(Un-)Biasing the Morphologies of Affect for HRI Purposes

One fundamental aspect of Human-Robot Interactions is the role of the morphologies of both humans and machines. Basically, humans are naturalistically oriented towards the social interaction with other humans. Taking into account that fact that human morphologies run a social role, and that affection or emotion are fundamental aspects of the eco-cognitive and social processes, this talk tries to relate some classic and current challenges related to HRI: moral bias, emotional role into HRI, and dynamical morphologies in transhumanist scenarios

Building artificial personalities: expressive communication channels based on an interlingua for a human-robot dance

The development of artificial personalities requires that we develop a further understanding of how personality is communicated. This can be done through developing humanrobot interaction (HRI). In this paper we report on the development of the SpiderCrab robot. This uses an interlingua based on Laban Movement Analysis (LMA) to intermediate a human-robot dance. Specifically, we developed measurements to analyse data in real time from a simple vision system and implemented a simple stochastic dancing algorithm on a custom built robot. This shows how, through some simple rules, a personality can emerge by biasing random behaviour. The system was tested with professional dancers and members of the public and the results (formal and anecdotal) are presented herein

Integration of Wavelet and Recurrence Quantification Analysis in Emotion Recognition of Bilinguals

Background: This study offers a robust framework for the classification of autonomic signals into five affective states during the picture viewing. To this end, the following emotion categories studied: five classes of the arousal-valence plane (5C), three classes of arousal (3A), and three categories of valence (3V). For the first time, the linguality information also incorporated into the recognition procedure. Precisely, the main objective of this paper was to present a fundamental approach for evaluating and classifying the emotions of monolingual and bilingual college students.Methods: Utilizing the nonlinear dynamics, the recurrence quantification measures of the wavelet coefficients extracted. To optimize the feature space, different feature selection approaches, including generalized discriminant analysis (GDA), principal component analysis (PCA), kernel PCA, and linear discriminant analysis (LDA), were examined. Finally, considering linguality information, the classification was performed using a probabilistic neural network (PNN).Results: Using LDA and the PNN, the highest recognition rates of 95.51%, 95.7%, and 95.98% were attained for the 5C, 3A, and 3V, respectively. Considering the linguality information, a further improvement of the classification rates accomplished.Conclusion: The proposed methodology can provide a valuable tool for discriminating affective states in practical applications within the area of human-computer interfaces

Detection and Analysis of Tremor Using a System Based on Smart Device and NoSQL Database

Tremor is the most common symptoms of Parkinson?s Disease (PD) and Essential Tremor (ET). Its detection and analysis during daily living plays a crucial role in the treatment of PD and ET patients. It is typically assessed in the clinic with certain tremor rating scales, which are qualitative, subjectdependent and do not necessarily reflect the real situation of the patient. In this paper, a system composed of a smartwatch, a smartphone and a NoSQL database sever is used to monitor the movements of the patients. A novel data analysis method is proposed to detect tremor and identify the connected actions. Tremor can be detected on the basis of the movement frequency difference and voluntary actions can also be recognized based on the rich information from the collected data. It helps clinicians to analyze the relationship between the tremor and a certain action. A series of simulated experiments are conducted to demonstrate the feasibility of the proposed system and data analysis method. The result shows that tremor happened during different situations can be detected with an adequate accuracy with the data collected by the proposed system. The actions around the tremor can also be identified

Mindfulness Enhancement Model Using Feedback Online Diary to Observe Oneself

In everyday life, facing too many problems has to draw us from being perfect human. So, awareness is necessary to protect one’s life to have a balance livelihood. Mindfulness is working with everything in every moment happening in our life. Being conscious of our thoughts to refrain from bad deeds while holding on to virtues with attentive consciousness of good or bad consequences is regarded as a way of mindfulness training which can be practiced in daily life. This paper would like to propose a mindfulness enhancement model using concrete tool with education theory of Cognitive Constructionism and Learning Diary on Artificial Intelligent (Expert System) infiltrating the feedback of Dhamma, morality or remarks to individual to practice and observe oneself. Keywords: Online diary, Expert system, Mindfulness, Innovation, Self realizatio

A Database of Full Body Virtual Interactions Annotated with Expressivity Scores

Abstract Recent technologies enable the exploitation of full body expressions in applications such as interactive arts but are still limited in terms of dyadic subtle interaction patterns. Our project aims at full body expressive interactions between a user and an autonomous virtual agent. The currently available databases do not contain full body expressivity and interaction patterns via avatars. In this paper, we describe a protocol defined to collect a database to study expressive full-body dyadic interactions. We detail the coding scheme for manually annotating the collected videos. Reliability measures for global annotations of expressivity and interaction are also provided