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We present a study on the adaptive threshold of the zero-velocity detector, which enables the detector to adjust to gait patterns with different speeds, from as low as walking with 80 steps per minute to as high as running with 160 steps per minute, without any tuning of design parameters during the navigation. This approach enables the zero-velocity update (ZUPT)-aided navigation algorithm to work properly with time-varying speed in a single navigation process. A Bayesian-based approach was applied to determine the adaptive threshold in the likelihood ratio test with a uniform prior information and time-varying cost function. The position error in a velocity-changing navigation scenario was demonstrated to be reduced by 12 times after applying the adaptive threshold instead of a fixed threshold.

We present an analytical relation between the position estimation uncertainty and inertial measurement unit (IMU) characteristics during zero-velocity-update (ZUPT) augmented pedestrian inertial navigation. The effect of angle random walk of gyroscopes and velocity random walk of accelerometers in IMU on circular error probable is studied. Numerical simulation of the ZUPT-augmented navigation algorithm is conducted based on a generated pedestrian trajectory to verify the analytical results, showing the discrepancy less than 15, which supports the fidelity of the results. Experiments are also conducted, and the results match our analytical prediction, with the error less than 20. This study offers a closed-form analytical expression to predict the performance of ZUPT-augmented pedestrian inertial navigation.

Aiming at problems of poor environmental adaptability of photoelectric navigation and low accuracy of long-term positioning of inertial navigation adopted in roadheader positioning, an inertial navigation positioning method of roadheader based on zero-velocity update was put forward based on analysis of movement characteristics of roadheader. In short stopping time interval of roadheader, velocity error curve of inertial navigation system is fitted by quadratic curve, and position error curve is obtained by integration, so as to correct position measurement value of inertial navigation system. The test results show that the inertial navigation positioning accuracy of roadheader based on zero-speed update can reach centimeter-level, fluctuation range of positioning error is small, and accumulated positioning error has no obvious increasing trend with the increase of time, which meets positioning requirements of roadheader.

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Highlights • Generally, Pedestrian Dead Reckoning (PDR) system with a foot mounted inertial sensors uses zero-velocity update (ZUPT). • ZUPT is not suitable for an irregular walk because the velocity is not small enough to assume zero during the stance phase. • Proposed PDR system uses a predicted velocity at the contact phase where a shoe touches the ground. • An ellipsoidal shoe modeling predicts the nonzero velocity under kinematic constraint. • The performance of the proposed algorithm is verified by experimental results with various motions. Abstract In this paper, we propose a pedestrian dead reckoning (PDR) system that uses the velocity predicted by a foot kinematic constraint and ellipsoidal shoe modeling in the contact phase. Generally, a PDR system with foot-mounted inertial sensors often uses zero-velocity update (ZUPT) to reduce the influence of the bias and white noise in the gyroscope and accelerometer signals. However, when a pedestrian walks irregularly, ZUPT is unsuitable because the velocity is not small enough to assume that it is zero during the stance phase. Unlike a conventional PDR system that uses ZUPT in the stance phase when the shoe has completely stopped, the proposed PDR system uses the predicted velocity in the contact phase when the shoe touches the ground from the heel-strike to the toe-off states by employing the lever arm vector and the angular velocity to predict the nonzero velocity under the kinematic constraint. The lever arm vector is obtained by the attitude of the shoe according to ellipsoidal shoe modeling. The predicted velocity is used as a measure of the filter during the contact phase. The proposed PDR system can be utilized in various motions with nonzero velocity in the contact phase. The performance of the algorithm was verified by experimental results with various motions. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel pedestrian indoor positioning system that uses sensor fusion between a foot-mounted inertial measurement unit (IMU) and a vision-based fiducial marker tracking system. The goal is to provide an after-action review for first responders during training exercises. The main contribution of this work comes from the observation that different walking types (e.g., forward walking, sideways walking, backward walking) lead to different levels of position and heading error. Our approach takes this into account when accumulating the error, thereby leading to more-accurate estimations. Through experimentation, we show the variation in error accumulation and the improvement in accuracy alter when and how often to activate the camera tracking system, leading to better balance between accuracy and power consumption overall. The IMU and vision-based systems are loosely coupled using an extended Kalman filter (EKF) to ensure accurate and unobstructed positioning computation. The motion model of the EKF is derived from the foot-mounted IMU data and the measurement model from the vision system. Existing indoor positioning systems for training exercises require extensive active infrastructure installation, which is not viable for exercises taking place in a remote area. With the use of passive infrastructure (i.e., fiducial markers), the positioning system can accurately track user position over a longer duration of time and can be easily integrated into the environment. We evaluated our system on an indoor trajectory of 250 m. Results show that even with discrete corrections, near a meter level of accuracy can be achieved. Our proposed system attains the positioning accuracy of 0.55 m for a forward walk, 1.05 m for a backward walk, and 1.68 m for a sideways walk with a 90% confidence level.

