Emotional flow monitoring for health using FLOWSENSE: An experimental study to test the impact of antismoking campaigns

Abstract

A multi-sensory system, entitled FLOWSENSE, was developed to monitor emotional responses in real time using both subjective (self-report) and objective (physiological) measures. To evaluate the program's reliability, an experiment was conducted using antismoking campaigns. Participants (N=92) were exposed to three advertisements of humor or of fear-appeals and asked to report continuously the emotions and the intensity they were feeling. Physiological responses were also collected by the system. Results showed statistically robust differences of emotions reported between the two different types of ads. Those exposed to the fear appeals ads reported significantly more often, with higher intensity and for longer periods of time fear and sadness, while the humor ads induced stronger positive emotions of happiness. The system was also able to localize fine grains of information which can help researchers understand and identify how health campaigns are influencing emotions and how this information may contribute to wider health outcomes.

The influence of valence shifts in fear appeals on message processing and behavioral intentions: A moderated mediation model

“…However, efficacy cues subsequently presented are likely to evoke other, more positive emotional states. Albeit theoretically sound, empirical research has thus far overlooked the consequences of such shifts in emotional responses during persuasive attempts (for exceptions, see [ 19 , 38 ]).…”

“…In sum, messages that succeed in effecting change by first promoting a threat and by later fostering efficacy perceptions and positive emotional experiences are presumably effective in supporting important determinants of adaptive health-relevant behaviors. However, research on persuasive (fear) appeals in health communication has rarely addressed the evolving nature of emotional experiences during exposure to messages as a crucial factor influencing the processing and outcomes of such messages (for exceptions, see [ 2 , 14 , 19 , 38 ]). Therefore, the purposes of our study were to induce emotional flows, measure the valence shifts experienced, and examine their effects on central determinants of health-relevant information processing and behavior.…”

Multimodal Approach for Emotion Recognition Based on Simulated Flight Experiments

“…Several methods and techniques can be applied to perform emotion recognition through the use of a couple of hardware devices and software such as: analysis of emotional properties based on two physiological data such as, ECG and EEG [3]; unified system for efficient discrimination of positive and negative emotions based on EEG data [4]; automatic recognizer of the facial expression around the eyes and forehead based on Electrooculography (EOG) data giving support to emotion recognition task [5]; use of GSR and ECG data to develop a study to examine the effectiveness of Matching Pursuit (MP) algorithm in emotion recognition, using mainly PCA to reduce the features dimensionality and Probabilistic Neural Network (PNN) as the recognition technique [6]; emotion recognition system based on physiological data using ECG and respiration (RSP) data, recorded simultaneously by a physiological monitoring device based on wearable sensors [7]; emotions recognition using EEG data and also performed an analyze about the impact of positive and negative emotions using SVM and RBF as the recognition methods [8]; new approach to emotion recognition based on EEG and classification method using Artificial Neural Networks (ANN) with features analysis based on Kernel Density Estimation (KDE) [9]; an application that stores several psychophysiological data based on HR, ECG, SpO2 and GSR, that were acquired while the users watched advertisements about smoking campaigns [10]; experiments based on flight simulator to developed a multimodal sensing architecture to recognize emotions using three different techniques for biosignal acquisitions [11]; multimodal sensing system to identify emotions using different acquisition techniques, based on photo presentation methodology [12]; real-time user interface with emotion recognition that depends on the need for skill development to support a change in the interface paradigm to one that is more human centered [13]; recognize emotions through psychophysiological sensing using a multiple-fusion-layer based on ensemble classifier of stacked auto encoder (MESAE) [14].…”