Foot-mounted Inertial Navigation System Research Articles

Data Fusion of Dual Foot-Mounted IMU for Pedestrian Navigation

Foot-mounted inertial navigation systems are suitable as the core pedestrian navigation system in GNSS denied environment for some applications (e.g., soldier and first responder positioning). However, to date the exiting zero velocity update-based algorithm still possesses some drawbacks including systematic heading drift. A beneficial strategy is to fuse the information of two foot-mounted systems to achieve more robust and reliable performance. In this paper, a novel equality constraint method based on pedestrian footstep mode is proposed to fuse the information of two systems in the absence of any additional ranging equipment. Real field experiments demonstrate that the proposed algorithm improves the performances of both two systems.

Zero-Velocity Detection—A Bayesian Approach to Adaptive Thresholding

A Bayesian zero-velocity detector for foot-mounted inertial navigation systems is presented. The detector extends existing zero-velocity detectors based on the likelihood-ratio test and allows, possibly time-dependent, prior information about the two hypotheses—the sensors being stationary or in motion—to be incorporated into the test. It is also possible to incorporate information about the cost of a missed detection or a false alarm. Specifically, we consider a hypothesis prior based on the velocity estimates provided by the navigation system and an exponential model for how the cost of a missed detection increases with the time since the last zero-velocity update. Thereby, we obtain a detection threshold that adapts to the motion characteristics of the user. Thus, the proposed detection framework efficiently solves one of the key challenges in current zero-velocity-aided inertial navigation systems: the tuning of the zero-velocity detection threshold. A performance evaluation on data with normal and fast gait demonstrates that the proposed detection framework outperforms any detector that chooses two separate fixed thresholds for the two gait speeds.

Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders.

Accurate stride-length estimation is a fundamental component in numerous applications, such as pedestrian dead reckoning, gait analysis, and human activity recognition. The existing stride-length estimation algorithms work relatively well in cases of walking a straight line at normal speed, but their error overgrows in complex scenes. Inaccurate walking-distance estimation leads to huge accumulative positioning errors of pedestrian dead reckoning. This paper proposes TapeLine, an adaptive stride-length estimation algorithm that automatically estimates a pedestrian’s stride-length and walking-distance using the low-cost inertial-sensor embedded in a smartphone. TapeLine consists of a Long Short-Term Memory module and Denoising Autoencoders that aim to sanitize the noise in raw inertial-sensor data. In addition to accelerometer and gyroscope readings during stride interval, extracted higher-level features based on excellent early studies were also fed to proposed network model for stride-length estimation. To train the model and evaluate its performance, we designed a platform to collect inertial-sensor measurements from a smartphone as training data, pedestrian step events, actual stride-length, and cumulative walking-distance from a foot-mounted inertial navigation system module as training labels at the same time. We conducted elaborate experiments to verify the performance of the proposed algorithm and compared it with the state-of-the-art SLE algorithms. The experimental results demonstrated that the proposed algorithm outperformed the existing methods and achieves good estimation accuracy, with a stride-length error rate of 4.63% and a walking-distance error rate of 1.43% using inertial-sensor embedded in smartphone without depending on any additional infrastructure or pre-collected database when a pedestrian is walking in both indoor and outdoor complex environments (stairs, spiral stairs, escalators and elevators) with natural motion patterns (fast walking, normal walking, slow walking, running, jumping).

Use of Magnetic Field for Mitigating Gyroscope Errors for Indoor Pedestrian Positioning.

In the field of indoor pedestrian positioning, the improved Quasi-Static magnetic Field (iQSF) method has been proposed to estimate gyroscope biases in magnetically perturbed environments. However, this method is only effective when a person walks along straight-line paths. For other curved or more complex path patterns, the iQSF method would fail to detect the quasi-static magnetic field. To address this issue, a novel approach is developed for quasi-static magnetic field detection in foot-mounted Inertial Navigation System. The proposed method detects the quasi-static magnetic field using the rate of change in differences between the magnetically derived heading and the heading derived from gyroscope. In addition, to eliminate the distortions caused by system platforms and shoes, a magnetometer calibration method is developed and the calibration is transformed from three-dimensional to two-dimensional coordinate according to the motion model of a pedestrian. The experimental results demonstrate that the proposed method can provide superior performance in suppressing the heading errors with the comparison to iQSF method.

Modeling, Detecting, and Tracking Freezing of Gait in Parkinson Disease Using Inertial Sensors.

In this paper, we develop new methods to automatically detect the onset and duration of freezing of gait (FOG) in people with Parkinson disease (PD) in real time, using inertial sensors. We first build a physical model that describes the trembling motion during the FOG events. Then, we design a generalized likelihood ratio test framework to develop a two-stage detector for determining the zero-velocity and trembling events during gait. Thereafter, to filter out falsely detected FOG events, we develop a point-process filter that combines the output of the detectors with information about the speed of the foot, provided by a foot-mounted inertial navigation system. We computed the probability of FOG by using the point-process filter to determine the onset and duration of the FOG event. Finally, we validate the performance of the proposed system design using real data obtained from people with PD who performed a set of gait tasks. We compare our FOG detection results with an existing method that only uses accelerometer data. The results indicate that our method yields 81.03% accuracy in detecting FOG events and a threefold decrease in the false-alarm rate relative to the existing method.

