Balancing Acts: New Wearable Sensors and AI Transform Balance Assessment

Balance can be affected by a variety of factors, including diseases such as Parkinson's disease, acute and chronic injuries to the nervous system, and the natural aging process. Accurately assessing a patient's balance is important to identify and manage conditions that affect coordination and stability. Balance assessments also play a key role in preventing falls, understanding movement disorders, and designing appropriate therapeutic interventions for different age groups and medical conditions.

However, traditional methods used to assess balance are often subjective, not comprehensive enough, and cannot be performed remotely. Furthermore, these assessments rely on expensive and specialized equipment that is not readily available in all clinical settings and depend on clinician expertise, which can lead to variable results. There is a strong need for more objective and comprehensive assessment tools for balance assessment.

Researchers from Florida Atlantic University's College of Engineering and Computer Science have developed a novel approach using wearable sensors and advanced machine learning algorithms to address a critical gap in balance assessment and establish a new benchmark in the application of wearable technology and machine learning in healthcare. This approach represents a major advancement in objective balance assessment, particularly for remote monitoring in home or care settings, and has the potential to transform the management of balance disorders.

In the study, the researchers used the Modified Clinical Test of Sensory Interaction of Balance (m-CTSIB), a test widely used in healthcare to assess the ability to maintain balance under different sensory conditions. Wearable sensors were attached to the study participants' ankles, lumbar spine (lower back), sternum, wrists, and arms.

The researchers collected comprehensive motion data from participants across four different sensory conditions of the m-CTSIB: eyes-open and eyes-closed balance performance on a stable surface, and eyes-open and eyes-closed on a foam surface. Each test condition lasted uninterrupted for approximately 11 seconds to simulate a continuous balance challenge and streamline the evaluation process. The researchers combined inertial measurement unit (IMU) sensors with specialized systems to assess ground truth m-CTSIB balance scores for analysis.

The data was then preprocessed and extensive features were extracted for analysis. To estimate the m-CTSIB score, the researchers applied multiple linear regression, support vector regression, and XGBOOST algorithms. The wearable sensor data served as input for the machine learning models, and the corresponding m-CTSIB scores from Falltrak II, one of the primary tools for fall prevention, served as ground truth labels for training and validating the models. Multiple machine learning models were then developed to estimate the m-CTSIB score from the wearable sensor data. The researchers also investigated the most effective sensor placement to optimize balance analysis.

The study results, published in the journal Frontiers in Digital Health, highlight the high accuracy of the approach and its strong correlation with actual balance scores, demonstrating that the method is effective and reliable for estimating balance. Data from the lumbar and dominant ankle sensors performed best in estimating balance scores, highlighting the importance of strategic sensor placement to capture relevant balance coordination and movements.

“Wearable sensors offer a practical and cost-effective solution to obtain detailed motion data essential for balance analysis,” said senior author Behnaz Ghoraani, PhD, associate professor in the FAU Department of Electrical Engineering and Computer Science, co-director of the FAU SMART Health Center and FAU Institute for Sensing and Embedded Networked Systems Engineering (I-SENSE) fellow. “Placed on areas such as the hips and lower extremities, these sensors provide insight into 3D motion dynamics that are essential for applications such as fall risk assessment in different populations. Coupled with advances in machine learning, datasets derived from these sensors can be translated into objective, quantifiable balance metrics using a variety of machine learning techniques.”

The results provide important insights into the importance of specific movements, feature selection, and sensor placement in estimating balance. In particular, the XGBOOST model utilizing lumbar sensor data achieves excellent results with both cross-validation methods, exhibiting high correlation and low mean absolute error, demonstrating consistent performance.

“The findings of this important study suggest that this new method has the potential to revolutionize the practice of balance assessment, especially in settings where traditional methods are impractical or inaccessible,” said Stella Batalama, PhD, dean of FAU's School of Engineering and Computer Science. “This approach is more accessible, cost-effective and can be administered remotely, which could have a significant impact on healthcare, rehabilitation, sports science or other fields where balance assessment is important.”

The aim of this study was born out of a recognition of the need for advanced tools to capture the subtle effects that different sensory inputs have on balance.

“Traditional balance assessments often lack the precision to comprehensively analyze these effects, creating gaps in the understanding and management of balance disorders,” Golani said. “Furthermore, wearables support remote monitoring, allowing healthcare professionals to assess a patient's balance remotely, which is particularly useful in a variety of healthcare scenarios.”

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