Traditional methods to assess balance often suffer from subjectivity, aren't comprehensive enough and can't be administered remotely. They also are expensive and require specialized equipment and clinical expertise. Using wearable sensors and advanced machine learning algorithms, researchers offer a practical and cost-effective solution for capturing detailed movement data, essential for balance analysis.
Using wearable sensors and advanced machine learning algorithms, researchers from Florida Atlantic University's College of Engineering and Computer Science have developed a novel approach that addresses a crucial gap in balance assessment and sets a new benchmark in the application of wearable technology and machine learning in health care.
The data was then preprocessed and an extensive array of features was extracted for analysis. To estimate the m-CTSIB scores, researchers applied Multiple Linear Regression, Support Vector Regression and XGBOOST algorithms. The wearable sensor data served as the input for their machine-learning models, and the corresponding m-CTSIB scores from Falltrak II, one of the leading tools in fall prevention, acted as the ground truth labels for model training and validation.
Results provide important insights into the significance of specific movements, feature selection and sensor placement in estimating balance. Notably, the XGBOOST model, utilizing the lumbar sensor data, achieved outstanding results in both cross-validation methods and demonstrated a high correlation and a low mean absolute error, indicating consistent performance.
"Traditional balance assessments often lack the granularity to dissect these influences comprehensively, leading to a gap in our understanding and management of balance impairments," said Ghoraani."Moreover, wearables support remote monitoring, enabling health care professionals to evaluate patients' balance remotely, which is particularly useful in diverse health care scenarios.