Detection Of Emotional States Research Articles

Emotional State Classification in Patient–Robot Interaction Using Wavelet Analysis and Statistics-Based Feature Selection

Due to a major shortage of nurses in the U.S., future healthcare service robots are expected to be used in tasks involving direct interaction with patients. Consequently, there is a need to design nursing robots with the capability to detect and respond to patient emotional states and to facilitate positive experiences in healthcare. The objective of this study was to develop a new computational algorithm for accurate patient emotional state classification in interaction with nursing robots during medical service. A simulated medicine delivery experiment was conducted at two nursing homes using a robot with different human-like features. Physiological signals, including heart rate (HR) and galvanic skin response (GSR), as well as subjective ratings of valence (happy-unhappy) and arousal (excited-bored) were collected on elderly residents. A three-stage emotional state classification algorithm was applied to these data, including: (1) physiological feature extraction; (2) statistical-based feature selection; and (3) a machine-learning model of emotional states. A pre-processed HR signal was used. GSR signals were nonstationary and noisy and were further processed using wavelet analysis. A set of wavelet coefficients, representing GSR features, was used as a basis for current emotional state classification. Arousal and valence were significantly explained by statistical features of the HR signal and GSR wavelet features. Wavelet-based de-noising of GSR signals led to an increase in the percentage of correct classifications of emotional states and clearer relationships among the physiological response and arousal and valence. The new algorithm may serve as an effective method for future service robot real-time detection of patient emotional states and behavior adaptation to promote positive healthcare experiences.

Multi-Agent Based E-Social Care Support System for Inhabitancies of a Smart Eco-Social Apartment

The paper presents further development steps of recently proposed model of an adaptive multi-agent-based e-social care system by introducing in it an intelligent power controller to supporting people in a Smart Ecological and Social Apartment, the SESA. The schematic diagrams of the controller are proposed for adaptive control of motors and LED lights as well as for measurements of EDA, HR, and FT signals of inhabitancies in the SESA. The proposed in this paper multi-agent system consists of two groups of agents, first, the agents for human’s physiological data collection, analysis and emotional state detection, and, second, the agents for an intelligent power control which takes environmental and hardware parameters and, with the use of embedded agents, provides adaptive control of LED lights, temperature as well as of power consuming by motors used in the SESA in accordance with the human state defined by the first group of agents.

Dynamic Brain Activation during Processing of Emotional Intonation: Influence of Acoustic Parameters, Emotional Valence, and Sex

Appreciation of the emotional tone of verbal utterances represents an important aspect of social life. It is still unsettled, however, which brain areas mediate processing of intonational information and whether the presumed right-sided superiority depends upon acoustic properties of the speech signal. Functional magnetic resonance imaging was used to disentangle brain activation associated with (i) extraction of specific acoustic cues and (ii) detection of specific emotional states. Stimulus material comprised pairs of emotionally intonated utterances, exclusively differing either in pitch range or in the length of stressed vowels. Hemodynamic responses showed a dynamic pattern of cerebral activation including sequenced bilateral responses of various cortical and subcortical structures. Activation associated with discrimination of emotional expressiveness predominantly emerged within the right inferior parietal lobule, within the bilateral mesiofrontal cortex and—with an asymmetry toward the right hemisphere—at the level of bilateral dorsolateral frontal cortex. Lateralization did not depend upon acoustic structure or emotional valence of stimuli. These findings might prove helpful in reconciling the controversial previous clinical and experimental data.

Are emotions contagious? Evoked emotions while viewing emotionally expressive faces: quality, quantity, time course and gender differences

In human interactions, frequently one individual becomes ‘infected’ with emotions displayed by his or her partner. We tested the predictions by Hatfield et al. (1992) (Primitive emotional contagion. Review of Personal and Social Psychology 14, 151–177) that the automatic, mostly unconscious component of this process, called ‘primitive emotional contagion’, is repeatable and fast, that stronger facial expressions of the sender evoke stronger emotions in the viewer and that women are more susceptible to emotional contagion than men. We presented photos from the Pictures of Facial Affect (Ekman and Friesen, 1976). ( Pictures of Facial Affect. Consulting Psychologists Press, Palo Alto) on a PC varying the affective content (happy and sad), the expressive strength and the duration of presentation. After each photo, subjects rated the strength of experienced happiness, sadness, anger, disgust, surprise, fear and pleasure. Feelings of happiness or sadness were significantly, specifically and repeatedly evoked in the viewer — even with presentations lasting only 500 ms. Stronger expressions evoked more emotion. The gender of the viewer had weak effects. We hypothesize that this fast and repeatable reaction is likely to have a ‘prewired’ neural basis. We propose that the induction of emotional processes within a subject by the perception of emotionally expressive faces is a powerful instrument in the detection of emotional states in others and as the basis for one's own reactions. Detailed knowledge of emotional reactions to faces is also valuable as a basis for psychiatric studies of disorders in affect and/or communication and in studies using functional imaging (fMRI or PET) where faces are increasingly used as stimuli.