Pedestrian Dead Reckoning System Research Articles

An accurate smartphone-based indoor pedestrian localization system using ORB-SLAM camera and PDR inertial sensors fusion approach

Using one’s own smartphone for indoor navigation, especially for visually impaired individuals, can be a valuable and empowering tool to promote independence and safe mobility. Additionally, the integration of Pedestrian Dead Reckoning (PDR), which relies on smartphone sensors like accelerometers and gyroscopes, is a well-known and widely used approach in the field of indoor navigation. However, despite continuous improvements in sensor technology, addressing drift issues remains a principal problem in the effective implementation of PDR systems. On the other hand, although the use of Simultaneous Localization and Mapping (SLAM) technologies in smartphones for indoor navigation has also gained significant adoption in recent years, there are indeed challenges associated with SLAM, including low localization accuracy and potential tracking divergence. To overcome the above limitations, this paper presents a novel and promising solution for smartphone-based indoor localization, addressing the limitations of both PDR and SLAM through their synergistic integration. In fact, to correct the accumulated errors of the proposed localization system and thus improve the accuracy, our approach uses an ORB-SLAM camera and PDR inertial sensors via a Kalman filter to achieve back-end fusion. Experimental results show that our localization system significantly reduces the mean localization error by 62%, marking a considerable improvement over the standalone PDR and SLAM systems. Moreover, compared with state-of-the-art techniques, our system achieves high localization accuracy, with an improvement of 59%, making it highly suitable for today’s real-time applications, especially beneficial for visually impaired people.

Predicting Signal Reception Information from GNSS Satellites in Indoor Environments without Site Survey: Towards Opportunistic Indoor Positioning Based on GNSS Fingerprinting

While traditional GPS localization is impractical in certain indoor environments, GPS satellites offer a valuable source of indoor contextual information, particularly around window-side areas where signals are more accessible. This study introduces a new method that predicts GPS signal reception information in a target indoor environment without the need for actual data collection in the target environment. By predicting the strengths of GPS signals that are expected to be measured at various indoor positions, we can construct a GPS signal reception map of a target environment, which functions similarly to a Wi-Fi fingerprinting map surrounding window-side areas, offering a new dimension in indoor positioning systems. For instance, it aids in the correction of accumulated errors in pedestrian dead reckoning (PDR) systems. Our proposed method leverages easily accessible inputs, including satellite location data, indoor floorplan image of the target floor, and 3D mapping of the surrounding buildings, to predict signal reception at each position in the target environment. We developed a specialized neural network, named multi-scale branch fusion network (MSBF-Net), designed to process and integrate data of varying scales, such as the floorplan image of the target environment and 3D map of the surrounding area obtained from Google Earth, with a specific focus on understanding the line-of-sight (LOS) and multi-path effects caused by internal obstacles and surrounding buildings. This advanced capability enables the network to effectively interpret complex signal interactions within urban environments, enhancing its predictive accuracy for GPS signal reception. The effectiveness of our method was rigorously evaluated using real-world environments. In addition, we employed our method to implement opportunistic GPS fingerprint-based indoor positioning, where position estimates are provided when a strong signal is observed from at least one satellite. Surprisingly, the positioning method achieved a positioning error of only 2.8 meters when a smartphone is close to window-side areas even though the method does not rely on labeled training data collected in a target environment and signal infrastructures installed in the target environment.

Learning-Based Stance Phase Detection and Multisensor Data Fusion for ZUPT-Aided Pedestrian Dead Reckoning System

Learning-Based Stance Phase Detection and Multisensor Data Fusion for ZUPT-Aided Pedestrian Dead Reckoning System

Enhanced Pedestrian Navigation on Smartphones With VLP-Assisted PDR Integration

Pedestrian dead-reckoning (PDR) is a potential indoor localization technology that utilizes the inertial measurement unit (IMU) to estimate location. However, one of its most significant drawbacks is the accumulation measurement error. This paper proposes a PDR system integrated with visible light positioning (VLP) that boasts low accumulated error and can achieve real-time and accurate indoor positioning using both the IMU and the camera sensor on a smartphone. A multi-frame fusion method for LED-ID decoding is introduced in the encoding and decoding process of the system, achieving 98.5% decoding accuracy with a 20-bit-long LED-ID at the height of 2.1 m. This method also accommodates the variation in the shutter speeds of cameras and heights of the LED. Meanwhile, absolute locations and step length could be further calibrated using a single light-emitting diode (LED), providing an average accuracy within 0.5 meters over a 108-meter walk.

