An Affect-Based Built Environment Video Analytics

Kaklauskas, Artūras; Zavadskas, Edmundas Kazimieras; Bardauskienė, Dalia; Cerkauskas, Justas; Ubarte, Ieva; Seniut, Mark; Dzemyda, Gintautas; Kaklauskaite, M.; Vinogradova, Irina; Velykorusova, Anastasiia

doi:10.1016/j.autcon.2019.102888

Cited by 17 publications

(12 citation statements)

References 30 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: 62%

“…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: 62%

“…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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“…The supplier's performance, market, hedonic, and customer-perceived values are directly proportional to a system of adequate indicators and the weights and values of these indicators. Therefore, the results of this research, neuro decision matrices [91,92], and MAMVA methods [89,90] can be used as a basis to determine the market, hedonic, and customer-perceived values of suppliers.…”

Section: Discussionmentioning

confidence: 99%

A New Decision-Making Approach Based on Fermatean Fuzzy Sets and WASPAS for Green Construction Supplier Evaluation

et al. 2020

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The construction industry is an important industry because of its effects on different aspects of human life experiences and circumstances. Environmental concerns have been considered in designing and planning processes of construction supply chains in the recent past. One of the most crucial problems in managing supply chains is the process of evaluation and selection of green suppliers. This process can be categorized as a multi-criteria decision-making (MCDM) problem. The aim of this study is to propose a novel and efficient methodology for evaluation of green construction suppliers with uncertain information. The framework of the proposed methodology is based on weighted aggregated sum product assessment (WASPAS) and the simple multi-attribute rating technique (SMART), and Fermatean fuzzy sets (FFSs) are used to deal with uncertainty of information. The methodology was applied to a green supplier evaluation and selection in the construction industry. Fifteen suppliers were chosen to be evaluated with respect to seven criteria including “estimated cost”, “delivery efficiency”, “product flexibility”, “reputation and management level”, “eco-design”, and “green image pollution”. Sensitivity and comparative analyses were also conducted to assess the efficiency and validity of the proposed methodology. The analyses showed that the results of the proposed methodology were stable and also congruent with those of some existing methods.

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“…These works in 10 – 13 have shown that the use of wearable vital sensor was effective for estimating emotions. Also Kaklauskas et al developed biometrical, affective, emotional and the surrounding environment maps in shopping by using remote biometrics analysis devices, such as vital sensors and web cameras 14 – 16 . However, in the emotion analysis method 10 – 13 , it is necessary for persons to always wear the measurement device, which may limit human behaviors.…”

mentioning

confidence: 99%

Predicting individual emotion from perception-based non-contact sensor big data

Komuro

Hashiguchi

Hirai

et al. 2021

Sci Rep

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This study proposes a system for estimating individual emotions based on collected indoor environment data for human participants. At the first step, we develop wireless sensor nodes, which collect indoor environment data regarding human perception, for monitoring working environments. The developed system collects indoor environment data obtained from the developed sensor nodes and the emotions data obtained from pulse and skin temperatures as big data. Then, the proposed system estimates individual emotions from collected indoor environment data. This study also investigates whether sensory data are effective for estimating individual emotions. Indoor environmental data obtained by developed sensors and emotions data obtained from vital data were logged over a period of 60 days. Emotions were estimated from indoor environmental data by machine learning method. The experimental results show that the proposed system achieves about 80% or more estimation correspondence by using multiple types of sensors, thereby demonstrating the effectiveness of the proposed system. Our obtained result that emotions can be determined with high accuracy from environmental data is a useful finding for future research approaches.

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An Affect-Based Built Environment Video Analytics

Cited by 17 publications

References 30 publications

Human Emotion Recognition: Review of Sensors and Methods

Human Emotion Recognition: Review of Sensors and Methods

A New Decision-Making Approach Based on Fermatean Fuzzy Sets and WASPAS for Green Construction Supplier Evaluation

Predicting individual emotion from perception-based non-contact sensor big data

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