A Flexible Sensor and MIMU-Based Multisensor Wearable System for Human Motion Analysis

Motivation: Magnetic-inertial measurement units (MIMUs) and flexible sensors are widely used in the wearable measurement system for human motion monitoring, clinical gait detection, and robotics motion control. However, MIMUs demonstrate measurement error due to magnetic disturbance in the indoor environment, and flexible sensors usually have low performance on linearity and accuracy. Objective: This article is intended to eliminate the low-accuracy problem caused by magnetic disturbances and improve the measurement accuracy of MIMU-flexiblesensor-based wearable systems. Approach: 1) a three-stage real-time adaptive anti-disturbance data fusion (RT-ADF) algorithm is proposed, which contains an anti-disturbance filter based on a double Mahony filter along with a state observer, a signal holder for sensors' data transmit synchronously, and a data fusion based on an adaptive Kalman filter; 2) the proposed algorithm is used and validated its performance on a designed MIMU-flexible sensor wearable system; and 3) ten groups of knee motions (flexion/extension), ten groups of hip motions (adduction/abduction), and ten groups of elbow motions (flexion/extension) have been done by seven subjects in the experiments. Main Results: The designed multisensor wearable system based on the presented data fusion algorithm demonstrates a high-accuracy performance under the magnetic disturbance environment, and the maximum root mean square error (RMSE) of the measured continuous 3-D motion angle of the knee, hip, and elbow cross all the experiments was 1.23 degrees, 1.15 degrees, and 3.67 degrees for each axis.