Unlike industrial-grade Inertial Navigation Sensors (INSs) that can provide credible tracking performance, more affordable consumer-grade low-cost INSs produce drifts in heading angles and positions that result in a poor tracking accuracy. Researchers have proposed drift correction methods that attempt to attenuate the drifts when walking straight along the dominant directions is detected. While determining the type of a pedestrian's walk is essential before the heading corrections are made, the current detection techniques heavily rely on thresholding. This paper proposes a novel threshold-less method to detect turns in walking by using pelvic rotation and correct the heading angle based on consumer-grade INSs. The experiments indicate the proposed turn detector and heading correction methods produce very good results which can be applied for future pedestrian tracking, activity recognition or rehabilitation. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel method for 3D pedestrian navigation of foot-mounted inertial systems by integrating a MEMS-IMU, barometer, and permanent magnet. Zero-velocity update (ZUPT) is a well-known algorithm to eliminate the accumulated error of foot-mounted inertial systems. However, the ZUPT stance phase detector using acceleration and angular rate is threshold-based, which may cause incorrect stance phase estimation in the running gait pattern. A permanent magnet-based ZUPT detector is introduced to solve this problem. Peaks extracted from the magnetic field strength waveform are mid-stances of stance phases. A model of peak-peak information and stance phase duration is developed to have a quantitative calculation method of stance phase duration in different movement patterns. Height estimation using barometer is susceptible to the environment. A height difference information aided barometer (HDIB) algorithm integrating MEMS-IMU and barometer is raised to have a better height estimation. The first stage of HDIB is to distinguish level ground/upstairs/downstairs and the second stage is to calculate height using reference atmospheric pressure obtained from the first stage. At last, a ZUPT-based adaptive average window length algorithm (ZUPT-AAWL) is proposed to calculate the true total travelled distance to have a more accurate percentage error (TTDE). This proposed method is verified via multiple experiments. Numerical results show that TTDE ranges from 0.32% to 1.04% in both walking and running gait patterns, and the height estimation error is from 0 m to 2.35 m. [ABSTRACT FROM AUTHOR]

A variety of indoor positioning technologies have emerged, such as ultrasound, infrared, wireless local area network, bluetooth, radio frequency identification, and ultra wideband. These systems require additional hardware devices and complicated deployment. Pedestrian Dead Reckoning can provide motion information at a high update rate and achieve high precision over a short time duration, However, without external aids, the system suffers from local anomalies and cumulative error after positioning for a longer length of time. Many researchers use a step length model combined with direction information to calculate the displacement of indoor pedestrians with accuracy step counting method. A step counting algorithm based on zero-velocity update was proposed. By analyzing the pedestrian's moving character and posture, acceleration amplitude measurement used to detection the movement stage of pedestrian was employed to calculate steps with Micro Electro Mechaical System (MEMS). The experimental results show that the scheme proposed in this paper was high precise and had good adaptability to different motion state, reporting accurately more than 98% overall, which is appropriate for complex indoor environment. [ABSTRACT FROM AUTHOR]

