Mental Health Tracker

Authors

  •   Mahati Dhananjay Gholap Student, Bachelors of Engineering (Information Technology), Vidyavardhini's College of Engineering and Technology, K.T. Marg, Vartak College Campus, Vasai Road (W), Palghar, Vasai, Maharashtra - 401 202
  •   Hardik Govind Dangiya Student, Bachelors of Engineering (Information Technology), Vidyavardhini's College of Engineering and Technology, K.T. Marg, Vartak College Campus, Vasai Road (W), Palghar, Vasai, Maharashtra - 401 202
  •   vaishnavi Kishore Rashivadekar Student, Bachelors of Engineering, Information Technology, Vidyavardhini's College of Engineering and Technology, K.T. Marg, Vartak College Campus, Vasai Road (W), Palghar, Vasai, Maharashtra - 401 202

DOI:

https://doi.org/10.17010/ijcs/2023/v8/i3/172865

Keywords:

Android Application

, Anxiety, Authentication, Deep Learning, Depression, Machine Learning, Mental Health, Prediction, Questions, Sentiment Analysis.

Paper Submission Date

, April 18, 2023, Paper sent back for Revision, April 28, Paper Acceptance Date, May 5, Paper Published Online, June 5, 2023

Abstract

The project’s main objective is to create a mental health tracker. There is a need to evaluate people’s mental state in the least intrusive manners possible to determine whether they are in pain, and then provide steps they may take to improve their situation. Users are asked a series of questions, and depending on their responses, tasks are recommended to them to keep track of their mental condition for usage in dashboard displays. All across the world, mental illnesses are very common. However, there is a global shortage of personnel who can provide mental health services. If mental illness is not treated, mortality and suicide attempts may rise. Conversational assistants have gained popularity in recent years as a solution to the problem of scarce resources. In this study, we present a mobile app with an integrated Chatbot that uses techniques from cognitive behavior therapy to help mentally ill people manage their emotions and thoughts. Daily questions from the application ask the user about recent events and their feelings. Using lexicon-based analysis and Natural Language processing, it automatically determines the user’s fundamental emotion from the input of natural language.

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Published

2023-07-06

How to Cite

Gholap, M. D., Dangiya, H. G., & Rashivadekar, vaishnavi K. (2023). Mental Health Tracker. Indian Journal of Computer Science, 8(3), 25–32. https://doi.org/10.17010/ijcs/2023/v8/i3/172865

Issue

Volume 8, Issue 3, May-June 2023

Section

Articles
Crossref
Scopus
Google Scholar
Europe PMC

References

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