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Related Experiment Video

Updated: Nov 29, 2025

Multi-Modal Home Sleep Monitoring in Older Adults

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Apathy Classification Based on Doppler Radar Image for the Elderly Person.

Naoto Nojiri¹, Zelin Meng², Kenshi Saho³

¹College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan.

Frontiers in Bioengineering and Biotechnology

|November 23, 2020

Summary

Doppler radar imaging shows promise for classifying apathy in the elderly. This non-invasive technique, analyzing walking patterns, achieved over 75% accuracy in initial studies, offering a convenient diagnostic alternative.

Keywords:

apathy classification deep learning doppler radar image machine learning the elderly person

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Published on: February 8, 2019

Area of Science:

Gerontology
Medical Imaging
Machine Learning

Background:

Apathy is a prevalent condition in the elderly, characterized by reduced motivation.
Current clinical diagnosis of apathy is inconvenient for elderly patients.
Non-invasive, privacy-preserving diagnostic methods are needed.

Purpose of the Study:

To investigate the feasibility of using Doppler radar imaging for apathy classification in older adults.
To develop and evaluate machine learning models for this classification task.

Main Methods:

178 elderly participants completed questionnaires and underwent Doppler radar imaging during walking.
Walking data was pre-processed by dividing images into sections and counting feature points after binarization.
Seven machine learning models, including a proposed neural network, were trained and tested for apathy classification.

Main Results:

The proposed neural network model achieved over 75% accuracy in classifying apathy.
Feature extraction involved image binarization and counting feature points in segmented image quadrants.
Initial results demonstrate the potential of Doppler radar for elderly apathy assessment.

Conclusions:

Doppler radar imaging offers a privacy-preserving method for assessing apathy in the elderly.
The study highlights the potential of machine learning applied to radar imaging for geriatric health monitoring.
Further research is needed to improve classification accuracy.