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Activity recognition using correlated pattern mining for people with dementia.

Kelvin Sim1, Clifton Phua, Ghim-Eng Yap

  • 1Institute for Infocomm Research, A*STAR, Singapore. shsim@i2r.a-star.edu.sg

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This study introduces correlated patterns for recognizing activities in dementia patients, improving monitoring accuracy. This method enhances the ability to support elderly individuals living independently at home.

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Area of Science:

  • Gerontology
  • Computer Science
  • Artificial Intelligence

Background:

  • Global aging populations are increasing the prevalence of senile dementia.
  • Monitoring elderly dementia patients' Activities of Daily Living (ADLs) is crucial for independent living.
  • Home-based sensors generate contextual data for activity recognition.

Purpose of the Study:

  • To improve activity recognition accuracy for dementia patient monitoring.
  • To propose correlated patterns as a superior alternative to frequent patterns for activity representation.

Main Methods:

  • Utilized sensor data from a smart home testbed.
  • Developed and applied correlated pattern mining techniques.
  • Compared correlated patterns against traditional frequent patterns for activity recognition.

Main Results:

  • Correlated patterns demonstrated superior performance in activity recognition.
  • An average improvement of 35.5% in recognition accuracy was observed when using correlated patterns.
  • Frequent patterns were found to be less effective representations of patient activities.

Conclusions:

  • Correlated patterns offer a more effective approach for recognizing activities in smart home environments for dementia care.
  • The proposed method enhances the reliability of sensor-based monitoring systems for the elderly.
  • This advancement can significantly aid in assisting dementia patients with ADLs, promoting independence.