In old age, several common health conditions, chronic illnesses, and disabilities affect the individual’s physical and mental health and prevent him from carrying out Activities of Daily Living. In this context, this article presents a comparative study between some Machine Learning algorithms used to identify behavioral abnormalities based on ADL (Activities of Daily Living), through the Novelty Detection technique. ADL data from eHealth Monitoring Open Data Project database were used to create a model that defines the baseline behavior of an elderly person, and new observations, to verify significant changes in behavior, are classified as outliers or abnormal. The Local Outlier Factor, One-class Support Vector Machine, Robust Covariance, and Isolation Forest algorithms were analyzed, and the Local Outlier Factor obtained the best result, reaching a precision and F1-Score of 96%. As elderly people can have completely different routines, the data from the dataset used are not generalizable, but specific to everyone. In this work, the issue of model retraining is not evaluated, however, a variation is recommended in the period of weeks necessary for model retraining. Despite the good performance obtained, it is necessary to consider reproducing the experiments with data from other databases, to improve the generalization of the proposed solution, as well as to carry out a more refined validation. It is also necessary to carry out experiments to evaluate whether the variation in the types of activities carried out throughout a day by an elderly person, as well as the inclusion of new activities in the elderly person's routine, can impact the performance of the proposed model.

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Activities of daily living,Machine learning algorithms,Novelty detection

• Computer and Information Sciences - Natural Sciences
• Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology