Student Work
Mental Health Sensing Using Machine Learning
PublicDownloadable Content
open in viewerUsing audio and text data from multiple sources, we evaluated the viability of using machine and deep learning to identify depression and anxiety. Machine learning methods using sub-clip boosting achieved an F1 score of 0.81 for depression and 0.83 for anxiety. Our convolutional neural networks and long-term short term memory models achieved F1 scores of 0.55 and 0.68 respectively for depression. As feature engineering, we used topological data analysis to create Betti curves in our machine learning pipeline. Furthermore, we developed a pipeline to generate text messages with deep learning models, for data augmentation purposes.
- This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.
- Creator
- Publisher
- Identifier
- E-project-040220-225214
- Advisor
- Year
- 2020
- Date created
- 2020-04-02
- Resource type
- Major
- Rights statement
Relations
- In Collection:
Items
Items
Thumbnail | Title | Visibility | Embargo Release Date | Actions |
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Mental_Health_Sensing_Final_Report.pdf | Public | Download |
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