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.
Highlights
Inertial data seems to be associated to fewer privacy concerns than the acquisition and processing of image data, even though computer vision techniques have been popular for Activities of Daily Living (ADL) recognition [14]
Considering eating recognition performance of the works that cared to preserve some of our important prerequisites, namely free-living utilization by using inertial data from an unobtrusive wristband, the results reported in this work appear to outperform those reported by [7] and [6], by achieving 97% F1-score for eating recognition in the all-day test set and 93% in the entire validation set with 5 different users, which included data from senior volunteers
This work proposed a new algorithm of activity recognition, which built upon the prior art to deliver a user-independent solution relying on a sensing system of low obstructiveness for the simultaneous recognition of eating and drinking activities in free-living conditions
Summary
The growth of the average life expectancy is due to the evolution of health systems, the pharmacological advances and the availability of biocompatible devices, associated with overall improved life conditions. Like smartwatches, are recognized for their high potential and utility to monitor activities (e.g., physical exercise). The recognition of Activities of Daily Living (ADL) has been extensively studied, using different data sources (inertial, image, RFID) and techniques. Inertial data seems to be associated to fewer privacy concerns than the acquisition and processing of image data, even though computer vision techniques have been popular for ADL recognition [14]. The related work overview of this section has, especial focus in inertial data-based approaches, following several techniques of time series analysis, further discussed and framed within the purpose of this work.
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