A WiFi-received signal strength index (RSSI) fingerprinting-based indoor positioning system (WiFi-RSSI IPS) is widely studied due to advantages of low cost and high accuracy, especially in a complex indoor environment where performance of the ranging method is limited. The key drawback that limits the large-scale deployment of WiFi-RSSI IPS is time-consuming offline site surveys. To solve this problem, we developed a method using multi-mounted devices to construct a lightweight site-survey radio map (LSS-RM) for WiFi positioning. A smartphone was mounted on the foot (Phone-F) and another on the waist (Phone-W) to scan WiFi-RSSI and simultaneously sample microelectromechanical system inertial measurement-unit (MEMS-IMU) readings, including triaxial accelerometer, gyroscope, and magnetometer measurements. The offline site-survey phase in LSS-RM is a client–server model of a data collection and preprocessing process, and a post calibration process. Reference-point (RP) coordinates were estimated using the pedestrian dead-reckoning algorithm. The heading was calculated with a corner detected by Phone-W and the preassigned site-survey trajectory. Step number and stride length were estimated using Phone-F based on the stance-phase detection algorithm. Finally, the WiFi-RSSI radio map was constructed with the RP coordinates and timestamps of each stance phase. Experimental results show that our LSS-RM method can reduce the time consumption of constructing a WiFi-RSSI radio map from 54 min to 7.6 min compared with the manual site-survey method. The average positioning error was below 2.5 m with three rounds along the preassigned site-survey trajectory. LSS-RM aims to reduce offline site-survey time consumption, which would cut down on manpower. It can be used in the large-scale implementation of WiFi-RSSI IPS, such as shopping malls, hospitals, and parking lots.

Objective: The foot progression angle (FPA) is an important clinical measurement but currently can only be computed while walking in a laboratory with a marker-based motion capture system. This paper proposes a novel FPA estimation algorithm based on a single integrated sensor unit, consisting of an accelerometer, gyroscope, and magnetometer, worn on the foot. Methods: The algorithm introduces a real-time heading vector with a complementary filter and utilizes a gradient descent method and zero-velocity update correction. Validation testing was performed by comparing FPA estimation from the wearable sensor with the standard FPAs computed from a marker-based motion capture system. Subjects performed nine walking trials of 2.5 min each on a treadmill. During each trial, subjects walked at one speed out of three options (1.0, 1.2, and 1.4 m/s) and walked with one gait pattern out of three options (normal, toe-in, and toe-out). Results: The algorithm estimated FPA to within 0.2 ^\circ of error or less for each walking conditions. Conclusion: A novel FPA algorithm has been introduced and described based on a single foot-worn sensor unit, and validation testing showed that FPA estimation was accurate for different walking speeds and foot angles. Significance: This study enables future wearable systems gait research to assess or train walking patterns outside a laboratory setting in natural walking environments. [ABSTRACT FROM PUBLISHER]

This paper investigates techniques on improving navigation accuracy using multiple sensors mounted on a mobile platform and exploiting the inherent characteristic of a ground vehicle that does not move along the cross-track and off the ground, often termed nonholonomic constraints (NHC) for car-like vehicles that assume no slip or skid. The forward velocity of the vehicle is obtained using a wheel encoder. The 3D velocity vector becomes observable during the normal moving state of the vehicle by using NHC, which produces one virtual sensor. Another virtual sensor is the zero-velocity update (ZVU) condition of the vehicle; when the condition is true, the 3D velocity vector (which is zero) becomes observable. These observables were employed in an extended Kalman filter (EKF) update to limit the growth of inertial navigation system error. We designed an EKF for data fusion of inertial measurement units, global positioning systems (GPS), and motion constraints (i.e., NHC and ZVU). We analyzed the effects of utilizing these constraints on improving navigation accuracy in stationary and dynamic cases. Our proposed navigation suite provides reliable accuracy for unmanned ground vehicle applications in a GPS-denied environment (e.g., forest canopy and urban canyon). [ABSTRACT FROM AUTHOR]

