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Smart Cushions with Machine Learning-Enhanced Force Sensors for Pressure Injury Risk Assessment.

Xinhao Xiang¹, Ke Zhang^1,2, Yi Qin³

¹Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai 201619, China.

ACS Applied Materials & Interfaces

|July 12, 2024

Summary

This summary is machine-generated.

A new smart cushion uses two-dimensional force sensors and machine learning to detect pressure injury risk in individuals with spinal cord injuries. This technology helps predict shear stress, improving patient care and reducing healthcare costs.

Keywords:

ferroelectret machine learning algorithm shear strain smart cushion two-dimensional force sensor

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Area of Science:

Biomedical Engineering
Rehabilitation Technology
Sensor Technology

Background:

Prolonged sitting poses a significant pressure injury (PI) risk for individuals with stroke or spinal cord injuries.
Current methods for assessing PI risk by measuring contact surface pressure and shear force are limited.

Purpose of the Study:

To develop a smart cushion system for real-time monitoring of pressure and shear forces in the buttocks.
To utilize machine learning for predicting gluteal muscle shear stresses and assessing PI risk.

Main Methods:

Integration of two-dimensional force sensors (2D-FSs), composed of ferroelectret coaxial (FCS) and ferroelectret film (FFS) units, to measure vertical and horizontal forces.
Application of deep neural networks for decoupling and analyzing sensor data.
Development of a genetic algorithm-optimized backpropagation neural network for predicting shear strain.

Main Results:

The smart cushion accurately measures simultaneous vertical and horizontal forces using the novel 2D-FS design.
The machine learning model effectively predicts shear strain in the buttocks, indicating PI risk.
The system demonstrates potential for integration with intelligent care platforms.

Conclusions:

The proposed smart cushion offers a novel approach to monitor and mitigate pressure injury risk.
This technology can enhance patient care, particularly for individuals with spinal cord injuries.
Potential for reducing nursing and rehabilitation costs through early risk detection.