A Hybrid CNN-LSTM-Based Approach for Pedestrian Dead Reckoning Using Multi-Sensor-Equipped Backpack

Researchers in academics and companies working on location-based services (LBS) are paying close attention to indoor localization based on pedestrian dead reckoning (PDR) because of its infrastructure-free localization method. PDR is the fundamental localization technique that utilize human motion to perform localization in a relative sense with respect to the initial position. The size, weight, and power consumption of micromechanical systems (MEMS) embedded into smartphones are remarkably low, making them appropriate for localization and positioning. Traditional pedestrian PDR methods predict position and orientation using stride length and continuous integration of acceleration in step and heading system (SHS)-based PDR and inertial navigation system (INS)-PDR, respectively. However, these two approaches provide accumulations of error and do not effectively leverage the inertial measurement unit (IMU) sequences. The PDR navigation solution relays on the standard of the MEMS, which yields PDR with the acceleration and angular velocity from the accelerometer and gyroscope, respectively. However, low-cost small MEMSs endure enormous error sources such as bias and noise. Hence, MEMS assessments lead to navigation solution drifts when utilized as inputs to the PDR. As a consequence, numerous methods have been proposed to mitigate and model the errors related to MEMS. Deep learning-based dead reckoning algorithms are provided to address aforementioned issues owing to the end-to-end learning framework. This paper proposes a hybrid convolutional neural network (CNN) and long short-term memory network (LSTM)-based inertial PDR system that extracts inertial measurement units (IMU) sequence features. The end-to-end learning framework is introduced to leverage the efficiency of low-cost MEMS because data-driven solutions provide more complete knowledge of the ever-increasing data volume and computational power over the filtering model approach. A CNN-LSTM model was employed to capture local spatial and temporal features. Experiments conducted on odometry datasets collected from multi-sensor backpack devices demonstrated that the proposed architecture outperformed previous traditional PDR methods, demonstrating that the root mean square error (RMSE) for the best user was 0.52 m. On the handheld smartphone-only dataset the best achieved R2 metric was 0.49.

Accurate Stride-Length Estimation Based on LT-StrideNet for Pedestrian Dead Reckoning Using a Shank-Mounted Sensor.

Pedestrian dead reckoning (PDR) is a self-contained positioning technology and has been a significant research topic in recent years. Pedestrian-stride-length estimation is the core part of the PDR system and directly affects the performance of the PDR. The current stride-length-estimation method is difficult to adapt to changes in pedestrian walking speed, which leads to a rapid increase in the error of the PDR. In this paper, a new deep-learning model based on long short-term memory (LSTM) and Transformer, LT-StrideNet, is proposed to estimate pedestrian-stride length. Next, a shank-mounted PDR framework is built based on the proposed stride-length-estimation method. In the PDR framework, the detection of pedestrian stride is achieved by peak detection with a dynamic threshold. An extended Kalman filter (EKF) model is adopted to fuse the gyroscope, accelerometer, and magnetometer. The experimental results show that the proposed stride-length-estimation method can effectively adapt to changes in pedestrian walking speed, and our PDR framework has excellent positioning performance.

Deep-Learning-Based Step Detection and Step Length Estimation With a Handheld IMU

A popular approach to track the position of a user is to use an inertial measurement unit (IMU), which allows tracking a user with a pedestrian dead reckoning (PDR) system by estimating the length and heading of each step a user takes. Furthermore, a user’s steps together with their length can be used to detect anomalies in the gait due to, e.g., a gait disorder. To determine the length and/or heading of a step, we first need an algorithm that estimates the boundaries of a step, i.e., the start and end, in the IMU signals. In this article, we propose a deep-learning-based step detection algorithm that detects the start and end of a step using only data from a handheld accelerometer. In this algorithm, also, the relationship between the start and end of a step is used to improve performance. To evaluate the performance of the detector, in contrast to most previous works that only look at the total number of detected steps, we determine if the start and end of each step are detected at the correct instant. This resulted in a step detector with an f-score of 99.2% and 99.0% for, respectively, detecting the start and end of a step. After detecting the start and end of the steps, we use a long short-term memory (LSTM)/convolutional neural network (CNN)-based step length estimator, which resulted in a mean absolute error (mae) on the step length of 3.21 cm. Finally, we also determine the combined performance of the step detector and step length estimator, i.e., where the proposed step detector is used to extract the accelerometer fragments of a step instead of assuming that the boundaries are known. This resulted in an mae of 6.56 cm using the deep-learning-based step length estimator.

