Contactless Drink Intake Monitoring Using Depth Data
Bibliographic record
Abstract
It is important for humans to remain hydrated, particularly for older adults who are at a greater risk of dehydration and may forget to drink. Monitoring liquid intake and getting reminders to drink throughout the day is a useful solution to increase hydration levels. The objective of this paper is to automatically detect drink events from multiple containers in a simulated home environment using a vision-based approach. The proposed work compares the use of depth and RGB (red, green, blue) cameras for this task. In this paper, we compared 2D and 3D Convolutional Neural Networks (CNN) using RGB and depth cameras. We collected data from nine participants performing drinking, eating and other Activities of Daily Living (ADL) in a simulated home environment. We found that for the 3D models, the RGB and depth camera inputs provided very similar F1-scores for both 10-Fold (94.3% vs 93.9%, respectively) and Leave-One-Subject-Out (LOSO) cross validation (84.2% vs 86.2%, respectively). This is a promising result as depth cameras also mitigate the challenges to privacy of RGB-based models. The 3D CNN models outperformed the 2D models, thereby creating a more robust system. Depth cameras are a useful alternative to RGB cameras with equal performance in identifying drinking events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".