Affective analytics of demonstration sites

Kaklauskas, Artūras; Jokubauskas, D.; Cerkauskas, Justas; Dzemyda, Gintautas; Ubarte, Ieva; Skirmantas, Darius; Podviezko, Askoldas; Simkute, I.

doi:10.1016/j.engappai.2019.03.001

Cited by 19 publications

(10 citation statements)

References 93 publications

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“…Consequently, this research represents an attempt at introducing the idea of humanizing the IoT and affective computing systems by applying human emotion recognition sensors and methods to academic and business communities. The confirmation of such is by the IoT and affective computing systems, which were developed by the authors of this research [25][26][27][28].…”

Section: Discussionmentioning

confidence: 61%

“…Beta (β) (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32) Beware, Concentration.…”

Section: Strong Sense Of Empathy and Intuitionmentioning

confidence: 99%

“…This review focuses on the hardware and methods used for automated emotion recognition, which are applicable for machine learning procedures using obtained experimental data analysis and automated solutions based on the results of these analyses. This study also analyzes the idea of humanizing the Internet of Things and affective computing systems, which has been validated by systems developed by the authors of this research [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

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Human Emotion Recognition: Review of Sensors and Methods

Dzedzickis

Kaklauskas

Bučinskas

2020

Sensors

408

170

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Automated emotion recognition (AEE) is an important issue in various fields of activities which use human emotional reactions as a signal for marketing, technical equipment, or human–robot interaction. This paper analyzes scientific research and technical papers for sensor use analysis, among various methods implemented or researched. This paper covers a few classes of sensors, using contactless methods as well as contact and skin-penetrating electrodes for human emotion detection and the measurement of their intensity. The results of the analysis performed in this paper present applicable methods for each type of emotion and their intensity and propose their classification. The classification of emotion sensors is presented to reveal area of application and expected outcomes from each method, as well as their limitations. This paper should be relevant for researchers using human emotion evaluation and analysis, when there is a need to choose a proper method for their purposes or to find alternative decisions. Based on the analyzed human emotion recognition sensors and methods, we developed some practical applications for humanizing the Internet of Things (IoT) and affective computing systems.

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Section: Discussionmentioning

confidence: 61%

“…Beta (β) (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32) Beware, Concentration.…”

Section: Strong Sense Of Empathy and Intuitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Human Emotion Recognition: Review of Sensors and Methods

Dzedzickis

Kaklauskas

Bučinskas

2020

Sensors

408

170

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“…An integration of Damasio's Somatic Marker Hypothesis [16], biometric, and physiological methods [32], categorical data using neuromarketing analysis methods by means of a spatial multiple criteria analysis (e.g., evoking human affective attitudes and compiling maps of emotional and physiological states [33]), developing neuro-advertisement variant combinations by applying a combinatorial method, employing a statistical analysis (LOGIT, KNN, MBP, and RBP) and incorporating multiple criteria analysis methods [32] comprises the VINERS Method.…”

Section: Discussionmentioning

confidence: 99%

“…The VINERS Method is an integrated composite consisting of the Somatic Marker Hypothesis by Damasio [16], biometric and physiological, methods [32]. This method offers a spatial multiple criteria analysis of categorical data using neuromarketing analytical methods, such as generating human affective attitudes maps along with maps of emotional and physiological states [33], applying a combinatorial method to develop various neuro-advertisement combinations, and engaging in statistical analysis (LOGIT, KNN, MBP, RBP) and multiple criteria analysis methods [32]. The stages of the multisensory, green, and energy efficient housing neuromarketing method appear below.…”

Section: A Multisensory Green and Energy Efficient Housing Neuromarmentioning

confidence: 99%

A Multisensory, Green, and Energy Efficient Housing Neuromarketing Method

et al. 2019

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Green products, clean energy, energy union, green buildings, eco-innovations, environment-related, and similar initiatives and policies have become very popular and widely applied all over the world. A pleasant built environment (parks, flowerbeds, beautiful buildings) and a repulsive environment (noise, polluted surroundings) influence a buyer’s outlook on an advertisement differently. An aesthetic, comfortable, and clean built environment evokes positive emotional states, not only at the time of housing selection and purchase but during the building’s life cycle as well. Potential housing buyers always feel comfortable in certain built environments, and they are inclined to spend more time there. The issues needing answers are how to measure the segmentation/physiological indicators (crowd composition by gender and age groups), as well as the emotional (happy, sad, angry, valence) and physiological (heart rate) states of potential homebuyers realistically, to produce an integrated evaluation of such data and offer buyers rational, green, and energy efficient housing alternatives. To achieve this purpose, the Multisensory, green and energy efficient housing neuromarketing method was developed to generate the necessary conditions. Here, around 200 million multisensory data recordings (emotional and physiological states) were accumulated, and the environmental air pollution (CO, NO2, PM10, volatile organic compounds) and noise pollution were investigated. Specific green and energy efficient building case studies appear in this article to demonstrate the developed method clearly. The obtained research results are in line with those from previous and current studies, which state that the interrelation of environmental responsiveness and age forms an inverse U and that an interest in green and energy efficient housing depends on age.

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