Inaudible Sounds From Appliances as Anchors: A New Signal of Opportunity for Indoor Localization

The indoor localization still has challenges when compromising accuracy, cost, scalability, and so on. In recent years, signals of opportunity have been widely used for low-cost and scalable indoor localization services, especially in complex and changeable environments. In this article, a new opportunistic signal for indoor localization is proposed using the inaudible sound pattern from the appliances, which has superiority in long-term existence, stability, high availability, and privacy protection. The short-time Fourier transformed feature is exploited with a random forest model to recognize the appliance, and a promised matching algorithm is proposed to avoid any false detection. The recognized appliances can be used as standalone location indicators or supplements, such as loop closure pairs and additional landmarks, to the existed indoor localization system. The experimental results show that the appliance recognition achieves a recall rate of 79.66% under 100% precision, and a 95.43% improvement of localization accuracy is obtained to the pedestrian dead reckoning system over a 621.58-m trajectory.

Online calibrated, energy-aware and heading corrected pedestrian navigation with foot-mounted MARG sensors

The objective of this paper is to propose an energy-aware and gait corrected pedestrian dead reckoning (PDR) approach using foot-mounted magnetic, angular rate and gravity (MARG) sensors. Compared with existing algorithms of PDR, the proposed method aims to solve three main problems for real pedestrian applications. First, to avoid limitations of off-line calibration for personal step length parameters, we utilize the zero-velocity-update (ZUPT) aided pedestrian MARG performance to continuously compute one’s pose information. Meanwhile, it accumulates the moving distance for further estimation of one’s step length during the initialization process. Secondly, due to different pedestrian gaits implicating the heading deviation angle between one’s moving direction and heading, there are non-negligible impacts on pedestrian dead reckoning accuracy. The linear Kalman filter (KF) is used to recursively estimate the deviated heading during aforementioned initialization process. The third problem is related to the energy consumption. Following three aspects of adaptive energy saving work are devised: (i) energy-aware strategy for gyroscopes measurements acquisition is adopted to guarantee lower energy consumption. (ii) Modes switching mechanism of navigation computation is applied to the initialization and dead reckoning processes. (iii) De-sampling after initialization process has been invoked. Finally, real-world experiments are carried out to evaluate the performances of developed PDR system. The results show the efficiency of the suggested approach.

LiDR: Visible-Light-Communication-Assisted Dead Reckoning for Accurate Indoor Localization

Pedestrian dead reckoning (PDR) is an inertial navigation system that relies on smartphone sensors for estimating a pedestrian’s step movements. However, such systems suffer from poor accuracy due to the drift and inherent noise in sensor readings. In addition, step size variation among pedestrians and device heterogeneity pose further challenges for building a scalable PDR system that can provide uniform performance across various devices and a diverse range of users. Visible light positioning (VLP), which uses LED lights with visible light communication (VLC) capability to provide high-accuracy localization, can achieve precision of a few cm. However, VLP systems suffer from practical limitations due to occasional line-of-sight (LOS) blockage and the sparse density of lighting in large-scale indoor venues. In this work, we propose a light-assisted dead reckoning (LiDR) system, which aims to address the problems of both VLP and PDR. It uses LED lighting as high-accuracy location landmarks to provide regular calibration for the PDR and estimates the individual pedestrians’ step size for increased accuracy. In addition, a light-shape-based heading angle correction algorithm is proposed to reduce the heading angle error and further improve the accuracy. The system is implemented as an Android-based navigation application, with a digital map and cloud-based backend storage for location, device, and user-specific parameters. The real-time performance of the system is evaluated in a 450- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{m}^{2}$ </tex-math></inline-formula> lab and on a 150-m walking track. The experimental results demonstrate that with a maximum light spacing of 15 m, an overall average accuracy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lt ~0.7$ </tex-math></inline-formula> m can be achieved for the whole system.

