Fitness Guide With Mental Health Support Using Fitmind
Bibliographic record
Abstract
This project delivers physical fitness and mentalwellness in today’s fast-paced lifestyle which hasbecome a crucial part of our life. While there arenumerous mobile and web applications addressingeither fitness or mental health, very few offer aholistic, intelligent, and integrated solution. The FitMind system is a comprehensive web-basedapplication designed to promote overall well-beingthrough the power of artificial intelligence andmachine learning. The platform provides three majorfeatures: a personalized Fitness Planner, a MentalHealth Tracker, and a Meditation & Wellness Advisor,all powered by robust ML models. The Fitness PlannerModule predicts a user’s fitness category based onpersonal parameters like age, BMI, activity level, andlifestyle habits, and recommends a suitable workoutplan and diet. The Mental Health Tracker Moduleleverages psychological screening scales like PHQ-9(Patient Health Questionnaire-9), GAD-7(Generalized Anxiety Disorder-7), and DASS-21(Depression, Anxiety, and Stress Scale) to assessmental health conditions, and provides tailored advicefor improving mental wellness. The Meditation &Wellness Module evaluates lifestyle parameters suchas sleep quality, screen time, stress levels, andmindfulness score, then suggests guided meditations,breathing techniques, and daily wellness tips.In addition, the system incorporates an AI-basedChatbot Module that uses natural languageprocessing (NLP) and a Naive Bayes classifier tounderstand user queries and offer instant responsesrelated to fitness, mental health, and meditation.Developed using Python, Flask, and MySQL, Fit Minddelivers an interactive and user-friendly experiencewith real-time recommendations and progresstracking.Overall, FitMind serves as a smart virtual wellnessassistant aimed at making mental and physical wellbeingaccessible, personalized, and engaging for allusers, especially students and working professionals.Its modular design also makes it scalable for futureintegration with wearable health devices and mobileplatforms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.095 | 0.032 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".