Meal-taking activity monitoring in the elderly based on sensor data: Comparison of unsupervised classification methods

TL;DR

This study compares unsupervised clustering methods to improve meal-taking activity detection in elderly individuals using sensor data, finding K-Means most effective for clear and efficient clustering.

Contribution

It introduces a comparative analysis of K-Means, GMM, and DBSCAN for elderly meal activity monitoring, highlighting K-Means as the most efficient method.

Findings

K-Means achieved the lowest Davies-Bouldin Index indicating optimal clustering.

GMM effectively distinguished activity categories based on duration.

DBSCAN identified complex patterns but was less practical due to parameter sensitivity.

Abstract

In an era marked by a demographic change towards an older population, there is an urgent need to improve nutritional monitoring in view of the increase in frailty. This research aims to enhance the identification of meal-taking activities by combining K-Means, GMM, and DBSCAN techniques. Using the Davies-Bouldin Index (DBI) for the optimal meal taking activity clustering, the results show that K-Means seems to be the best solution, thanks to its unrivalled efficiency in data demarcation, compared with the capabilities of GMM and DBSCAN. Although capable of identifying complex patterns and outliers, the latter methods are limited by their operational complexities and dependence on precise parameter configurations. In this paper, we have processed data from 4 houses equipped with sensors. The findings indicate that applying the K-Means method results in high performance, evidenced by a particularly low Davies-Bouldin Index (DBI), illustrating optimal cluster separation and cohesion. Calculating the average duration of each activity using the GMM algorithm allows distinguishing various categories of meal-taking activities. Alternatively, this can correspond to different times of the day fitting to each meal-taking activity. Using K-Means, GMM, and DBSCAN clustering algorithms, the study demonstrates an effective strategy for thoroughly understanding the data. This approach facilitates the comparison and selection of the most suitable method for optimal meal-taking activity clustering.