Magnetic Matching-Aided Indoor Localization System Based on a Waist-Mounted Self-Contained Sensor Array

This paper presents a magnetic matching-aided indoor localization system based on a waist-mounted self-contained sensor array. Our purpose is to localize and track the elderly in nursing homes through the proposed wearable device to ensure their safety. The device consists of a low-cost 9-axis self-contained sensor array, a microcontroller, and a WiFi transmission module. This system uses the step length and heading-based pedestrian dead reckoning (PDR) framework as the backbone to estimate the user’s position using the averaged inertial data from the sensor array. A magnetic fingerprint matching (MFM) algorithm is introduced to constrain the drift of the PDR system. Meanwhile, we construct a single-step-based hybrid magnetic fingerprint model to improve the low discernibility of the magnetic field. Finally, we propose an augmented particle filter to fuse the PDR and the MFM algorithms to enhance the system performance further. Experimental results show that 95% of the positioning error after fusion is about 1.47 m, which is 99.3% higher than that of PDR, and the average positioning error after fusion is 0.55 m, which is 61.3% higher than that of PDR. Experimental results have successfully validated the effectiveness and high performance of the proposed magnetic matching-aided wearable indoor localization system.

Adaptive Pedestrian Stride Estimation for Localization: From Multi-Gait Perspective.

Accurate and reliable stride length estimation modules play a significant role in Pedestrian Dead Reckoning (PDR) systems, but the accuracy of stride length calculation suffers from individual differences. This paper presents a stride length prediction strategy for PDR systems that can be adapted across individuals and broad walking velocity fields. It consists of a multi-gait division algorithm, which can divide a full stride into push-off, swing, heel-strike, and stance based on multi-axis IMU data. Additionally, based on the acquired gait phases, the correlation between multiple features of distinct gait phases and the stride length is analyzed, and multi regression models are merged to output the stride length value. In experimental tests, the gait segmentation algorithm provided gait phases division with the F-score of 0.811, 0.748, 0.805, and 0.819 for stance, push-off, swing, heel-strike, respectively, and IoU of 0.482, 0.69, 0.509 for push-off, swing, heel-strike, respectively. The root means square error (RMSE) of our proposed stride length estimation was 151.933, and the relative error for total distance in varying walking speed tests was less than 2%. The experimental results validated that our proposed gait phase segmentation algorithm can accurately recognize gait phases for individuals with wide walking speed ranges. With no need for parameter modification, the stride length method based on the fusion of multiple predictions from different gait phases can provide better accuracy than the estimations based on the full stride.

Learning-based stance-phase detection for a pedestrian dead-reckoning system with dynamic gait speeds

Due to the errors arising from low-cost inertial measurement units, pedestrian dead-reckoning (PDR) systems suffer from accumulative velocity and position errors. Previous works proved that the zero-velocity update (ZUPT) method can effectively reduce the velocity errors of the PDR system so that positional errors can be mitigated. The ZUPT algorithm is triggered by a stance phase-detection module that generally adopts a fixed threshold. However, the fixed-threshold-based detector is not robust enough and may not recognize stance phases accurately with dynamic gait speeds. Incorrect detection degrades the positioning accuracy of the overall system. In this paper, to improve the performance of the stance-phase detector, we explore the relationship between threshold and gait speed, and then threshold regression problems are constructed to design machine-learning-based stance-phase detectors that are robust to gait speed. Real-world highly dynamic experiments have illustrated the effectiveness of the proposed methods with dynamic gait speeds. Compared to the best fixed-threshold-based traditional method, the experimental results show that the machine-learning-based methods reduce the minimum root mean squared error (RMSE) of the distance measurement by 22.5%–37.2%, the minimal RMSE of the start–end error by 5.8%–13.2% and the average RMSE of the positional error by 12.4%–17.5%.

An Adaptive Human Activity-Aided Hand-Held Smartphone-Based Pedestrian Dead Reckoning Positioning System