A Wearable Inertial Pedestrian Navigation System With Quaternion-Based Extended Kalman Filter for Pedestrian Localization

This paper presents a wearable inertial pedestrian navigation system and its associated pedestrian trajectory reconstruction algorithm for reconstructing pedestrian walking trajectories in indoor and outdoor environments. The proposed wearable inertial pedestrian navigation system is constructed by integrating a triaxial accelerometer, a triaxial gyroscope, a triaxial magnetometer, a microcontroller, and a Bluetooth wireless transmission module. Users wear the system on foot while walking in indoor and outdoor environments at normal speed without any external positioning techniques. During walking movement, the measured inertial signals generated from walking movements are transmitted to a computer via the wireless module. Based on the foot-mounted inertial pedestrian navigation system, a pedestrian trajectory reconstruction algorithm composed of the procedures of inertial signal acquisition, signal preprocessing, trajectory reconstruction, and trajectory height estimation has been developed to reconstruct floor walking and stair climbing trajectories. In order to minimize the cumulative error of the inertial signals, we have utilized a sensor fusion technique based on a double-stage quaternion-based extended Kalman filter to fuse acceleration, angular velocity, and magnetic signals. Experimental results have successfully validated the effectiveness of the proposed wearable inertial pedestrian navigation system and its associated pedestrian trajectory reconstruction algorithm.

Dual MIMU Pedestrian Navigation by Inequality Constraint Kalman Filtering.

The foot-mounted inertial navigation system is an important method of pedestrian navigation as it, in principle, does not rely any external assistance. A real-time range decomposition constraint method is proposed in this paper to combine the information of dual foot-mounted inertial navigation systems. It is well known that low-cost inertial pedestrian navigation aided with both ZUPT (zero velocity update) and the range decomposition constraint performs better than those in their own respective methods. This paper recommends that the separation distance between the position estimates of the two foot-mounted inertial navigation systems be restricted by an ellipsoidal constraint that relates to the maximum step length and the leg height. The performance of the proposed method is studied by utilizing experimental data, and the results indicate that the method can effectively correct the dual navigation systems’ position over the traditional spherical constraint.

Online three-axis magnetometer calibration for a pedestrian navigation system using a foot-mounted inertial navigation system

Foot-mounted inertial navigation systems(INS)having inexpensive micro electro mechanical system(MEMS)-based inertial sensors are widely used for pedestrian navigation.A foot-mounted INS can be combined with a magnetometer to constrain the heading angle error,but the magnetometer needs to be calibrated before use.This paper presents a magnetometer error model and an online calibration algorithm based on the foot-mounted INS characteristics.The magnetometer error characteristics and the foot-mounted INS mobility characteristics are used to develop a state equation and a magnetometer error measurement equation.An extended Kalman filter(EKF)is used for the online estimation and real-time calibration of the three-axis magnetometer errors with the zero velocity update(ZUPT)algorithm and a magnetic heading angle constraint algorithm for error constraint.The algorithm is validated by walking in a square playing ground.The results show that the online estimation and calibration algorithm reduce the end position error of the east direction from-110.7to 1.8mand the end position error of the north direction from 37.8to 5.2m compared to the system without calibration. This algorithm provides online calibration of magnetometer errors and significantly improves the positioning accuracy of pedestrian navigation.

Stance-Phase Detection for ZUPT-Aided Foot-Mounted Pedestrian Navigation System

Zero velocity updates (ZUPT) is an effective way for the foot-mounted inertial pedestrian navigation systems. For the ZUPT technique to work properly, it is necessary to correctly detect the stance phase of each gait cycle. An adaptive stance-phase detection method is proposed based solely on an inertial sensor, which deals with the measurement fluctuations in swing and stance phases differently, and applies a clustering algorithm to partition the potential gait phases into true and false clusters, thereby yielding a time threshold to eliminate the false gait phases. The roles of the detection parameters and the relationship between them are analyzed to offer some suggestions for parameter tuning. Detection performance is evaluated with multisubject experimental data collected at varying walking speeds. The evaluation results show that the proposed detection method performs well in the presence of measurement fluctuations, which can make the detection of stance phases more robust and the choice of detection parameters more flexible.

A Novel Kalman Filter with State Constraint Approach for the Integration of Multiple Pedestrian Navigation Systems

Numerous solutions/methods to solve the existing problems of pedestrian navigation/localization have been proposed in the last decade by both industrial and academic researchers. However, to date there are still major challenges for a single pedestrian navigation system (PNS) to operate continuously, robustly, and seamlessly in all indoor and outdoor environments. In this paper, a novel method for pedestrian navigation approach to fuse the information from two separate PNSs is proposed. When both systems are used at the same time by a specific user, a nonlinear inequality constraint between the two systems’ navigation estimates always exists. Through exploring this constraint information, a novel filtering technique named Kalman filter with state constraint is used to diminish the positioning errors of both systems. The proposed method was tested by fusing the navigation information from two different PNSs, one is the foot-mounted inertial navigation system (INS) mechanization-based system, the other PNS is a navigation device that is mounted on the user’s upper body, and adopting the pedestrian dead reckoning (PDR) mechanization for navigation update. Monte Carlo simulations and real field experiments show that the proposed method for the integration of multiple PNSs could improve each PNS’ navigation performance.

Zero-Velocity Detection—An Algorithm Evaluation

In this study, we investigate the problem of detecting time epochs when zero-velocity updates can be applied in a foot-mounted inertial navigation (motion tracking) system. We examine three commonly used detectors: the acceleration moving variance detector, the acceleration magnitude detector, and the angular rate energy detector. We demonstrate that all detectors can be derived within the same general likelihood ratio test framework given the different prior knowledge about the sensor signals. Further, by combining all prior knowledge, we derive a new likelihood ratio test detector. Subsequently, we develop a methodology to evaluate the performance of the detectors. Employing the developed methodology, we evaluate the performance of the detectors using leveled ground, slow (approx. 3 km/h) and normal (approx. 5 km/h) gait data. The test results are presented in terms of detection versus false-alarm probability. Our preliminary results shows that the new detector performs marginally better than the angular rate energy detector that outperforms both the acceleration moving variance detector and the acceleration magnitude detector.