Physiological sensor based affective state recognition
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
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Brac University
2021
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10361-155052022-01-26T10:18:16Z Physiological sensor based affective state recognition Habib, Fahim Fazle Mohammad, Khaled Sami, Sikder Shadman Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Physiology This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 32-33). With rapid advancements of Medical IoT sensors in recent years, using them to recognize an individual’s affective state has become more easily attainable. If an individual’s physiological signals are recorded while they are made to experience certain feelings, the data can be used to create a model that can recognize those feelings using the sensor data. In this paper, a system is created to use data collected from physiological sensors to predict the affective state of the individual the data is extracted from. First, the sensor data was trimmed down to just the portions where the participants experience the feeling and filtered to get rid of unnecessary features and bad data. Then, the data was processed to condense the sensor readings of the entire time a user experienced a feeling into a single row that represents that time period. Finally, the data was mapped to the feeling felt. Instead of using generic colloquial terms for emotions, more abstract notions of defining emotions were used - specifically, the Valence-Arousal-Dominance space which defines emotions using these three parameters. Using that data-set, feature selection was done to find the most important features to feed to Machine Learning Models to detect the affective state of the patient in the Valence-Arousal-Dominance space. The novelty of our research comes from the features used to predict the emotions, which include statistical representations of the raw signal data and special domain features that give further insight into the signal data from EEG and ECG. Fahim Fazle Habib Khaled Mohammad Sikder Shadman Sami B. Computer Science 2021-10-21T05:01:07Z 2021-10-21T05:01:07Z 2021 2021-01 Thesis ID 16201048 ID 19141028 ID 20141031 http://hdl.handle.net/10361/15505 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 33 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Physiology |
| spellingShingle |
Physiology Habib, Fahim Fazle Mohammad, Khaled Sami, Sikder Shadman Physiological sensor based affective state recognition |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Alam, Md. Golam Rabiul |
| author_facet |
Alam, Md. Golam Rabiul Habib, Fahim Fazle Mohammad, Khaled Sami, Sikder Shadman |
| format |
Thesis |
| author |
Habib, Fahim Fazle Mohammad, Khaled Sami, Sikder Shadman |
| author_sort |
Habib, Fahim Fazle |
| title |
Physiological sensor based affective state recognition |
| title_short |
Physiological sensor based affective state recognition |
| title_full |
Physiological sensor based affective state recognition |
| title_fullStr |
Physiological sensor based affective state recognition |
| title_full_unstemmed |
Physiological sensor based affective state recognition |
| title_sort |
physiological sensor based affective state recognition |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15505 |
| work_keys_str_mv |
AT habibfahimfazle physiologicalsensorbasedaffectivestaterecognition AT mohammadkhaled physiologicalsensorbasedaffectivestaterecognition AT samisikdershadman physiologicalsensorbasedaffectivestaterecognition |
| _version_ |
1814308754733137920 |