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Quantifying Mild Cognitive Impairments in Older Adults Using Multi-modal Wearable Sensor Data in a Kitchen

Bonwoo Koo1, Ibrahim Bilau2, Amy D Rodriguez3

  • 1KAIST, Daejeon, South Korea.

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|July 15, 2025
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Summary

Wearable sensors can detect Mild Cognitive Impairment (MCI) by analyzing kitchen activities. This technology shows promise for early screening of cognitive decline in older adults.

Keywords:
Eye TrackingInstrumental Activities of Daily Living (IADL)Kitchen ActivityMild Cognitive ImpairmentWearables

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Area of Science:

  • Neuroscience
  • Gerontology
  • Human-Computer Interaction

Background:

  • Mild Cognitive Impairment (MCI) screening often relies on traditional methods, with limited research on behavioral markers in daily living settings.
  • Existing wearable studies for MCI primarily focus on gait analysis in controlled environments.
  • Kitchen activities, crucial for daily living, are known to be affected by visuospatial deficits common in MCI.

Purpose of the Study:

  • To investigate the use of wrist and eye-tracking wearable sensors for quantifying kitchen activities in individuals with MCI.
  • To explore the potential of multimodal sensing for differentiating individuals with MCI from those with normal cognition.
  • To identify behavioral markers associated with cognitive decline during a common daily task.

Main Methods:

  • Collected multimodal data using wrist and eye-tracking sensors from 19 older adults (11 with MCI, 8 with normal cognition) performing a yogurt bowl preparation task.
  • Developed a multimodal analysis model to classify participants based on their sensor data.
  • Performed feature importance analysis to understand which behavioral markers are most associated with MCI.

Main Results:

  • The multimodal analysis model achieved a 74% F1 score in classifying individuals with MCI from those with normal cognition.
  • Feature importance analysis revealed associations between weaker upper limb motor function and delayed eye movements with cognitive decline.
  • Findings align with previous research indicating motor and visual-behavioral changes in MCI.

Conclusions:

  • This pilot study demonstrates the feasibility of using wearable sensors to monitor behavioral markers of MCI during everyday kitchen activities.
  • The findings suggest that multimodal sensing can aid in the early detection of cognitive decline in real-world settings.
  • Further large-scale validation studies in home environments are warranted to confirm these promising results.