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Design and Analysis for Fall Detection System Simplification

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Published on: April 6, 2020

A Computer Vision-Based System to Help Health Professionals to Apply Tests for Fall Risk Assessment.

Jesús Damián Blasco-García¹, Gabriel García-López², Marta Jiménez-Muñoz²

¹Clinical and Experimental Neuroscience (NiCE), Institute for Aging Research, Biomedical Institute for Bio-Health Research of Murcia (IMIB-Arrixaca), School of Medicine, University of Murcia, Campus Mare Nostrum, 30120 Murcia, Spain.

Sensors (Basel, Switzerland)

|March 28, 2024

Summary

This summary is machine-generated.

This study introduces an automated system using RGB-D cameras to evaluate gait and balance in older adults, aiding in fall prevention. The technology allows therapists to remotely assess fall risk, improving care for the growing elderly population.

Keywords:

RGB-D sensor automation balance digital transformation of health systems early diagnosis elderly fall risk gait telemedicine tests

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Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults

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Published on: February 8, 2019

Area of Science:

Gerontology
Biomedical Engineering
Rehabilitation Technology

Background:

Aging population presents challenges for healthcare and social care.
Falls are a significant risk for older adults, necessitating effective prevention strategies.
Traditional gait and balance assessments can be time-consuming and require therapist presence.

Purpose of the Study:

To develop and validate an automated system for evaluating gait and balance in older adults.
To provide a tool for objective, remote assessment of fall risk factors.
To support therapists in diagnosing and preventing falls in the elderly.

Main Methods:

Utilized an RGB-D camera to capture human motion and pose data.
Digitally represented key parameters relevant to established gait and balance tests.
Validated the system in a laboratory setting and a nursing home trial with residents.

Main Results:

Demonstrated the system's usefulness in objectively evaluating clinical test parameters.
Showcased ease of use for therapists, enabling remote assessment.
Validated the system's effectiveness with a small cohort of nursing home residents.

Conclusions:

The RGB-D camera-based system offers an effective tool for objective gait and balance assessment.
The technology facilitates remote fall risk evaluation, supporting preventative care for the elderly.
Paved the way for future advancements including cloud platforms, robotic integration, and AI-driven fall risk detection.