Pedestrian tracking with shoe-mounted inertial sensors

By E. Foxlin. IEEE Computer Graphics and Applications, 2005

1,451 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/0ef5d13873cb1acd354525f30dd20db421c71869

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Citations in the corpus

A Theoretical Foundation of Network Localization and Navigation (2018, background): “Those measurements provide the information about the state evolution over different instants and can significantly benefit localization through a filtering process [77]–[81].”
An introduction to inertial navigation (2007, background): “assumptions are exploited is NavShoe [6], in which a shoe mounted IMU is used to track the location of a pedestrian.”
Accurate Pedestrian Indoor Navigation by Tightly Coupling Foot-Mounted IMU and RFID Measurements (2012, methodology): “The IEZ+ method, using only the self-contained information of the IMU, has proven to be a very reliable PDR method, with accumulated errors of approximately 1% of the total traveled distance [12], [14].”
Practical motion capture in everyday surroundings (2007, background): “In addition, the motion of the root cannot be reliably recovered from inertial sensors alone, although in some cases this problem can be alleviated by detecting foot plants [Foxlin 2005].”
Pedestrian localisation for indoor environments (2008, background): “Accurate people tracking in a general environment using small and wearable inertial sensors has yet to be reliably shown, although previous attempts have been made [8, 9].”
3D gait assessment in young and elderly subjects using foot-worn inertial sensors. (2010, background): “, 2007) and/or GPS (Foxlin, 2005), but results remain essentially qualitative and were not validated for use in clinical field.”
A Survey of Indoor Inertial Positioning Systems for Pedestrians (2013, methodology): “A 15-state model is commonly used: three states each for position, velocity and attitude errors plus six states to model the accelerometer and gyroscope biases [27], [48].”
Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers (2010, background): “Moreover, recent developments concern the integration of triads of accelerometers and gyros with mutually orthogonal sensitive axes within three-dimensional strap-down inertial navigation systems that are proposed for applications in virtual reality, pedestrian navigation, robotics, and so forth [18”
Estimation of IMU and MARG orientation using a gradient descent algorithm (2011, methodology): “Translational position data was recovered using methods similar to [39], [40], [41], [42], [43].”
High-precision, consistent EKF-based visual-inertial odometry (2013, methodology): “…while several algorithms (especially in the GPSaided inertial navigation community) employ the simple approximation I I + F t that is equivalent to using one-step Euler integration (e.g. (Foxlin, 2005; Farrell, 2008; Zachariah and Jansson, 2010; Lupton and Sukkarieh, 2012; Vu et al., 2012)).”
Using Inertial Sensors for Position and Orientation Estimation (2017, neutral): “Including the knowledge that during standing, walking and running, the feet are stationary on the floor during certain periods of time solves this problem [40, 24].”
SmartPDR: Smartphone-Based Pedestrian Dead Reckoning for Indoor Localization (2015, methodology): “Strapdown usage of accelerometer has been studied to track users, and foot-mounted inertial sensors have been used to detect step event [24], [25].”
Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU (2010, methodology): “The PDR navigation state transition model is a non-linear function of the states, but it can be linearized around a state estimate [14], [11].”
Non-GPS Positioning Systems (2017, background): “For example, Zero-Velocity-Update (ZUPT) strategy achieves considerable reduction in error by placing inertial sensors on a foot of the user [31, 41].”