Browsing by Author "Tjhai, Chandra"
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- ItemOpen AccessIntegration of Multiple Low-Cost Wearable Inertial/Magnetic Sensors and Kinematics of Lower Limbs for Improving Pedestrian Navigation Systems(2019-07-25) Tjhai, Chandra; O'Keefe, Kyle; El-Sheimy, Naser M; Ferber, Reed; Detchev, Ivan Denislavov; Wieser, AndreasThis thesis presents a work in pedestrian navigation that utilizes multiple low-cost wearable inertial/magnetic sensors and kinematics of lower limbs for improving the positioning performance. A multi-wearable sensor system is developed for this research in order to record the walking motion using multiple MPU-6050 and MPU-9250 sensors. The focus of this research is to investigate the feasibility of using multiple low-cost sensors distributed on lower limb segments as a pedestrian navigation system. The proposed method uses seven wearable sensor modules distributed on pelvis, thighs and shanks. A skeletal model consists of five limb segments is used to model the forward kinematics of lower limbs. Different attitude estimator algorithms are tested and compared. The step size and heading are computed using the forward kinematics. To evaluate the proposed navigation method, two experiments are conducted. The first experiment is a treadmill walk to evaluate the accuracy of the estimated segment orientation angles and step sizes. The second experiment involves turning motion where a test subject walks around a rectangular path. The results show that the use of a wearable multi-sensor system can provide a pedestrian navigation solution with error comparable to the solution computed using a single higher-cost sensor.
- ItemOpen AccessUsing Step Size and Lower Limb Segment Orientation from Multiple Low-Cost Wearable Inertial/Magnetic Sensors for Pedestrian Navigation(2019-07-17) O'Keefe, Kyle P. G.; Tjhai, ChandraThis paper demonstrates the use of multiple low-cost inertial/magnetic sensors as a pedestrian navigation system for indoor positioning. This research looks at the problem of pedestrian navigation in a practical manner by investigating dead-reckoning methods using low-cost sensors. This work uses the estimated sensor orientation angles to compute the step size from the kinematics of a skeletal model. The orientations of limbs are represented by the tilt angles estimated from the inertial measurements, especially the pitch angle. In addition, different step size estimation methods are compared. A sensor data logging system is developed in order to record all motion data from every limb segment using a single platform and similar types of sensors. A skeletal model of five segments is chosen to model the forward kinematics of the lower limbs. A treadmill walk experiment with an optical motion capture system is conducted for algorithm evaluation. The mean error of the estimated orientation angles of the limbs is less than 6 degrees. The results show that the step length mean error is 3.2 cm, the left stride length mean error is 12.5 cm, and the right stride length mean error is 9 cm. The expected positioning error is less than 5% of the total distance travelled.