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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

Utilizing Machine Learning to Recognize Human Activities for Elderly and Homecare.

Razan Alaraj¹, Riyad Alshammari²

¹Health Informatics Department, College of Public Health and Health Informatics, King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS), King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia.

Acta Informatica Medica : AIM : Journal of the Society for Medical Informatics of Bosnia & Herzegovina : Casopis Drustva Za Medicinsku Informatiku Bih

|January 8, 2021

Summary

Machine learning algorithms accurately classify human activities using smartphone data, offering a promising solution for dementia patient monitoring and homecare support. This technology aids caregivers in tracking daily living activities.

Keywords:

BigML Dementia HAR Machine Learning Smartphone

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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

Area of Science:

Gerontology
Computer Science
Biomedical Engineering

Background:

Dementia is a progressive age-related cognitive disorder with no cure, impacting daily task performance.
Global dementia prevalence is projected to reach 152 million by 2050, highlighting the need for supportive care solutions.
Technological advancements, particularly smartphone applications, offer novel ways to monitor dementia patients' activities for homecare.

Purpose of the Study:

To evaluate the accuracy of machine learning algorithms in classifying human activities using smartphone-derived sensory data.
To compare the performance of three classification algorithms for activity recognition in the context of dementia care.

Main Methods:

Utilized a labeled public dataset for human activity classification into six distinct categories.
Employed the BigML platform to construct and train machine learning models.
Applied activity recognition algorithms, including ridged regression and deep neural networks.

Main Results:

Machine learning models demonstrated high accuracy in classifying human activities.
Activity recognition algorithms achieved over 98% accuracy for most activities.
Both ridged regression and deep neural networks proved effective for accurate activity classification.

Conclusions:

Smartphone applications can effectively monitor dementia patients' daily activities, enhancing homecare services.
This technology empowers caregivers by providing a reliable method for patient activity tracking.
Patients only need to carry a smartphone, simplifying the monitoring process for both patients and caregivers.