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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.

Addressing the health challenges faced by the aging population, particularly undernutrition, is of paramount importance, given the significant representation of older individuals in society. Undernutrition arises from a disbalance between nutritional intake and energy expenditure, making its diagnosis crucial. Advances in technology allowed a better precision and efficiency of biomarker measurements, making it easier to detect undernutrition in the elderly. This article introduces an innovative system developed as part of the CART initiative at Toulouse University Hospital in France. This system takes a comprehensive approach to monitor health and well-being, collecting data that can provide insights, shape health outcomes, and even predict them. A key focus of this system is on identifying nutrition-related behaviors. By integrating quantitative and clinical assessments, which include biannual nutritional evaluations, as well as physical and physiological measurements like mobility and weight, this approach improves the diagnosis and prevention of undernutrition risks. It offers a more holistic perspective aligned with physiological standards. An example is given with the data collection of an elderly person followed at home for 3 months. We believe that this advance could make a significant contribution to the overall improvement of health and well-being, particularly in the elderly population.