Using a mobile range-camera motion capture system to evaluate the performance of integration of multiple low-cost wearable sensors and gait kinematics for pedestrian navigation in realistic environments

This paper presents a comparison of joint angle and step length estimation computed from multiple low-cost wearable sensors and optical systems. Seven wearable sensors are mounted on the pelvis, and the thigh, shank, and foot of both legs. Two types of experiments are conducted, a treadmill walk and straight line walk. The joint angles are estimated using a Kalman filtering method to fuse all sensor measurements. The step lengths are computed based on the estimated joint angles. The results show that the average errors of pitch angles and step lengths are 3 degrees and 6 cm, respectively, for treadmill walk.

[1]  Estefania Munoz Diaz,et al.  Step detector and step length estimator for an inertial pocket navigation system , 2014, 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[2]  Chan Gook Park,et al.  Use of Multiple Wearable Inertial Sensors in Human Localization , 2015 .

[3]  S. Godha,et al.  Foot mounted inertial system for pedestrian navigation , 2008 .

[4]  Valérie Renaudin,et al.  Step Length Estimation Using Handheld Inertial Sensors , 2012, Sensors.

[5]  Chan Gook Park,et al.  Kinematic Model-Based Pedestrian Dead Reckoning for Heading Correction and Lower Body Motion Tracking , 2015, Sensors.

[6]  Fernando Seco Granja,et al.  Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU , 2010, 2010 7th Workshop on Positioning, Navigation and Communication.

[7]  Susanna Kaiser,et al.  Performance comparison of foot- and pocket-mounted inertial navigation systems , 2016, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[8]  Chandra Tjhai,et al.  Step-size estimation using fusion of multiple wearable inertial sensors , 2017, 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[9]  D. Roetenberg,et al.  Xsens MVN: Full 6DOF Human Motion Tracking Using Miniature Inertial Sensors , 2009 .

[10]  Mark Newman,et al.  The Limits of In-Run Calibration of MEMS Inertial Sensors and Sensor Arrays , 2016 .

[11]  Sidney Pascal Kwakkel,et al.  Human Lower Limb Kinematics Using GPS/INS , 2009 .

[12]  Eric Foxlin,et al.  Pedestrian tracking with shoe-mounted inertial sensors , 2005, IEEE Computer Graphics and Applications.

[13]  Jeremy Steward.  Range Camera Motion Capture: Geometric Parameter Extraction from Human Motion Data in Point Clouds , 2017 .

[14]  Federica Pascucci,et al.  3D pedestrian dead reckoning and activity classification using waist-mounted inertial measurement unit , 2015, 2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[15]  Federica Pascucci,et al.  C-IPS: A smartphone based Indoor Positioning System , 2016, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[16]  Simina Emerich,et al.  Distance Measuring using Accelerometer and Gyroscope Sensors , 2012 .

[17]  Valérie Renaudin,et al.  Motion Mode Recognition and Step Detection Algorithms for Mobile Phone Users , 2013, Sensors.

[18]  Angelo M. Sabatini,et al.  Assessment of walking features from foot inertial sensing , 2005, IEEE Transactions on Biomedical Engineering.

[19]  Anshul Kumar,et al.  Strap-down Pedestrian Dead-Reckoning system , 2011, 2011 International Conference on Indoor Positioning and Indoor Navigation.