Emotion, Expressed Clinical Trial
Official title:
Study of Evoked Emotional Responses for Supporting Research of EMO-001 Device Using Photoplethysmography, Galvanic Skin Response, and Electrocardiography
Mental health and emotional awareness are a crucial need of the time. Changing lifestyles,
stress, and anxiety are seen more commonly and affect many adults in the United States and
other countries. Primary aim of this study is to identify the bio-physiological data that is
corrected emotions of a person in support of EMO-001 device research.
This study will induce different emotions in the test subjects and collect physiological
response signals using Photo Plethysmography (PPG), Galvanic Skin Response (GSR) and
Electrocardiogram (ECG) sensors. The sensor data is digitally recorded in a storage bank. The
data will be subsequently used to develop supervised and unsupervised classification
algorithms.
Emotional states considered in this study are Anger, Amusement, Neutral, Disgust, Sadness,
Scared, Surprise and Thrill. These emotions were induced by showing video clips of three to
five minutes to the subjects. For each emotion three clips were shown to each subject. Video
clips are sourced from movies, TV shows, and real-life recordings. Subjects evaluated each
video clip and classify them into perceived emotional ratings.
Data is processed through a series of filter and transformation methods. The transformed data
is used to develop and calibrate algorithms that can identify emotions.
Millions of people are affected by mental health conditions every year. Approximately one in
five adults in the U.S. experiences at least one mental illness episode every year.
Approximately 16 million people have at least one major depressive episode in one year.
Researchers have identified that Millennial age-group is at highest risk of developing
anxiety, depression, and thoughts of suicide than any other generation. These conditions are
attributed by many reasons like increasing competition, less communication, less real-world
attachment, work pressure, etc.
Considering the increasing level of issues related to mental health, compelled to think about
understanding emotions and associated health impact. Popular methods of emotion detection are
by detecting facial expression but to detect real-time emotion, biophysical signals are the
best tool as per literature. Also, by considering the future possibility of product form of
EMO-001, emotion detection through bio-signals proved to be the correct approach. The intent
of the research is to classify the evoked emotions based on bio-signals in support of the
product EMO-001 device using photo plethysmography (PPG), Galvanic Skin Response (GSR) and
Electrocardiogram (ECG). Identification of emotions from bio-signals, and development of
classification algorithm helps the product to identify the emotional state of the person
real-time and eventually helps to track the emotional state and suggest an activity which can
lift the emotional well being of a person.
Major six emotions, amusement, sad, disgust, scared, surprised, thrill were considered to
collect bio signal. Bio signals for the neutral state were also captured to create a baseline
for identifying changes in the bio signals with changing emotions.
To find the gender balance in subjects, a similar ratio of male and female considered.
Targeting Millennial age-group for this product, we consider people from the age of 18 to 33
years with a minimum bachelor degree qualification and those who are employed. We considered
people who understand English and Hindi are considered as subject as video content were in
these languages only.
Exploring different methods of emotion elicitation, an audio visual method was considered for
this trial, as it is the most effective methods described in different previous literature
and through practical scenario. More than 1,000 Video clips from open platform were seen and
analysed for five different emotions. Out of which 100 video (20 per emotion) clips were
scrutinized by three level checks, selected video clips were also validated by internal blind
check to figure out what exact emotion has been elicited from each video. From these
identified clips, different set of clips had been prepared. We could identify that video
clips are less effective to elicit the anger; hence interaction method was used for it.
Internal validation of data capturing sensors and assembly done to ensure the bio signals
obtained are of good quality. For the collection of bio signals, subjects were asked to sit
in a proper position, and sensors were placed at particular place of the body.
When the subject was ready for the participation, initially for 30 seconds neutral pictures
(e.g. nature sites) were shown to neutralize his/her emotions. After that starting with any
of the emotion, three randomly selected videos of a particular emotion from data bank was
shown, each followed by a neutral clipping to neutralize the effect of the earlier video. In
parallel of videos, the bio-signal data was collected from the sensors. After each video, the
subject was asked to fill up a feedback form to rate his emotions. After finishing the signal
collection of five different emotions and neutral in between, interaction with subject
initiated where the subject was asked about their general background and slowly turned to
some sensitive topic which helped to elicit the anger emotion.
The trial had completely non-invasive intervention, where participant need to sit for two
hours to watch the videos. Collected data would further be analysed and used for development
of emotion classification algorithm.
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