Name
Title | ||
|---|---|---|
Eating and Drinking Recognition in Free-Living Conditions for Triggering Smart Reminders. |
Abstract | ||
|---|---|---|
The increasingly aging society in developed countries has raised attention to the role of technology in seniors' lives, namely concerning isolation-related issues. Independent seniors that live alone frequently neglect meals, hydration and proper medication-taking behavior. This work aims at eating and drinking recognition in free-living conditions for triggering smart reminders to autonomously living seniors, keeping system design considerations, namely usability and senior-acceptance criteria, in the loop. To that end, we conceived a new dataset featuring accelerometer and gyroscope wrist data to conduct the experiments. We assessed the performance of a single multi-class classification model when compared against several binary classification models, one for each activity of interest (eating vs. non-eating; drinking vs. non-drinking). Binary classification models performed consistently better for all tested classifiers (k-NN, Naive Bayes, Decision Tree, Multilayer Perceptron, Random Forests, HMM). This evidence supported the proposal of a semi-hierarchical activity recognition algorithm that enabled the implementation of two distinct data stream segmentation techniques, the customization of the classification models of each activity of interest and the establishment of a set of restrictions to apply on top of the classification output, based on daily evidence. An F1-score of 97% was finally attained for the simultaneous recognition of eating and drinking in an all-day acquisition from one young user, and 93% in a test set with 31 h of data from 5 different unseen users, 2 of which were seniors. These results were deemed very promising towards solving the problem of food and fluids intake monitoring with practical systems which shall maximize user-acceptance. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/s19122803 | SENSORS |
Keywords | Field | DocType |
classification,data segmentation,drinking recognition,eating recognition,elderly care,human activity recognition | Decision tree,Activity recognition,Binary classification,Naive Bayes classifier,Usability,Electronic engineering,Artificial intelligence,Engineering,Random forest,Hidden Markov model,Machine learning,Personalization | Journal |
Volume | Issue | ISSN |
19 | 12 | 1424-8220 |
Citations | PageRank | References |
1 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Diana Gomes | 1 | 1 | 2.03 |
| João Mendes-Moreira | 2 | 317 | 29.50 |
| Inês Sousa | 3 | 1 | 1.02 |
| Joana Silva | 4 | 15 | 1.69 |