Pedestrian dead reckoning (PDR), enabled by smartphones’ embedded inertial sensors, is widely applied as a type of indoor positioning system (IPS). However, traditional PDR faces two challenges to improve its accuracy: lack of robustness for different PDR-related human activities and positioning error accumulation over elapsed time. To cope with these issues, we propose a novel adaptive human activity-aided PDR (HAA-PDR) IPS that consists of two main parts, human activity recognition (HAR) and PDR optimization. (1) For HAR, eight different locomotion-related activities are divided into two classes: steady-heading activities (ascending/descending stairs, stationary, normal walking, stationary stepping, and lateral walking) and non-steady-heading activities (door opening and turning). A hierarchical combination of a support vector machine (SVM) and decision tree (DT) is used to recognize steady-heading activities. An autoencoder-based deep neural network (DNN) and a heading range-based method to recognize door opening and turning, respectively. The overall HAR accuracy is over 98.44%. (2) For optimization methods, a process automatically sets the parameters of the PDR differently for different activities to enhance step counting and step length estimation. Furthermore, a method of trajectory optimization mitigates PDR error accumulation utilizing the non-steady-heading activities. We divided the trajectory into small segments and reconstructed it after targeted optimization of each segment. Our method does not use any a priori knowledge of the building layout, plan, or map. Finally, the mean positioning error of our HAA-PDR in a multilevel building is 1.79 m, which is a significant improvement in accuracy compared with a baseline state-of-the-art PDR system.

Pseudo-Zero Velocity Re-Detection Double Threshold Zero-Velocity Update (ZUPT) for Inertial Sensor-Based Pedestrian Navigation

Zero-velocity update method (ZUPT) is widely used in inertial measurement unit (IMU)-based pedestrian navigation systems for mitigating sensor drifting error. In the basic pedestrian dead reckoning (PDR) system, especially in the foot-tie PDR system, zero-velocity update method with Kalman filter are two core algorithms. In the basic PDR system, ZUPT usually uses a single threshold to judge the gait of pedestrians. A single threshold, however, makes ZUPT unable to accurately judge the gait of pedestrians in different road conditions. In this paper, we propose a new, redesigned ZUPT method without using additional equipment and filter algorithms to further improve the accuracy of correction results. The method uses a sliding detection algorithm to help re-detect the zero-velocity intervals, aiming to remove the pseudo-zero velocity interval and the pseudo-motion interval, as well as improving the performance of the ZUPT method. The method was implemented in a shoe-mounted IMU-based navigation system. For walking step detection tests, the accuracy of the proposed modified ZUPT method reached 87.24%, 25% higher than the conventional methods. In a long-distance walking path tracking test, the mean error of the estimated path of our method is 0.61 m, an 81.69% reduction compared to the conventional ZUPT methods. The details of the improved ZUPT method presented in this paper not only enable the tracking technology to better track a pedestrian’s step changes during walking, but also provide better calculation conditions for subsequent filter operations.

S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices

Mobile location-based services (MLBS) are attracting attention for their potential public and personal use for a variety of applications such as location-based advertisement, smart shopping, smart cities, health applications, emergency response, and even gaming. Many of these applications rely on Inertial Navigation Systems (INS) due to the degraded GNSS services indoors. INS-based MLBS using smartphones is hindered by the quality of the MEMS sensors provided in smartphones which suffer from high noise and errors resulting in high drift in the navigation solution rapidly. Pedestrian dead reckoning (PDR) is an INS-based navigation technique that exploits human motion to reduce navigation solution errors, but the errors cannot be eliminated without aid from other techniques. The purpose of this study is to enhance and extend the short-term reliability of PDR systems for smartphones as a standalone system through an enhanced step detection algorithm, a periodic attitude correction technique, and a novel PCA-based motion direction estimation technique. Testing shows that the developed system (S-PDR) provides a reliable short-term navigation solution with a final positioning error that is up to 6 m after 3 min runtime. These results were compared to a PDR solution using an Xsens IMU which is known to be a high grade MEMS IMU and was found to be worse than S-PDR. The findings show that S-PDR can be used to aid GNSS in challenging environments and can be a viable option for short-term indoor navigation until aiding is provided by alternative means. Furthermore, the extended reliable solution of S-PDR can help reduce the operational complexity of aiding navigation systems such as RF-based indoor navigation and magnetic map matching as it reduces the frequency by which these aiding techniques are required and applied.

A Novel Robust Step Detection Algorithm for Foot-Mounted IMU

Step detection is very important for the foot-mounted Pedestrian Dead Reckoning (PDR) system. To deal with the false detection caused by variant stepping modes and occasional disturbance, a novel robust step detection algorithm is proposed. The proposed algorithm detects the number of steps and zero-velocity phase using data from inertial sensors mounted on the user's foot. It includes two parts: a novel anti-fault detection algorithm and a novel zero-velocity detection method, which are used to reduce the over-detection and miss-detection of steps, respectively. Field tests show that the proposed approach is robust under varying motion speeds in different step modes for different persons and the average accuracy of the proposed algorithm is 99.94% in all motion modes, which significantly exceeds conventional estimation approaches in terms of step detection accuracy.

