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Manifestaciones Clínicas

Md Saif Hassan Onim1, Himanshu Thapliyal1

  • 1University of Tennessee, Knoxville, TN, USA.

Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 26, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Los sensores portátiles muestran una correlación limitada con las expresiones faciales para la detección de emociones en adultos mayores. El aprendizaje automático puede mejorar la precisión en la detección de emociones en esta población.

Palabras clave:
sensores portátilesexpresiones facialesdetección de emocionesadultos mayoresaprendizaje automático

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Área de la Ciencia:

  • Gerontología; Computación Afectiva; Ingeniería Biomédica

Sus antecedentes:

  • La evaluación emocional es vital para los adultos mayores, especialmente aquellos con la enfermedad de Alzheimer y demencias relacionadas con la enfermedad de Alzheimer (AD/ADRD).
  • Las expresiones faciales son el estándar de oro para la detección de emociones en este grupo demográfico.
  • La detección de emociones a través de sensores portátiles ofrece aplicabilidad en el mundo real y un interés significativo.

Objetivo del estudio:

  • Capturar estados emocionales en adultos mayores utilizando sensores portátiles.
  • Correlacionar estos estados emocionales con datos fisiológicos.
  • Establecer la expresión facial como el estándar de oro para la comparación en el desarrollo de sistemas de detección de emociones portátiles para adultos mayores y personas con AD/ADRD.

Principales métodos:

  • Participaron 50 adultos mayores (60-75 años), que usaron sensores para datos fisiológicos (GSR, FC, PPG).
  • El reconocimiento facial analizó expresiones en tiempo real, cuantificando emociones (Alegría, Ira, Sorpresa, Miedo, Desprecio, Aversión, Tristeza).
  • Se analizaron la distribución de datos y los mapas de calor de correlación.

Principales resultados:

  • La alegría y la ira fueron las emociones detectadas con mayor frecuencia.
  • La alegría mostró una correlación positiva débil con la frecuencia cardíaca (r=0.15, p<0.05).
  • El miedo mostró una correlación moderada con la conductancia de la GSR (r=0.35, p<0.01), mientras que el desprecio y la aversión tuvieron una correlación mínima con los datos fisiológicos.

Conclusiones:

  • Se encontró una correlación limitada entre las expresiones faciales y los datos de los sensores portátiles.
  • Los modelos estadísticos tradicionales pueden no capturar la dinámica no lineal de los datos emocionales.
  • La investigación futura debe utilizar el aprendizaje automático para mejorar la precisión de la detección emocional en adultos mayores.