factors that impact the level of empathy evoked by a social robot, measured through user perceptions gauged by standard questionnaires. An empirical mixed-design study involving 28 participants was conducted, utilizing a Furhat robot with either an ironic (empathetic) or apathetic (non-empathetic) identity during verbal interactions with users. The primary objective of this research is to explore the user perception of a robot with or without a specific personality, with a particular focus on the empathetic personality, known to enhance Theory of Mind (ToM). The investigation aims to discern the extent to which users’ perceptions are influenced by the personality of the robot and whether this influence varies based on the personalities of the users themselves
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