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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

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Published on: July 27, 2018

Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine

William Taylor¹, Kia Dashtipour¹, Syed Aziz Shah²

¹James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK.

Sensors (Basel, Switzerland)

|July 2, 2021

Summary

This study uses radar imaging and machine learning to detect falls in elderly individuals by analyzing daily activities. Convolutional Neural Networks achieved 95.30% accuracy, improving fall detection systems.

Keywords:

activity detection machine learning radar sensing wireless sensing

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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

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Published on: December 11, 2015

Area of Science:

Gerontology
Biomedical Engineering
Computer Science

Background:

Aging can lead to 'unstable incapacity' and a decline in independence in activities of daily living (ADLs).
Detecting ADLs is crucial for improving elderly home care and developing effective fall detection systems.

Purpose of the Study:

To present a novel fall detection system for the elderly using radar image classification.
To classify daily routine activities and identify fall events using machine learning techniques.

Main Methods:

Utilized radar data from 99 volunteers to classify six human activities: walking, sitting, standing, picking up objects, drinking water, and falls.
Applied various machine learning algorithms including Random Forest, KNN, SVM, LSTM, Bi-LSTM, and CNN.
Employed data processing techniques such as Principal Component Analysis (PCA) and data augmentation to optimize radar images.

Main Results:

The Convolutional Neural Network (CNN) algorithm, combined with PCA and data augmentation, achieved the highest accuracy of 95.30%.
PCA proved most beneficial when training data was augmented, enhancing model performance.
The proposed approach demonstrated superior accuracy compared to existing state-of-the-art methods.

Conclusions:

Radar image classification with advanced machine learning techniques offers a highly accurate method for elderly fall detection.
The integration of PCA and data augmentation significantly improves the performance of CNN-based fall detection systems.
This research contributes to advancing fall detection technology, enhancing safety and independence for the elderly.