Fts App: Towards Explainable AI-Driven Adaptive Weight Tracking
Bibliographic record
Abstract
We present the FTS app, an adaptive weight management tracking application that integrates an explainable artificial intelligence (XAI) model, specifically designed for normal weight individuals who aim to maintain their current weight status. Weight maintenance requires consistent monitoring of calories consumed from food and those expended through exercise, a challenging task with conventional apps available on Play Store or App Store. The FTS app uniquely analyzes both food logs and daily steps to predict users' current weight. A key feature is its explainable AI framework that helps users understand why certain foods might trigger weight fluctuations and offer healthier alternatives. The system integrates two complementary models: (1) a ChatGPT-4 model and (2) the proprietary FTS- XAI model, both designed to classify foods as healthy or unhealthy while calculating caloric content to estimate the user's current weight. The app includes a built-in step counting module that tracks physical activity before meals, eliminating the need for expensive wearable devices and reducing privacy concerns related to thirdparty access. By combining these calorie metrics, the app automatically calculates the user's current weight and keeps them informed. This paper presents our design framework to guide the research community in addressing the needs of this predominantly overlooked population with weight maintenance goals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; both teacher heads agree on what is shown here.
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".