Methods to Detect Emotion
Research successfully identified five different methods of detecting emotions that are backed by research evidence. The methods are Electroencephalogram emotion detection, brain activity emotion detection, three-stage emotion detection based on physiological signals, heart rate method, and movement-based emotion detection. A detailed report is presented below.
ELECTROCEPHALOGRAM (EEG) EMOTION DETECTION METHOD
- This emotion detection method introduces a way of recognizing changes in EEG features that point to changes emotions.
- The study concentrates on happiness and amusement emotions.
- It was expected that the PSD of Alpha waves of amusement and happiness are bigger than what is recorded by the emotions of fear.
- This method shows an emotion detection accuracy rate of 79.5%.
EMOTION DETECTION BASED ON BRAIN ACTIVITY
- This study outlines a method which uses brain activity to determine prevailing emotions.
- In the study, this method is used to measure the emotions like envy, anger, disgust, happiness, gratitude, lust, fear, shame, and pride.
- The study reported neural activation as the main reason for the recorded differences in brain images. Different emotional words and images handed to the participants resulted to different intensities of brain activity.
- The emotion detection accuracy level was reported to be 61%-81% for all emotions.
THREE-STAGE EMOTION DETECTION BASED ON PHYSIOLOGICAL SIGNS
- This method consists of three interlinked stages to eliminate errors and differences.
- The study focuses on active and pleasant emotions.
- Pleasant emotions are expected to calm the physiological body processes or result to small fluctuations.
- Emotion recognition accuracy rate of 86.7% has been recorded using this approach.
HEART RATE (HR) METHOD
- The study on cardiovascular features during emotional responses.
- This study focused on emotions of fear, anger, and happiness.
- Anger raised the heart rate, happiness resulted to a constant heart rate variance, and fear resulted to lower finger pulse amplitudes.
- This method successfully recognizes emotions with an accuracy rate of 84.1%.
RECOGNIZING EMOTION FROM BODY MOVEMENTS
- This method uses the Feed Forward Deep Convolution Network to predict the human emotion based on body movements.
- In this method the emphasis is on emotions like happiness, sadness, anger, fear, and untrustworthy.
- The system uses captured images to determine the emotional state of real-time images of people.
- This study yields an accuracy performance of rate 95% compared to previous studies that scored 86%-90%.
In order to determine the different methods of detecting emotion that are supported by research, we consulted scholarly publications for scientific studies on emotion detection. We restricted our research to studies published in the last 24 months and found resourceful studies although some of them draw insight from older studies in the same field. Ultimately, our attempt was successful and we found five methods of detecting emotions that are supported by scientific and academic research.