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Eating and drinking recognition in free-living conditions for triggering smart reminders

 
: Gomes, D.; Mendes-Moreira, J.; Sousa, I.; Silva, J.

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Volltext ()

Sensors. Online journal 19 (2019), Nr.12, Art. 2803, 22 S.
http://www.mdpi.com/journal/sensors
ISSN: 1424-8220
ISSN: 1424-8239
ISSN: 1424-3210
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer AICOS ()

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.

: http://publica.fraunhofer.de/dokumente/N-558514.html