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IoT-Based Solution for Detecting and Monitoring Upper Crossed Syndrome.

Ammar Shaheen¹, Hisham Kazim¹, Mazen Eltawil¹

¹Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates.

Sensors (Basel, Switzerland)

|January 11, 2024

Summary

This summary is machine-generated.

A smart wearable back brace uses IMU sensors and an LSTM model to accurately detect Upper Crossed Syndrome (UCS), a common spinal issue from sedentary behavior. This technology aids in monitoring and preventing UCS, improving patient-doctor coordination and spinal health.

Keywords:

Upper Crossed Syndrome back brace corrective wearables hunchback inertial measurement unit machine learning

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Area of Science:

Biomedical Engineering
Health Informatics
Rehabilitation Technology

Background:

Sedentary lifestyles lead to prolonged sitting, increasing risks of spinal disorders like Upper Crossed Syndrome (UCS).
UCS is associated with thoracic kyphosis, potentially causing severe spinal curvature and related health complications.
Current detection methods rely on clinical assessments, necessitating improved automated monitoring solutions.

Purpose of the Study:

To develop and evaluate a multi-layered system for a smart wearable back brace to detect, monitor, and prevent signs of UCS.
To enhance patient-doctor interaction and coordination through automated detection and real-time feedback.
To improve patient spinal health management by providing timely interventions.

Main Methods:

Integration of Inertial Measurement Unit (IMU) sensors into a wearable back brace to capture postural data.
Utilizing machine learning classifiers, including a Long Short-Term Memory (LSTM) model, for postural classification.
Developing a mobile interface for real-time data visualization and user feedback.

Main Results:

The smart wearable brace effectively detects hunched-back postures indicative of UCS.
The Long Short-Term Memory (LSTM) model achieved a high classification accuracy of 99.3% for UCS detection.
The integrated system facilitates real-time monitoring and feedback for users.

Conclusions:

The proposed smart wearable back brace system offers an accurate and efficient method for detecting and monitoring Upper Crossed Syndrome.
Real-time data visualization and feedback empower users to correct posture and mitigate UCS-related issues.
This technology has the potential to significantly improve the management and prevention of UCS in individuals with sedentary lifestyles.