Identifying Physical Worsening in Elderly Using Objective and Self-Reported Measures

M. Abbas (1) , Dominique Somme (2, 3) , R. Le Bouquin Jeannes (1)

1 LTSI - Laboratoire Traitement du Signal et de l'Image
2 ARENES - Arènes: politique, santé publique, environnement, médias
3 Centre Hospitalier Universitaire de Rennes [CHU Rennes] = Rennes University Hospital [Ponchaillou]

This paper investigates the possibility of predicting physical weakening in older adults in view to detect early the on-set of frailty process. This study is based on two types of features, namely (i) measured features, which are calculated objectively using performance tests and questionnaires, and (ii) self-reported features, which are based on the older person's auto-evaluation. Two machine learning-based models are proposed. The first one identifies the potential occurrence of physical weakening by comparing the evolution of the aforementioned features between two time slots. The second one predicts a future worsening based on the current values of these features. Both models are evaluated and interpreted using a public dataset. © 2021 IEEE.

Mots clés: Frailty; Measured features; Older adults; Physical weakening; Self-reported features

Domaines: Ingénierie biomédicale

M. Abbas, Dominique Somme, R. Le Bouquin Jeannes. Identifying Physical Worsening in Elderly Using Objective and Self-Reported Measures. 6th International Conference on Advances in Biomedical Engineering, ICABME 2021, Oct 2021, Werdanyeh, Lebanon. pp.193-196, ⟨10.1109/ICABME53305.2021.9604819⟩. ⟨hal-03556737⟩