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Videos de Conceptos Relacionados

Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Measurements of Strain01:27

Measurements of Strain

Strain quantifies the deformation of a material under force, typically measured as normal strain, which represents the change in length when compared with the original length. Electrical strain gauges are used for enhanced accuracy. These devices consist of a conductive wire mounted on a paper backing that adheres to the material's surface. These gauges operate on the piezoresistive effect, where the wire's electrical resistance changes in response to mechanical deformation. The strain gauge...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...

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Updated: May 10, 2026

Home-Based Monitor for Gait and Activity Analysis
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Un marco de aprendizaje profundo para la clasificación de fragilidad basada en la marcha utilizando unidades de

Arslan Amjad1, Agnieszka Szczęsna1, Monika Błaszczyszyn2

  • 1Department of Computer Graphics, Vision and Digital Systems, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland.

PloS one
|February 24, 2026
PubMed
Resumen

Este estudio presenta una nueva evaluación de la fragilidad utilizando sensores portátiles y aprendizaje profundo (DL) para clasificar a adultos mayores. El modelo InceptionTime logró una alta precisión, permitiendo la detección temprana de la fragilidad.

Palabras clave:
fragilidadaprendizaje profundosensores portátilesevaluación de la marchaadultos mayoresInceptionTimeintervención temprana

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Área de la Ciencia:

  • Gerontología
  • Ingeniería Biomédica
  • Inteligencia Artificial

Sus antecedentes:

  • La fragilidad en los adultos mayores aumenta los riesgos para la salud y los costos sociales.
  • Las evaluaciones actuales de fragilidad pueden ser lentas y subjetivas.
  • La detección e intervención tempranas son cruciales para el manejo de la fragilidad.

Objetivo del estudio:

  • Desarrollar un método avanzado de evaluación de la fragilidad utilizando sensores portátiles y aprendizaje profundo (DL).
  • Clasificar a los adultos mayores en etapas frágiles o no frágiles con precisión.
  • Permitir el monitoreo en tiempo real para intervenciones oportunas.

Principales métodos:

  • Se utilizaron dos conjuntos de datos (GSTRIDE, FRAILPOL) con 1-5 sensores de Unidad de Medición Inercial (IMU).
  • Se implementó un marco de partición de datos centrado en el participante con segmentación de ventanas de señales.
  • Se aplicaron y evaluaron varios algoritmos de DL, incluido InceptionTime.

Principales resultados:

  • InceptionTime logró una precisión del 82% en el conjunto de datos GSTRIDE y del 79% en el conjunto de datos FRAILPOL.
  • Alta precisión, recall y puntuaciones F1 confirmaron la efectividad del modelo.
  • El modelo capturó con éxito características espacio-temporales de las señales IMU brutas.

Conclusiones:

  • El enfoque propuesto de DL con sensores IMU portátiles ofrece un método eficaz para la evaluación de la fragilidad.
  • InceptionTime demuestra un rendimiento superior en la clasificación de las etapas de fragilidad.
  • Esta tecnología facilita el monitoreo objetivo y en tiempo real de la fragilidad y la intervención temprana.