Abstract

This paper proposes a method for three dimensional gait analysis using wearable sensors and quaternion calculations. Seven sensor units consisting of a tri-axial acceleration and gyro sensors, were fixed to the lower limbs. The acceleration and angular velocity data of each sensor unit were measured during level walking. The initial orientations of the sensor units were estimated using acceleration data during upright standing position and the angular displacements were estimated afterwards using angular velocity data during gait. Here, an algorithm based on quaternion calculation was implemented for orientation estimation of the sensor units. The orientations of the sensor units were converted to the orientations of the body segments by a rotation matrix obtained from a calibration trial. Body segment orientations were then used for constructing a three dimensional wire frame animation of the volunteers during the gait. Gait analysis was conducted on five volunteers, and results were compared with those from a camera-based motion analysis system. Comparisons were made for the joint trajectory in the horizontal and sagittal plane. The average RMSE and correlation coefficient (CC) were 10.14 deg and 0.98, 7.88 deg and 0.97, 9.75 deg and 0.78 for the hip, knee and ankle flexion angles, respectively.

Highlights

  • Gait analysis is commonly done with an optical tracking system such as the Vicon motion analysis system (Vicon Motion Systems, Inc., Los Angeles, CA, US)

  • High CC was observed for all volunteers, there was variation in the root mean square error (RMSE)

  • The variation in the RMSE is believed to be caused by inaccuracies in calculating the lower limb measurements from camera images

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Summary

Introduction

Gait analysis is commonly done with an optical tracking system such as the Vicon motion analysis system (Vicon Motion Systems, Inc., Los Angeles, CA, US). An alternative is to use acceleration or angular velocity data measured from small inertial sensors attached directly to the body [1,2,3,4] This method has the advantage of identifying human motion in a wide variety of environments. This method does not directly measure position, only acceleration or angular velocity data of body segments they are attached to, a major challenge is to translate these data into meaningful three dimensional positional data, such as the joint angles of the lower limbs during gait. This can be used for evaluating differences in the gait; for example patients with ACL related injuries [5]

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