首页|New Findings from Florida Atlantic University in the Area of Machine Learning De scribed (Objective estimation of m-CTSIB balance test scores using wearable sens ors and machine learning)
New Findings from Florida Atlantic University in the Area of Machine Learning De scribed (Objective estimation of m-CTSIB balance test scores using wearable sens ors and machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Boca Raton, Fl orida, by NewsRx editors, research stated, “Accurate balance assessment is impor tant in healthcare for identifying and managing conditions affecting stability a nd coordination. It plays a key role in preventing falls, understanding movement disorders, and designing appropriate therapeutic interventions across various a ge groups and medical conditions.” Our news reporters obtained a quote from the research from Florida Atlantic Univ ersity: “However, traditional balance assessment methods often suffer from subje ctivity, lack of comprehensive balance assessments and remote assessment capabil ities, and reliance on specialized equipment and expert analysis. In response to these challenges, our study introduces an innovative approach for estimating sc ores 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 collecte d comprehensive movement data from 34 participants under four different sensory conditions using an array of inertial measurement unit (IMU) sensors coupled wit h a specialized system to evaluate ground truth m-CTSIB balance scores for our a nalysis. This data was then preprocessed, and an extensive array of features was extracted for analysis. To estimate the m-CTSIB scores, we applied Multiple Lin ear Regression (MLR), Support Vector Regression (SVR), and XGBOOST algorithms. O ur subject-wise Leave-One-Out and 5-Fold cross-validation analysis demonstrated high accuracy and a strong correlation with ground truth balance scores, validat ing 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.”
Florida Atlantic UniversityBoca RatonFloridaUnited StatesNorth and Central AmericaCyborgsEmerging Technologi esMachine Learning
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