A step length estimation method based on frequency domain feature analysis and gait recognition for pedestrian dead reckoning

The demand for pedestrian navigation has increased along with the rapid progress in mobile and wearable devices. This study develops an accurate and usable Step Length Estimation (SLE) method for a Pedestrian Dead Reckoning (PDR) system with features including a wide range of step lengths, a self-contained system, and real-time computing, based on the multi-sensor fusion and Fuzzy Logic (FL) algorithms. The wide-range SLE developed in this study was achieved by using a knowledge-based method to model the walking patterns of the user. The input variables of the FL are step strength and frequency, and the output is the estimated step length. Moreover, a waist-mounted sensor module has been developed using low-cost inertial sensors. Since low-cost sensors suffer from various errors, a calibration procedure has been utilized to improve accuracy. The proposed PDR scheme in this study demonstrates its ability to be implemented on waist-mounted devices in real time and is suitable for the indoor and outdoor environments considered in this study without the need for map information or any pre-installed infrastructure. The experiment results show that the maximum distance error was within 1.2% of 116.51 m in an indoor environment and was 1.78% of 385.2 m in an outdoor environment.

Pedestrian dead reckoning (PDR) is the critical component in indoor pedestrian tracking and navigation services. While most of the recent PDR solutions exploit in-built inertial sensors in smartphones for next step estimation, due to measurement errors and sensing drift, the accuracy of walking direction, step detection, and step length estimation cannot be guaranteed, leading to large accumulative tracking errors. In this paper, we propose a radar-assisted PDR scheme, called RadarPDR, which integrates a frequency-modulation continuous-wave (FMCW) radar to assist the inertial sensors-based PDR. We first establish a segmented wall distance calibration model to deal with the radar ranging noise caused by irregular indoor building layouts and fuse wall distance estimation with acceleration and azimuth signals measured by the inertial sensors of a smartphone. We also propose a hierarchical particle filter(PF) together with an extended Kalman filter for position and trajectory adjustment. Experiments have been conducted in practical indoor scenarios. Results demonstrate that the proposed RadarPDR is efficient and stable and outperforms the widely used inertial sensors-based PDR scheme.

In this paper, a comparison of IMU-based step length and heading estimation methods for indoor environments is presented. We compared two step detection methods: detecting the stance phase in each stride and detecting negative peaks in the horizontal acceleration filtered signal. Likewise, three step length estimation methods were compared: Zero Velocity Update (ZUPT), step frequency-based and step length parameter-based. Finally, the comparison of two heading estimation methods is presented: Heuristic Heading Reduction (HHR) and Zero Angular Rate update (ZARU). The yaw obtained from the sensor was corrected and compared with both heading estimation methods. All methods were tested at high, normal and low walking frequencies. Both step detections methods showed a good performance at normal and high step frequencies. The step length estimation method that depend of a linear parameter presented an error of 0.3m in a route of 96m and was the one with lowest error. The yaw corrected presents the lowest error of 3° in turns of 90°.

Pedestrian Dead Reckoning (PDR), which comes with many sensors integrated into widely available smartphones, is known as one of the most popular indoor positioning techniques. Sensors such as accelerometers, gyroscopes, and magnetometers are used to determine three important components in PDR: step detection, step length estimation, and heading estimation. Among them, the last component is the most challenging since a small heading error accumulates to produce a very large positioning error, especially when the pedestrian holds the smartphone in unconstrained styles such as swinging the phone freely along the pedestrian’s walking direction or putting the phone into the pants’ front pockets. The research proposes an adaptive heading estimation method to deal with heading errors caused by smartphone holding styles. The novelties are described as follows. Firstly, the proposed method attempts to classify the four basic smartphone holding styles using a machine learning algorithm based on simple features of acceleration values to give pedestrians more freedom during the walking period. Secondly, the proposed method adaptively combines the two heading estimation methods, which are calculated from the integrated sensors, to determine the walking direction for different smartphone holding styles. The experimental results show that the proposed heading estimation method achieves average heading errors of less than 30 degrees when testing in two different walking paths with the smartphone held in dynamic styles. It helps to reduce the heading errors by more than 15% compared to previous heading estimation methods.

This paper presents an extension to an indoor positioning system leveraging the camera which is built into current mobile phones to detect steps. Current indoor positioning systems on mobile phones based on pedestrian dead reckoning (PDR) often rely on step length and step frequency estimation. Usually the accelerometer is used to determine these values. Depending on the attitude of the device or the smoothness of the movement sometimes steps are not correctly detected. We propose to use the camera as additional sensor element to detect the forward section of both feet appear and disappear in the camera image and detect the steps based on this information. A comparison to an accelerometer based approach is provided.

