Sleeve for Knee Angle Monitoring: An IMU-POF Sensor Fusion System

• Published in: IEEE journal of biomedical and health informatics Vol. 25; no. 2; pp. 465 - 474
• Main Authors: Vargas-Valencia, Laura Susana, Schneider, Felipe B. A., Leal-Junior, Arnaldo G., Caicedo-Rodriguez, Pablo, Sierra-Arevalo, Wilson A., Rodriguez-Cheu, Luis E., Bastos-Filho, Teodiano, Frizera-Neto, Anselmo
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerometers; Biomedical monitoring; Clinical trials; Data integration; Extended Kalman filter; Gait; Inertial platforms; Inertial sensors; joint angles; Kalman filters; Knee; Magnetometers; Mathematical analysis; Monitoring; Motion capture; motion estimation; Multisensor fusion; Optical fibers; POF sensors; Polymers; Quaternions; Rehabilitation; Reproducibility; sensor fusion; Sensors; Walking; wearable systems
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2020.2988360

The knee flexion-extension angle is an important variable to be monitored in various clinical scenarios, for example, during physical rehabilitation assessment. The purpose of this work is to develop and validate a sensor fusion system based on a knee sleeve for monitoring of physical therapy. The system consists of merging data from two inertial measurement units (IMUs) and an intensityvariation based Polymer Optical Fiber (POF) curvature sensor using a quaternion-based Multiplicative Extended Kalman Filter (MEKF). The proposed data fusion method is magnetometer-free and deals with sensors' uncertainties through reliability intervals defined during gait. Walking trials were performed by twelve healthy participants using our knee sleeve system and results were validated against a gold standard motion capture system. Additionally, a comparison with other three knee angle estimation methods, which are exclusively based on IMUs, was carried out. The proposed system presented better performance (mean RMSE <; 3.3°, LFM coefficients, α 1 = 0.99 ± 0.04, α 0 = 0.70 ± 2.29, R 2 = 0.98 ± 0.01 and ρC > 0.99) when compared to the other evaluated methods. Experimental results demonstrate the usability and feasibility of our system to estimate knee motion with high accuracy, repeatability, and reproducibility. This wearable system may be suitable for motion assessment in rehabilitation labs in future studies.