1. Analysis of the correspondence of the degree of fragility with the way to exercise the force of the hand
- Author
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Universitat Politècnica de Catalunya. Departament de Resistència de Materials i Estructures a l'Enginyeria, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. TOC - Tecnologia orientada a la comunitat, Pérez Guindal, Elsa, Llanas Parra, Francesc Xavier, Musté Rodríguez, Marta, Pérez López, Carlos, Macho Pérez, Oscar, Català Mallofré, Andreu, Universitat Politècnica de Catalunya. Departament de Resistència de Materials i Estructures a l'Enginyeria, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. TOC - Tecnologia orientada a la comunitat, Pérez Guindal, Elsa, Llanas Parra, Francesc Xavier, Musté Rodríguez, Marta, Pérez López, Carlos, Macho Pérez, Oscar, and Català Mallofré, Andreu
- Abstract
BACKGROUND: Frailty is a geriatric syndrome characterized by increased individual vulnerability with an increase in both dependence and mortality when exposed to external stressors. The use of Frailty Indices in routine clinical practice is limited by several factors, such as the cognitive status of the patient, times of consultation, or lack of prior information from the patient. OBJECTIVES: In this study, we propose the generation of an objective measure of frailty, based on the signal from hand grip strength (HGS). DESIGN AND MEASUREMENTS: This signal was recorded with a modified Deyard dynamometer and processed using machine learning strategies based on supervised learning methods to train classifiers. A database was generated from a cohort of 138 older adults in a transverse pilot study that combined classical geriatric questionnaires with physiological data. PARTICIPANTS: Participants were patients selected by geriatricians of medical services provided by collaborating entities. SETTINGS AND RESULTS: To process the generated information 20 selected significant features of the HGS dataset were filtered, cleaned, and extracted. A technique based on a combination of the Synthetic Minority Oversampling Technique (SMOTE) to generate new samples from the smallest group and ENN (technique based on K-nearest neighbors) to remove noisy samples provided the best results as a well-balanced distribution of data. CONCLUSION: A Random Forest Classifier was trained to predict the frailty label with 92.9% of accuracy, achieving sensitivities higher than 90%., This work was partially supported by the Spanish Ministry of “Ciencia, Innovación y Universidades” under project RTI2018-096701-B-C22, and by the Catalonia FEDER program, resolution GAH/815/2018 under the project, “PECT Garraf: Envelliment actiu i saludable i dependència”. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature., Peer Reviewed, Postprint (published version)
- Published
- 2024