Hybrid Indoor Positioning System for Pedestrians With Swinging Arms Based on Smartphone IMU and RSSI of BLE

Over the last decades, researchers have invented many positioning algorithms for pedestrians in the indoor environment, such as trilateration, fingerprint, and pedestrian dead reckoning (PDR) system. However, indoor positioning is still difficult to solve with current technology. The fingerprint and trilateration based on received signal strength indicator (RSSI) usually require filters to stabilize the RSSI signal, which will cause a delay in positioning, which is fatal for real-time positioning. The PDR system with the inertial measurement unit (IMU) of smartphones usually requires the direction of the Y-axis of smartphones parallel with movement. Nevertheless, pedestrians will swing their arms while walking normally, which is not considered in the traditional PDR. In this case, a huge bias in heading estimation is inevitable, which causes the traditional PDR system to be unavailable during walking with swinging arms. In this article, a hybrid indoor positioning algorithm based on IMU and RSSI is proposed for pedestrians with swinging arms. The PDR system is improved by analyzing the characteristics of walking postures. In order to eliminate the cumulative error existing in the traditional PDR system, we propose the multipoint positioning algorithm based on RSSI for calibration. Combined with the Kalman filter, positioning delay and error have been effectively reduced for indoor location-based services (ILBSs).

Cooperative Positioning Method of Dual Foot-Mounted Inertial Pedestrian Dead Reckoning Systems

The inertial-based pedestrian dead reckoning (PDR) system assisted by zero-velocity update (ZUPT) algorithm has been widely researched, as it can independently provide effective pedestrian's position in indoor environment. However, in the realistic test, the system still often suffers from drift, due to the ZUPT algorithm's poor observation of system's heading. In this article, based on full analysis of the existing maximum range constraint algorithm between both-side feet, first, a constraint method based on the maximum distance of one-side foot has been proposed. It mainly uses the principle that the distance between the position of one-side foot at current time and that of the previous stationary instant should be within a maximum range. Second, a one-side foot and both-side feet cooperative maximum range constraint algorithm has been proposed. It mainly uses the one-side foot maximum range and the both-side feet maximum range to intersect the most likely position of pedestrian's current moving foot and then uses this position to constrain the heading divergence. Finally, to further improve the heading constraint accuracy, a maximum range decoupling algorithm has been proposed, which decouples the maximum ranges of one-side foot and both-side feet into the distance in the forward direction. Then, using this distance, the heading divergence can be constrained more effectively.

A Full-State Robust Extended Kalman Filter for Orientation Tracking During Long-Duration Dynamic Tasks Using Magnetic and Inertial Measurement Units.

Accurate and robust orientation estimation using magnetic and inertial measurement units (MIMUs) has been a challenge for many years in long-duration measurements of joint angles and pedestrian dead-reckoning systems and has limited several real-world applications of MIMUs. Thus, this research aimed at developing a full-state Robust Extended Kalman Filter (REKF) for accurate and robust orientation tracking with MIMUs, particularly during long-duration dynamic tasks. First, we structured a novel EKF by including the orientation quaternion, non-gravitational acceleration, gyroscope bias, and magnetic disturbance in the state vector. Next, the a posteriori error covariance matrix equation was modified to build a REKF. We compared the accuracy and robustness of our proposed REKF with four filters from the literature using optimal filter gains. We measured the thigh, shank, and foot orientation of nine participants while performing short- and long-duration tasks using MIMUs and a camera motion-capture system. REKF outperformed the filters from literature significantly (p < 0.05) in terms of accuracy and robustness for long-duration tasks. For example, for foot MIMU, the median RMSE of (roll, pitch, yaw) were (6.5, 5.5, 7.8) and (22.8, 23.9, 25) deg for REKF and the best filter from the literature, respectively. For short-duration trials, REKF achieved significantly (p < 0.05) better or similar performance compared to the literature. We concluded that including non-gravitational acceleration, gyroscope bias, and magnetic disturbance in the state vector, as well as using a robust filter structure, is required for accurate and robust orientation tracking, at least in long-duration tasks.