In this paper, we present a novel pedestrian indoor positioning system that uses sensor fusion between a foot-mounted inertial measurement unit (IMU) and a vision-based fiducial marker tracking system. The goal is to provide an after-action review for first responders during training exercises. The main contribution of this work comes from the observation that different walking types (e.g., forward walking, sideways walking, backward walking) lead to different levels of position and heading error. Our approach takes this into account when accumulating the error, thereby leading to more-accurate estimations. Through experimentation, we show the variation in error accumulation and the improvement in accuracy alter when and how often to activate the camera tracking system, leading to better balance between accuracy and power consumption overall. The IMU and vision-based systems are loosely coupled using an extended Kalman filter (EKF) to ensure accurate and unobstructed positioning computation. The motion model of the EKF is derived from the foot-mounted IMU data and the measurement model from the vision system. Existing indoor positioning systems for training exercises require extensive active infrastructure installation, which is not viable for exercises taking place in a remote area. With the use of passive infrastructure (i.e., fiducial markers), the positioning system can accurately track user position over a longer duration of time and can be easily integrated into the environment. We evaluated our system on an indoor trajectory of 250 m. Results show that even with discrete corrections, near a meter level of accuracy can be achieved. Our proposed system attains the positioning accuracy of 0.55 m for a forward walk, 1.05 m for a backward walk, and 1.68 m for a sideways walk with a 90% confidence level. [ABSTRACT FROM AUTHOR]

A WiFi-received signal strength index (RSSI) fingerprinting-based indoor positioning system (WiFi-RSSI IPS) is widely studied due to advantages of low cost and high accuracy, especially in a complex indoor environment where performance of the ranging method is limited. The key drawback that limits the large-scale deployment of WiFi-RSSI IPS is time-consuming offline site surveys. To solve this problem, we developed a method using multi-mounted devices to construct a lightweight site-survey radio map (LSS-RM) for WiFi positioning. A smartphone was mounted on the foot (Phone-F) and another on the waist (Phone-W) to scan WiFi-RSSI and simultaneously sample microelectromechanical system inertial measurement-unit (MEMS-IMU) readings, including triaxial accelerometer, gyroscope, and magnetometer measurements. The offline site-survey phase in LSS-RM is a client⁻server model of a data collection and preprocessing process, and a post calibration process. Reference-point (RP) coordinates were estimated using the pedestrian dead-reckoning algorithm. The heading was calculated with a corner detected by Phone-W and the preassigned site-survey trajectory. Step number and stride length were estimated using Phone-F based on the stance-phase detection algorithm. Finally, the WiFi-RSSI radio map was constructed with the RP coordinates and timestamps of each stance phase. Experimental results show that our LSS-RM method can reduce the time consumption of constructing a WiFi-RSSI radio map from 54 min to 7.6 min compared with the manual site-survey method. The average positioning error was below 2.5 m with three rounds along the preassigned site-survey trajectory. LSS-RM aims to reduce offline site-survey time consumption, which would cut down on manpower. It can be used in the large-scale implementation of WiFi-RSSI IPS, such as shopping malls, hospitals, and parking lots.

In this paper, we present a novel method for 3D pedestrian navigation of foot-mounted inertial systems by integrating a MEMS-IMU, barometer, and permanent magnet. Zero-velocity update (ZUPT) is a well-known algorithm to eliminate the accumulated error of foot-mounted inertial systems. However, the ZUPT stance phase detector using acceleration and angular rate is threshold-based, which may cause incorrect stance phase estimation in the running gait pattern. A permanent magnet-based ZUPT detector is introduced to solve this problem. Peaks extracted from the magnetic field strength waveform are mid-stances of stance phases. A model of peak-peak information and stance phase duration is developed to have a quantitative calculation method of stance phase duration in different movement patterns. Height estimation using barometer is susceptible to the environment. A height difference information aided barometer (HDIB) algorithm integrating MEMS-IMU and barometer is raised to have a better height estimation. The first stage of HDIB is to distinguish level ground/upstairs/downstairs and the second stage is to calculate height using reference atmospheric pressure obtained from the first stage. At last, a ZUPT-based adaptive average window length algorithm (ZUPT-AAWL) is proposed to calculate the true total travelled distance to have a more accurate percentage error (TTDE). This proposed method is verified via multiple experiments. Numerical results show that TTDE ranges from 0.32% to 1.04% in both walking and running gait patterns, and the height estimation error is from 0 m to 2.35 m., Competing Interests: The authors declare no conflict of interest.