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Review
. 2020 Jan 21;20(3):592.
doi: 10.3390/s20030592.

Human Emotion Recognition: Review of Sensors and Methods

Andrius Dzedzickis  1 , Artūras Kaklauskas  2 , Vytautas Bucinskas  1
Affiliations
Review

Human Emotion Recognition: Review of Sensors and Methods

Andrius Dzedzickis et al. Sensors (Basel). .

Abstract

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.

Keywords: emotion perception; human emotions; physiologic sensors.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Russel’s circumplex model of emotions.
Figure 2
Figure 2
Electroencephalography (EEG) measurements: (a) distribution of EEG electrodes on human scalp [39]; (b) special headset with installed electrodes [40].
Figure 3
Figure 3
EEG signal: (a) example of raw data [43]; (b) peak to peak signal amplitude evaluation technique [44].
Figure 4
Figure 4
Schematic representation of electrocardiography (ECG) [69]: (a) 12-lead ECG: RA, LA, LL, RL; (b) example of ECG signals.
Figure 5
Figure 5
ECG procedure [72]: (a) typical set up; (b) Main parameters of an ECG heartbeat signal.
Figure 6
Figure 6
Possible places for attaching GSR electrodes [86].
Figure 7
Figure 7
Example of raw GSR signal. The blue area indicates the phasic component of the signal; grey area represents the tonic component. The red line indicates the trigger (moment of delivery of the stimulus) [88].
Figure 8
Figure 8
Principle of photoplethysmography (PPG) [104]: (a) reflective mode; (b) transmitting mode; (c) example of PPG signal.
Figure 9
Figure 9
Comparison between ECG and PPG signals [109].
Figure 10
Figure 10
Example of skin temperature change due to applied stimulus [127].
Figure 11
Figure 11
Facial electromyography [149]: location of electrodes.
Figure 12
Figure 12
Example of EMG electrodes [148]: (a) needle electrode; (b) fine wire electrode; (c) gelled electrodes; (d) dry electrodes.
Figure 13
Figure 13
Principle of electrooculography (EOG): (a) electrode placement scheme [160]; (b) measurement principle [161].
Figure 14
Figure 14
Comparison between EOG and EMG signals during three different, sequential actions [162]: 1—Corrugator supercilii EMG; 2—vertical EOG; 3—horizontal EOG.
Figure 15
Figure 15
Classification of measurement methods for emotions recognition.
See this image and copyright information in PMC

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