MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02495nam a22001937a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
BML |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.3 |
| Item number |
PHU |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Phutela, Nishtha |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Development of stress induction and detection system to study its effect on brain |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Gurgaon |
| Name of publisher, distributor, etc |
BML Munjal University |
| Date of publication, distribution, etc |
2022 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
139p. |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis submitted in the fulfillment of the requirement for the degree of Doctor of Philosophy by Nishtha Phutela Under the supervision of Dr. Devanjali Relan, Prof. Goldie Gabrani and Prof. Ponnurangam Kumaraguru |
| Degree type |
Doctor of Philosophy |
| Year degree granted |
2022 |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Stress has become a significant mental health problem of the 21st century. The number of people suffering from stress is increasing rapidly. Thus, easy-to-use, inexpensive, and accurate biomarkers are needed to detect stress during its inception. Early detection of stress-related diseases allows people to access healthcare services. This thesis focuses on the development of stress stimuli and the detection of stress induced by these stimuli. Identifying brain regions affected while exposing the subject to these stressful stimuli has also been done. Three different stimuli, viz. videos, gamified application, and a game, are investigated to study their effect as stress induction stimuli. To this end, in this thesis, a system is proposed to classify participants into stressed and non-stressed categories using machine learning, deep learning, and statistical techniques. The statistical significance between stressed and non-stressed was found using Higuchi Fractal Dimensions (HFD) feature extracted from EEG. This feature also helped identify the brain s most affected region due to stress. Another outcome of this thesis is the extra annotation of the ground truth which further helps to validate the participant s experience under the influence of stressful stimuli. This annotation was performed by evaluating participant performance under time pressure. In addition, a technique based on in-game analytics is presented to complement the betterment of self-reported data. Further, another dimension utilizing signatures from WiFi Media Access Control (MAC) layer traffic is presented to detect stress indicators in a device-agnostic way. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Engineering and Technology |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computer Science Artificial Intelligence |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
<a href="https://shodhganga.inflibnet.ac.in/handle/10603/444027">https://shodhganga.inflibnet.ac.in/handle/10603/444027</a> |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
<a href="http://drc.bml.edu.in:8080/jspui/handle/123456789/2836">http://drc.bml.edu.in:8080/jspui/handle/123456789/2836</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Thesis |
