Examinando por Autor "Neira Álvarez, Marta"

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A database with frailty, functional and inertial gait metrics for the research of fall causes in older adults
(Nature, 2023-08-25) García-de-Villa, Sara; García-Villamil Neira, Guillermo; Neira Álvarez, Marta; Huertas-Hoyas, Elisabet; Ruiz Ruiz, Luisa; J. del-Ama, Antonio; Rodríguez Sánchez, María Cristina; Jiménez, Antonio R.
The GSTRIDE database contains information of the health status assessment of 163 elderly adults. We provide socio-demographic data, functional and frailty variables, and the outcomes from tests commonly performed for the evaluation of elder people. The database contains gait parameters estimated from the measurements of an Inertial Measurement Unit (IMU) placed on the foot of volunteers. These parameters include the total walking distance, the number of strides and multiple spatio-temporal gait parameters, such as stride length, stride time, speed, foot angles and clearance, among others. The main processed database is stored, apart from MS Excel, in CSV format to ensure their usability. The database is complemented with the raw IMU recordings in TXT format, in order to let researchers test other algorithms of gait analysis. We include the Python programming codes as a base to reproduce or modify them. The database stores data to study the frailty-related parameters that distinguish faller and non-faller populations, and analyze the gait-related parameters in the frail subjects, which are essential topics for the elderly.
Validation of an IMU-based Gait Analysis Method for Assessment of Fall Risk Against Traditional Methods
(Institute of Electrical and Electronics Engineers, 2024-07-29) García-de-Villa, Sara; Ruiz Ruiz, Luisa; García-Villamil Neira, Guillermo; Neira Álvarez, Marta; Huertas-Hoyas, Elisabet; del-Ama, Antonio J.
Falls are a severe problem in older adults, often resulting in severe consequences such as injuries or loss of consciousness. It is crucial to screen fall risk in order to prescribe appropriate therapies that can potentially prevent falls. Identifying individuals who have experienced falls in the past, commonly known as fallers, is used to evaluate fall risk, as a prior fall indicates a higher likelihood of future falls. The methods that have the most support from evidence are Gait Speed (GS) and Time Up and Go (TUG), which use specific cut-off values to evaluate the fall risk. There have been proposals for alternative methods that use wearable sensor technology to improve fall risk assessment. Although these technological alternatives are promising, further research is necessary to validate their use in clinical settings. In this study, we propose a method for identifying fallers based on a Support Vector Machine (SVM) classifier. The inputs for the classifier are the gait parameters obtained from a 30-minute walk recorded using an Inertial Measurement Unit (IMU) placed at the foot of patients. We validated our proposed method using a sample of 157 patients aged over 70 years. Our findings indicate significant differences (p< 0.05) in stride speed, clearance, angular velocity, acceleration, and coefficient of variability among steps between fallers and non-fallers. The proposed method demonstrates the its potential to classify fallers with an accuracy of [79.6]% , slightly outperforming the GS method which provides an accuracy of [77.0]% , and also overcomes its dependency on the cut-off speed to determine fallers. This method could be valuable in detecting fallers during long-term monitoring that does not require periodic evaluations in a clinical setting