首页|University Hospital Basel Reports Findings in Machine Learning (Classification of inertial sensor-based gait patterns of orthopaedic conditions using machine learning: A pilot study)
University Hospital Basel Reports Findings in Machine Learning (Classification of inertial sensor-based gait patterns of orthopaedic conditions using machine learning: A pilot study)
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NETL
NSTL
Wiley
New research on Machine Learning is the subject of a report. According to news originating from Basel, Switzerland, by NewsRx correspondents, research stated, "Elderly patients often have more than one disease that affects walking behavior. An objective tool to identify which disease is the main cause of functional limitations may aid clinical decision making." Our news journalists obtained a quote from the research from University Hospital Basel, "Therefore, we investigated whether gait patterns could be used to identify degenerative diseases using machine learning. Data were extracted from a clinical database that included sagittal joint angles and spatiotemporal parameters measured using seven inertial sensors, and anthropometric data of patients with unilateral knee or hip osteoarthritis, lumbar or cervical spinal stenosis, and healthy controls. Various classification models were explored using the MATLAB Classification Learner app, and the optimizable Support Vector Machine was chosen as the best performing model. The accuracy of discrimination between healthy and pathologic gait was 82.3%, indicating that it is possible to distinguish pathological from healthy gait. The accuracy of discrimination between the different degenerative diseases was 51.4%, indicating the similarities in gait patterns between diseases need to be further explored."
BaselSwitzerlandEuropeCyborgsEmerging TechnologiesHealth and MedicineMachine LearningOrthopedics
NETL
NSTL
Wiley
