BACKGROUND: A standard phenotype of frailty was independently associated with an increased risk of adverse outcomes including comorbidity, disability and with increased risks of subsequent falls and fractures. Postural control deficit measurement during quiet standing has been often used to assess balance and fall risk in elderly frail population. Real time human motion tracking is an accurate, inexpensive and portable system to obtain kinematic and kinetic measurements. The aim of this study was to examine orientation and acceleration signals from a tri-axial inertial magnetic sensor during quiet standing balance tests using the wavelet transform in a frail, a prefail and a healthy population. METHODS: Fourteen subjects from a frail population (79±4 years), eighteen subjects from a prefrail population (80±3 years) and twenty four subjects from a healthy population (40±3 years) volunteered to participate in this study. All signals were analyzed using time-frequency information based on wavelet decomposition and principal component analysis. FINDINGS: The absolute sum of the coefficients of the wavelet details corresponding to the high frequencies component of orientation and acceleration signals were associated with frail syndrome. INTERPRETATION: These parameters could be of great interest in clinical settings and improved rehabilitation therapies and in methods for identifying elderly population with frail syndrome.
BACKGROUND: A standard phenotype of frailty was independently associated with an increased risk of adverse outcomes including comorbidity, disability and with increased risks of subsequent falls and fractures. Postural control deficit measurement during quiet standing has been often used to assess balance and fall risk in elderly frail population. Real time human motion tracking is an accurate, inexpensive and portable system to obtain kinematic and kinetic measurements. The aim of this study was to examine orientation and acceleration signals from a tri-axial inertial magnetic sensor during quiet standing balance tests using the wavelet transform in a frail, a prefail and a healthy population. METHODS: Fourteen subjects from a frail population (79±4 years), eighteen subjects from a prefrail population (80±3 years) and twenty four subjects from a healthy population (40±3 years) volunteered to participate in this study. All signals were analyzed using time-frequency information based on wavelet decomposition and principal component analysis. FINDINGS: The absolute sum of the coefficients of the wavelet details corresponding to the high frequencies component of orientation and acceleration signals were associated with frail syndrome. INTERPRETATION: These parameters could be of great interest in clinical settings and improved rehabilitation therapies and in methods for identifying elderly population with frail syndrome.
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Authors: Alicia Martínez-Ramírez; Ion Martinikorena; Marisol Gómez; Pablo Lecumberri; Nora Millor; Leocadio Rodríguez-Mañas; Francisco José García García; Mikel Izquierdo Journal: J Neuroeng Rehabil Date: 2015-05-24 Impact factor: 4.262