Sensor Fusion in Human Cyber Sensor System for Motion Artifact Removal from NIRS Signal

Abstract

Near-Infrared Spectroscopy (NIRS) signals have been widely used to monitor hemodynamic changes in clinical and psychological investigations as well as human system interfacing such as brain computer interfacing (BCI) for gait and rehabilitation. However, the estimation of hemodynamic changes might be blurred due to the presence of motion artifacts in a moving human in the loop system, which should be removed for a more accurate estimation. To register the motion information more accurately, a wearable wireless NIRS cyber sensor system was developed capable of registering motion-related signals from a multisensory integrated Inertia Measurement Unit (IMU) placed close to the NIR optical sensor. Although multi-axis accelerometer, gyroscope and magnetometer signals that are highly correlated to the motion at the optical sensor may provide a good estimation of the motion artifacts in the NIRS signal, the motion fusion algorithms might provide more accurate estimation of motion artefacts in the NIR signal by overcoming the intrinsic limitations of individual sensors such as imprecision and drifts. This study was purposed to determine whether the combination of motion fusion algorithm-based signal and individual sensor readings from IMU could provide a more accurate correction of the motion artifacts in the NIRS signal. The results revealed that the signal-to-noise ratio (SNR) increased significantly when motion fusion signals were used in the estimation and removal of the motion artifacts. The results suggest that the motion fusion algorithm can provide a more accurate estimation and removal of motion artifacts and thus, supporting a better detection of hemodynamic changes.