Gait characterization for osteoarthritis patients using wearable gait sensors (H-Gait systems).

The objective of this work was to investigate the possibilities of using the wearable sensors-based H-Gait system in an actual clinical trial and proposes new gait parameters for characterizing OA gait. Seven H-Gait sensors, consisting of tri-axial inertial sensors, were attached to seven lower limb body segments (pelvis, both thighs, both shanks and both feet). The acceleration and angular velocity data measured were used to estimate three-dimensional kinematic parameters of patients during level walking. Three new parameters were proposed to assess the severity of OA based on the characteristics of these joint center trajectories in addition to conventional gait spatio-temporal parameters. The experiment was conducted on ten subjects with knee OA. The kinematic results obtained (hip, knee and ankle joint angles, joint trajectory in the horizontal and sagittal planes) were compared with those from a reference healthy (control) group. As a result, the angle between the right and left knee trajectories along with that of the ankle joint trajectories were almost twice as large (21.3° vs. 11.6° and 14.9° vs. 7.8°) compared to those of the healthy subjects. In conclusion, it was found that the ankle joints during stance abduct less to avoid adduction at the knee as the severity of OA increases and lead to more acute angles (less parallel) between the right and left knee/ankle joints in the horizontal plane. This method was capable to provide quantitative information about the gait of OA patients and has the advantage to allow for out-of-laboratory monitoring.

Drift removal for improving the accuracy of gait parameters using wearable sensor systems.

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.