Measuring Clinically Relevant Knee Motions With A Self-Calibrated Wearable Sensor

Abstract

Low-cost sensors provide a unique opportunity to continuously monitor patient progress during rehabilitation; however, these sensors have yet to demonstrate the fidelity and lack the calibration paradigms necessary to be viable tools for clinical research. Therefore, the purpose of this study was to validate a low-cost wearable sensor that accurately measured peak knee extension during clinical exercises and needed no additional equipment for calibration. Knee flexion was quantified using a 9-axis motion sensor and directly compared to motion capture data. Peak extension values during seated knee extensions were accurate within 5 degrees across all subjects (RMS error: 2.6 degrees, P = 0.29) but less accurate during sit-to-stand exercises (RMS error: 16.6 degrees, P = 0.48). Knee flexion during gait strongly correlated (0.84 ≤ r_xy ≤ 0.99) with motion capture measurements but demonstrated average errors of 10 degrees. This study demonstrated a low-cost sensor that satisfied our criteria: a simple calibration procedure resulting in accurate measures of joint function during clinical exercises, making it a feasible tool for continuous patient monitoring to guide regenerative rehabilitation.