eFurniture for home-based frailty detection using artificial neural networks and wireless sensors.
Med Eng Phys
; 35(2): 263-8, 2013 Feb.
Article
em En
| MEDLINE
| ID: mdl-21981806
ABSTRACT
The purpose of this study is to integrate wireless sensor technologies and artificial neural networks to develop a system to manage personal frailty information automatically. The system consists of five parts (1) an eScale to measure the subject's reaction time; (2) an eChair to detect slowness in movement, weakness and weight loss; (3) an ePad to measure the subject's balancing ability; (4) an eReach to measure body extension; and (5) a Home-based Information Gateway, which collects all the data and predicts the subject's frailty. Using a furniture-based measuring device to provide home-based measurement means that health checks are not confined to health institutions. We designed two experiments to obtain optimum frailty prediction model and test overall system performance (1) We developed a three-step process to adjust different parameters to obtain an optimized neural identification network whose parameters include initialization, L.R. dec and L.R. inc. The post-process identification rate increased from 77.85% to 83.22%. (2) We used 149 cases to evaluate the sensitivity and specificity of our frailty prediction algorithm. The sensitivity and specificity of this system are 79.71% and 86.25% respectively. These results show that our system is a high specificity prediction tool that can be used to assess frailty.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Idoso Fragilizado
/
Redes Neurais de Computação
/
Telemedicina
/
Tecnologia sem Fio
/
Habitação
/
Decoração de Interiores e Mobiliário
/
Monitorização Fisiológica
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Aged
/
Female
/
Humans
/
Male
Idioma:
En
Ano de publicação:
2013
Tipo de documento:
Article