Actual Emotion and False Emotion Classification by Physiological Signal

Kim, Chi Jung; Chang, Min-Hyuk

doi:10.1109/sip.2015.17

Cited by 5 publications

(2 citation statements)

References 1 publication

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“…The model proposed in [7,8] discussed the design specifications and software implementation of a physiological signal-based user-independent emotion recognition system. This system has various utilities over medical and smart home issues.…”

Section: Related Workmentioning

confidence: 99%

Classification of Physiological Signals for Emotion Recognition using IoT

Tiwari¹,

Agarwal²,

Syafrullah³

et al. 2019

EECSI

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Emotion recognition gains huge popularity now a days. Physiological signals provides an appropriate way to detect human emotion with the help of IoT. In this paper, a novel system is proposed which is capable of determining the emotional status using physiological parameters, including design specification and software implementation of the system. This system may have a vivid use in medicine (especially for emotionally challenged people), smart home etc. Various Physiological parameters to be measured includes, heart rate (HR), galvanic skin response (GSR), skin temperature etc. To construct the proposed system the measured physiological parameters were feed to the neural networks which further classify the data in various emotional states, mainly in anger, happy, sad, joy. This work recognized the correlation between human emotions and change in physiological parameters with respect to their emotion.

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Section: Related Workmentioning

confidence: 99%

Classification of Physiological Signals for Emotion Recognition using IoT

Tiwari¹,

Agarwal²,

Syafrullah³

et al. 2019

EECSI

View full text Add to dashboard Cite

show abstract

“…For a complete diagnosis of UX with the user's thoughts patterns and believes, it is preferable to combine the objective data with subjective data using suitable tools such as a questionnaire and "UX heat maps" (Georges et al, 2016). However, objective methods used today to assess user experience are questionable due to signal contamination: motor interference in Brain Computer Interaction (BCI) and Electromyogram (EMG) (Mc-Farland et al, 2005), signal distinction of two close emotions such as stress or excitement in Electrocardiogram (ECG) or Electroencephalogram (EEG) (Chai et al, 2014;Kim and Chang, 2015), late response latency in Galvanic Skin Response (GSR) and Skin Temperature (SKT) (Kim and Chang, 2015).…”

Section: User Experience Questionnaires Reviewmentioning

confidence: 99%

A methodology for the design of pedagogically adaptable learning environments.

Barange

Blandin

Querrec

2016

IJVR

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In the last decades, the industry has profoundly integrated the use of digital resources in their production process. However, these assets are rarely re-used for the training of the users, operators and technicians that have to interact with these objects. Furthermore, although training and learning environments are classical applications of virtual reality, the design of these environments is generally ad hoc, i.e. dedicated to specific operations on specific objects, hence requiring the intervention of programmers whenever a modification of the pedagogical scenario is required. In this article, we propose a methodology to design adaptable virtual environments, by separating the role of the different protagonists that play a part in the creation of learning environments. In particular, its goal is to allow the teachers to implement different scenarios according to the level of the trainees and to the pedagogical objectives without the intervention of computer scientists. An example of adaptable wind turbine environment is shown, with three different learning situations: simulator, safety training and preventive maintenance training.

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Identifying Anger Veracity Using Neural Network and Long-Short Term Memory with Bimodal Distribution Removal

Jin

Zhu

2020

Communications in Computer and Information Science

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Actual Emotion and False Emotion Classification by Physiological Signal

Cited by 5 publications

References 1 publication

Classification of Physiological Signals for Emotion Recognition using IoT

Classification of Physiological Signals for Emotion Recognition using IoT

A methodology for the design of pedagogically adaptable learning environments.

Identifying Anger Veracity Using Neural Network and Long-Short Term Memory with Bimodal Distribution Removal

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