Assessing gait activities and balance control with the use of wearable sensors and deep learning model

Abstract

ABSTRACT

This dissertation investigates the application of inertial measurement units (IMUs) for assessing gait balance control and explores the integration of IMUs and machine learning to classify gait activities. Wearable sensor technology, particularly IMUs, has gained attention due to its portability, cost-effectiveness, and ability to capture precise motion data in real-world settings. With the growing need to prevent falls and maintain mobility in aging populations, the role of IMUs in gait analysis is promising. The research presented in this dissertation explores several aspects of IMU-based gait assessment, comparing IMU performance with traditional motion capture systems, applying machine learning models for gait activity classification and recognition, and improving conventional clinical tests to better evaluate fall risk.

A narrative review sets the foundation by summarizing existing work on using IMUs to assess balance control. The dissertation continues with experimental studies comparing IMU data to gold-standard methods, applying deep learning to advanced gait data, and enhancing the Timed-Up-and-Go (TUG) test using IMU data. These studies demonstrate the potential of IMUs in quantifying balance control and classifying gait tasks with a deep learning model.

The results highlight the strong correlations between IMU-based and traditional measurements of biomechanical data, suggesting that advanced machine learning models can recognize various gait tasks and can segment the subtask to reveal the potential issue underlying the inferior performance. The findings emphasize the benefits of wearable sensors in both clinical and everyday environments and emphasize the need for further research to address the accuracy and usability.

The dissertation concludes with recommendations for future research, including exploring the integration of artificial intelligence, digital health platforms, and smart home technologies with IMUs to enable continuous, real-time monitoring of balance control and mobility. These advancements could lead to more effective interventions and prevention strategies for individuals at risk of falls.