Drift Removal for Improving the Accuracy of Gait Parameters Using Wearable Sensor Systems

Abstract

Accumulated signal noise will cause the integrated values to drift from the true value when measuring orientation angles of wearable sensors. This work proposes a novel method to reduce the effect of this drift to accurately measure human gait using wearable sensors. Firstly, an infinite impulse response (IIR) digital 4th order Butterworth filter was implemented to remove the noise from the raw gyro sensor data. Secondly, the mode value of the static state gyro sensor data was subtracted from the measured data to remove offset values. Thirdly, a robust double derivative and integration method was introduced to remove any remaining drift error from the data. Lastly, sensor attachment errors were minimized by establishing the gravitational acceleration vector from the acceleration data at standing upright and sitting posture. These improvements proposed allowed for removing the drift effect, and showed an average of 2.1°, 33.3°, 15.6° difference for the hip knee and ankle joint flexion/extension angle, when compared to without implementation. Kinematic and spatio-temporal gait parameters were also calculated from the heel-contact and toe-off timing of the foot. The data provided in this work showed potential of using wearable sensors in clinical evaluation of patients with gait-related diseases.