Gait posture estimation using wearable acceleration and gyro sensors

Abstract

A method for gait analysis using wearable acceleration sensors and gyro sensors is proposed in this work. The volunteers wore sensor units that included a tri-axis acceleration sensor and three single axis gyro sensors. The angular velocity data measured by the gyro sensors were used to estimate the translational acceleration in the gait analysis. The translational acceleration was then subtracted from the acceleration sensor measurements to obtain the gravitational acceleration, giving the orientation of the lower limb segments. Segment orientation along with body measurements were used to obtain the positions of hip, knee, and ankle joints to create stick figure models of the volunteers. This method can measure the three dimensional positions of joint centers of the hip, knee, and ankle during movement. Experiments were carried out on the normal gait of three healthy volunteers. As a result, the flexion-extension (F-E) and the adduction-abduction (A-A) joint angles of the hips and the flexion-extension (F-E) joint angles of the knees were calculated and compared with a camera motion capture system. The correlation coefficients were above 0.88 for the hip F-E, higher than 0.721 for the hip A-A, better than 0.924 for the knee F-E. A moving stick figure model of each volunteer was created to visually confirm the walking posture. Further, the knee and ankle joint trajectories in the horizontal plane showed that the left and right legs were

Kalman-Filtering-Based Joint Angle Measurement with Wireless Wearable Sensor System for Simplified Gait Analysis

“…The Kalman filtering based measurement method stably reduced joint angle error and increased correlation coefficient almost achieving the target values, even if walking speeds or subjects were different. In the previous studies that did not require any special equipments for calibration and time-consuming set up process, the RMSE was 6.42 deg and the correlation coefficient was 0.93 for knee joint angle measurement [1], mean RMSE values were 8.72 deg and 6.79 deg and mean correlation coefficients were 0.88 and 0.92 for hip flexion/extension angle and knee joint angle, respectively [7]. It is considered that the developed system in this study achieved good accuracy considering practical application as a simple gait analysis system.…”

“…Therefore, just attaching sensors such as the method of this study is a preferable preparation for the measurement. Considering the practical sensor attachment method, RMSE smaller than 5 deg and correlation coefficient larger than 0.95 were targeted in this study based on previous works [1], [3], [6], [7]. The Kalman filtering based measurement method stably reduced joint angle error and increased correlation coefficient almost achieving the target values, even if walking speeds or subjects were different.…”

A Validation Test of Measurement Method of Lower Limb Angles Based on Kalman Filter on Different Type of Inertial Sensors

“…From these results, the movement acceleration is considered to be the cause of the error increase as target angle range increases. In this paper, parameter value to determine the gain of the Kalman filter was fixed for all sensors ( 5 10 under the static condition, and 7 10 under the dynamic condition) in analyzing the measurement data. Basically, it is better to correct measured angles with gyroscope more strongly by acceleration signal under the static condition than those under the dynamic condition in the Kalman filtering based method.…”

“…Accelerometer outputs were filtered with low-pass filter with cut off frequency of 20Hz. From the results of preliminary experiments, the parameter value of the Kalman filter under the static and dynamic conditions were determined to 5 10 , respectively. For evaluating the accuracy in angle measurement of the sensors, root mean squared error (RMSE) and correlation coefficient between measured angles and reference values were calculated.…”

Wearable Inertial Sensors and Their Applications

“…Using combined signals from accelerometers, gyroscopes, and magnetometers, the Kalman-based fusion algorithm has been applied to obtain dynamic orientations and positions of human body segments [8À10]. Furthermore, use of segment orientations have been proposed to visualize 3D gait from accelerometer and gyroscopic measurements in a global coordinate system [11]. In addition, a Gaussian particle filter has been used with wearable inertial sensors to evaluate the maximum angle of a walking cycle [12].…”