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Related Experiment Video

Updated: Jan 7, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Clinical Manifestations.

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
Summary
This summary is machine-generated.

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Wearable sensors show limited correlation with facial expressions for emotion detection in older adults. Machine learning may improve accuracy for detecting emotions in this population.

Area of Science:

  • Gerontology
  • Affective Computing
  • Biomedical Engineering

Background:

  • Emotional assessment is vital for older adults, especially those with Alzheimer's Disease and Alzheimer's Disease Related Dementias (AD/ADRD).
  • Facial expressions are the gold standard for emotion detection in this demographic.
  • Emotion detection via wearable sensors offers real-world applicability and significant interest.

Purpose of the Study:

  • To capture emotional states in older adults using wearable sensors.
  • To correlate these emotional states with physiological data.
  • To establish facial expression as the gold standard for comparison in developing wearable emotion detection systems for older adults and individuals with AD/ADRD.

Main Methods:

  • 50 older adults (60-75 years) participated, wearing sensors for physiological data (GSR, HR, PPG).

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  • Facial recognition analyzed real-time expressions, quantifying emotions (Joy, Anger, Surprise, Fear, Contempt, Disgust, Sadness).
  • Data distribution and correlation heatmaps were analyzed.

    • Main Results:

    • Joy and Anger were the most frequent emotions detected.
    • Joy showed a weak positive correlation with heart rate (r=0.15, p<0.05).
    • Fear showed a moderate correlation with GSR conductance (r=0.35, p<0.01), while Contempt and Disgust had minimal correlation with physiological data.

    Conclusions:

    • Limited correlation was found between facial expressions and wearable sensor data.
    • Traditional statistical models may not capture the non-linear dynamics of emotional data.
    • Future research should utilize machine learning for improved emotional detection accuracy in older adults.