Relating Self-Reported Balance Problems to Sensory Organization and Dual-Tasking in Chronic Traumatic Brain Injury

Objective estimation of m-CTSIB balance test scores using wearable sensors and machine learning

Accurate balance assessment is important in healthcare for identifying and managing conditions affecting stability and coordination. It plays a key role in preventing falls, understanding movement disorders, and designing appropriate therapeutic interventions across various age groups and medical conditions. However, traditional balance assessment methods often suffer from subjectivity, lack of comprehensive balance assessments and remote assessment capabilities, and reliance on specialized equipment and expert analysis. In response to these challenges, our study introduces an innovative approach for estimating scores on the Modified Clinical Test of Sensory Interaction on Balance (m-CTSIB). Utilizing wearable sensors and advanced machine learning algorithms, we offer an objective, accessible, and efficient method for balance assessment. We collected comprehensive movement data from 34 participants under four different sensory conditions using an array of inertial measurement unit (IMU) sensors coupled with a specialized system to evaluate ground truth m-CTSIB balance scores for our analysis. This data was then preprocessed, and an extensive array of features was extracted for analysis. To estimate the m-CTSIB scores, we applied Multiple Linear Regression (MLR), Support Vector Regression (SVR), and XGBOOST algorithms. Our subject-wise Leave-One-Out and 5-Fold cross-validation analysis demonstrated high accuracy and a strong correlation with ground truth balance scores, validating the effectiveness and reliability of our approach. Key insights were gained regarding the significance of specific movements, feature selection, and sensor placement in balance estimation. Notably, the XGBOOST model, utilizing the lumbar sensor data, achieved outstanding results in both methods, with Leave-One-Out cross-validation showing a correlation of 0.96 and a Mean Absolute Error (MAE) of 0.23 and 5-fold cross-validation showing comparable results with a correlation of 0.92 and an MAE of 0.23, confirming the model's consistent performance. This finding underlines the potential of our method to revolutionize balance assessment practices, particularly in settings where traditional methods are impractical or inaccessible.

Reliability and Minimal Detectable Change for a Smartphone-Based Motor-Cognitive Assessment: Implications for Concussion Management

Our purpose was to investigate the reliability and minimal detectable change characteristics of a smartphone-based assessment of single- and dual-task gait and cognitive performance. Uninjured adolescent athletes (n = 17; mean age = 16.6, SD = 1.3 y; 47% female) completed assessments initially and again 4 weeks later. The authors collected data via an automated smartphone-based application while participants completed a series of tasks under (1) single-task cognitive, (2) single-task gait, and (3) dual-task cognitive-gait conditions. The cognitive task was a series of continuous auditory Stroop cues. Average gait speed was consistent between testing sessions in single-task (0.98, SD = 0.21 vs 0.96, SD = 0.19 m/s; P = .60; r = .89) and dual-task (0.92, SD = 0.22 vs 0.89, SD = 0.22 m/s; P = .37; r = .88) conditions. Response accuracy was moderately consistent between assessments in single-task standing (82.3% accurate, SD = 17.9% vs 84.6% accurate, SD = 20.1%; P = .64; r = .52) and dual-task gait (89.4% accurate, SD = 15.9% vs 85.8% accurate, SD = 20.2%; P = .23; r = .81) conditions. Our results indicate automated motor-cognitive dual-task outcomes obtained within a smartphone-based assessment are consistent across a 1-month period. Further research is required to understand how this assessment performs in the setting of sport-related concussion. Given the relative reliability of values obtained, a smartphone-based evaluation may be considered for use to evaluate changes across time among adolescents, postconcussion.