A Real-time Emotion Recognition System Based on an AI System-On-Chip Design

Li, Wei-Chih; Yang, Cheng-Jie; Fang, Wai-Chi

doi:10.1109/isocc50952.2020.9333072

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…On the other hand, it is hard to record participants' every emotional state accurately, especially when the limited options of questionnaire do not contain their subtle emotions, or they are even not sure how to describe their emotions [6]. The biological signals are much more objective and precise in terms of reflecting people's emotional states [6,111,112]. For example, for those who do not like to show their emotions, recognizing their emotions through appearance or behavior habits would be deceptive.…”

Section: Assessing Recognition Performance Using Biological Signalsmentioning

confidence: 99%

A Review of Emotion Recognition Methods From Keystroke, Mouse, and Touchscreen Dynamics

Yang

Qin²

2021

IEEE Access

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Emotion can be defined as a subject's organismic response to an external or internal stimulus event. The responses could be reflected in pattern changes of the subject's facial expression, gesture, gait, eye-movement, physiological signals, speech and voice, keystroke, and mouse dynamics, etc. This suggests that on the one hand emotions can be measured/recognized from the responses, and on the other hand they can be facilitated/regulated by external stimulus events, situation changes or internal motivation changes. It is well-known that emotion has a close relationship with both physical and mental health, usually affecting an individual's and a team's work performance, thus emotion recognition is an important prerequisite for emotion regulation towards better emotional states and work performance. The primary problem in emotion recognition is how to recognize a subject's emotional states easily and accurately. Currently, there are a body of good research on emotion recognition from facial expression, gesture, gait, eye-tracking, and other physiological signals such as speech and voice, but they are all intrusive and obtrusive to some extent. In contrast, keystroke, mouse and touchscreen (KMT) dynamics data can be collected non-intrusively and unobtrusively as secondary data responding to primary physical actions, thus, this paper aims to review the state-of-the-art research on emotion recognition from KMT dynamics and to identify key research challenges, opportunities and a future research roadmap for referencing. In addition, this paper answers the following six research questions (RQs): (1) what are the commonly used emotion elicitation methods and databases for emotion recognition? (2) which emotions could be recognized from KMT dynamics? (3) what key features are most appropriate for recognizing different specific emotions? (4) which classification methods are most effective for specific emotions? ( 5) what are the application trends of emotion recognition from KMT dynamics? (6) which application contexts are of greatest concern?

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Section: Assessing Recognition Performance Using Biological Signalsmentioning

confidence: 99%

A Review of Emotion Recognition Methods From Keystroke, Mouse, and Touchscreen Dynamics

Yang

Qin²

2021

IEEE Access

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“…For the detection of emotional and cognitive states, the researchers use different machine learning techniques, supporting real-time applications (Li, 2020), that require a parametrization of the utilized physiological signals, involving a specific processing for the EDA (Geršak, 2020), which is found to be very useful for emotion recognition (Ganapathy, 2021). The most commonly used parameter for the PPG (Moressi, 2021 is Heart Rate Variability (HRV), of which multiple methods for calculation are available (Kalinkov, 2020).…”

Section: Introductionmentioning

confidence: 99%

Preprocessing of PPG and EDA signals for detection of emotional and cognitive states via physiological signals

Kalinkov

Markova

2022

AJTUV

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Presented in the current paper is a methodology for approaching the preprocessing of Photoplethysmography and Electrodermal activity for the detection of emotional and cognitive states in humans via physiological signals. Examined closely are the effects of downsampling and segmentation of the PPG, the segmentation and separation of the Skin Conductance Level (SCL), and Skin Conductance Response (SCR) components of the EDA signal with both median and low pass filters. The results from the research indicate that the most appropriate preprocessing with regard to emotions and cognitive load classification is segmentation of 2 minutes which is the recommended length for frequency analysis of heart rate variability. Recommended, furthermore, is the downsampling of the PPG to 64 Hz, which proved to be the lowest sampling frequency that doesn’t introduce errors in the systolic peak detection, neither does it drastically affect the length of the Inter Beat Intervals (IBIs). Proposed, as to the separation of the SCL component of the EDA, is the usage of median filter with window length of 75% of the sampling frequency, which introduces negligible artefacts, mainly at the start of the signal.

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A Novel EEG-Based Real-Time Emotion Recognition Approach Using Deep Neural Networks on Raspberry Pi

Kleybolte

Märtin

2023

Lecture Notes in Computer Science

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A Real-time Emotion Recognition System Based on an AI System-On-Chip Design

Cited by 4 publications

References 2 publications

A Review of Emotion Recognition Methods From Keystroke, Mouse, and Touchscreen Dynamics

A Review of Emotion Recognition Methods From Keystroke, Mouse, and Touchscreen Dynamics

Preprocessing of PPG and EDA signals for detection of emotional and cognitive states via physiological signals

A Novel EEG-Based Real-Time Emotion Recognition Approach Using Deep Neural Networks on Raspberry Pi

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