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Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
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Video Experimental Relacionado

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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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Un nuevo conjunto de datos para el reconocimiento de actividad de marcha en entornos del mundo real

John C Mitchell1,2, Abbas A Dehghani-Sanij1, Shengquan Xie3

  • 1School of Mechanical Engineering, University of Leeds, Leeds LS2 9JT, UK.

Sensors (Basel, Switzerland)
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Resumen

Este estudio presenta el conjunto de datos de reconocimiento de actividad humana consciente del contexto (CAHAR), el primero en etiquetar tanto actividades como terrenos para mejorar el análisis remoto de la marcha. Este recurso permite modelos avanzados de sensores portátiles para la evaluación del riesgo de caídas.

Palabras clave:
sensores de fuerzareconocimiento de actividad humanasensores inercialesentornos realessistemas de sensoresterrenosensores portátilesredes de sensores inalámbricos

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Área de la Ciencia:

  • Biomecánica
  • Tecnología de Sensores
  • Aprendizaje Automático

Sus antecedentes:

  • Las caídas son una preocupación mundial importante para la salud, y el análisis de la marcha es crucial para la identificación del riesgo de caídas.
  • Los sensores portátiles y el aprendizaje profundo ofrecen potencial para el análisis de marcha remoto, mejorando la calidad de los datos y la automatización.
  • El reconocimiento preciso de la actividad humana (HAR) y la clasificación del terreno son esenciales para el análisis de marcha en el mundo real.

Objetivo del estudio:

  • Abordar la falta de conjuntos de datos adecuados para la clasificación del terreno en el análisis de la marcha.
  • Presentar el conjunto de datos de reconocimiento de actividad humana consciente del contexto (CAHAR), el primer conjunto de datos etiquetado de actividad y terreno.
  • Facilitar el desarrollo de modelos de clasificación avanzados para el análisis de marcha remoto.

Principales métodos:

  • Se recopilaron datos de 20 participantes sanos utilizando unidades de medición inercial (IMU), plantillas de resistencia a la fuerza (FSR), sensores de color y LiDAR.
  • Se capturaron datos en una amplia gama de terrenos interiores y exteriores.
  • Se desarrolló el conjunto de datos CAHAR con etiquetas de actividad y terreno.

Principales resultados:

  • El conjunto de datos CAHAR es el primero de su tipo, que integra información sobre la actividad humana y el terreno del entorno.
  • El conjunto de datos apoya el desarrollo de modelos capaces de identificar simultáneamente HAR y el terreno.
  • Permite el progreso hacia un análisis de marcha remoto más sofisticado utilizando sensores portátiles.

Conclusiones:

  • El conjunto de datos CAHAR es un recurso importante para avanzar en la investigación en tecnología de sensores portátiles para el análisis de la marcha.
  • Proporciona una base para la creación de modelos de predicción de riesgo de caídas más precisos.
  • Facilita el desarrollo de sistemas inteligentes para el monitoreo de la marcha en el mundo real y la prevención de caídas.