Smartphone mode classification is essential to many applications, such as daily life monitoring, healthcare, and indoor positioning. In the latter, it was shown that knowledge of the smartphone location on pedestrians can improve the positioning accuracy. Most of the research conducted in this field is focused on pedestrian motion in a horizontal plane. In this research, we use supervised machine learning techniques to recognize and classify the smartphone mode (text, talk, pocket and swing) while accounting for the movement up and downstairs. We distinguish between the going up and the down motion, each with four different smartphone modes, making eight states in total. This classification is based on the use of an optimal set of sensors that varies according to battery life and the energy consumption of each sensor. The classifier was trained and tested on a dataset constructed from multiple user measurements (total of 94 min) to achieve robustness. This provided an accuracy of more than 90% in the cross validation method and 91.5% if the texting mode is excluded. When considering only stairs motion, regardless of the direction, the accuracy improves to 97%. These results may assist many algorithms, mainly in pedestrian dead reckoning, in improving a variety of challenges such as speed and step length estimation and cumulative error reduction.

Accurate indoor localization estimation has important social and commercial values, such as indoor location services and pedestrian retention times. Acoustic-based methods can achieve high localization accuracies in specific scenarios with special equipment; however, it is a challenge to obtain accurate localization with general equipment in indoor environments. To solve this problem, we propose a novel fusion CHAN and the improved pedestrian dead reckoning (PDR) indoor localization system (CHAN-IPDR-ILS). In this system, we propose a step length estimation method that adds the previous two steps for extracting more accurate information to estimate the current step length. The maximum influence factor is set for the previous two steps to ensure the importance of the current step length. We also propose a heading direction correction method to mitigate the errors in sensor data. Finally, pedestrian localization is achieved using a motion model with acoustic estimation and dynamic improved PDR estimation. In the fusion localization, the threshold and confidence level of the distance between estimation base-acoustic and improved PDR estimation are set to mitigate accidental and cumulative errors. The experiments were performed at trial sites with different users, devices, and scenarios, and experimental results demonstrate that the proposed method can achieve a higher accuracy compared with the state-of-the-art methods. The proposed fusion localization system manages equipment heterogeneity and provides generality and flexibility with different devices and scenarios at a low cost.

Walking/gait speed is a key measure for daily mobility characterization. To date, various studies have attempted to design algorithms to estimate walking speed using an inertial sensor worn on the lower back, which is considered as a proper location for activity monitoring in daily life. However, these algorithms were rarely compared and validated on the same datasets, including people with different preferred walking speed. This study implemented several original, improved, and new algorithms for estimating cadence, step length and eventually speed. We designed comprehensive cross-validation to compare the algorithms for walking slow, normal, fast, and using walking aids. We used two datasets, including reference data for algorithm validation from an instrumented mat (40 subjects) and shanks-worn inertial sensors (88 subjects), with normal and impaired walking patterns. The results showed up to 50% performance improvements. Training of algorithms on data from people with different preferred speeds led to better performance. For the slow walkers, an average RMSE of 2.5 steps/min, 0.04 m, and 0.10 m/s were respectively achieved for cadence, step length, and speed estimation. For normal walkers, the errors were 3.5 steps/min, 0.08 m, and 0.12 m/s. An average RMSE of 1.3 steps/min, 0.05 m, and 0.10 m/s were also observed on fast walkers. For people using walking aids, the error significantly increased up to an RMSE of 14 steps/min, 0.18 m, and 0.27 m/s. The results demonstrated the robustness of the proposed combined speed estimation approach for different speed ranges. It achieved an RMSE of 0.10, 0.18, 0.15, and 0.32 m/s for slow, normal, fast, and using walking aids, respectively.

BackgroundThe estimation of the spatio-temporal gait parameters is of primary importance in both physical activity monitoring and clinical contexts. A method for estimating step length bilaterally, during level walking, using a single inertial measurement unit (IMU) attached to the pelvis is proposed. In contrast to previous studies, based either on a simplified representation of the human gait mechanics or on a general linear regressive model, the proposed method estimates the step length directly from the integration of the acceleration along the direction of progression.MethodsThe IMU was placed at pelvis level fixed to the subject's belt on the right side. The method was validated using measurements from a stereo-photogrammetric system as a gold standard on nine subjects walking ten laps along a closed loop track of about 25 m, varying their speed. For each loop, only the IMU data recorded in a 4 m long portion of the track included in the calibrated volume of the SP system, were used for the analysis. The method takes advantage of the cyclic nature of gait and it requires an accurate determination of the foot contact instances. A combination of a Kalman filter and of an optimally filtered direct and reverse integration applied to the IMU signals formed a single novel method (Kalman and Optimally filtered Step length Estimation - KOSE method). A correction of the IMU displacement due to the pelvic rotation occurring in gait was implemented to estimate the step length and the traversed distance.ResultsThe step length was estimated for all subjects with less than 3% error. Traversed distance was assessed with less than 2% error.ConclusionsThe proposed method provided estimates of step length and traversed distance more accurate than any other method applied to measurements obtained from a single IMU that can be found in the literature. In healthy subjects, it is reasonable to expect that, errors in traversed distance estimation during daily monitoring activity would be of the same order of magnitude of those presented.

Substations are critical infrastructures in modern power systems, requiring accurate and reliable personnel positioning to ensure operational safety and inspection efficiency. Pedestrian dead reckoning (PDR) technology has emerged as a preferred solution for real-time seamless positioning due to its autonomous, anti-jamming and passive characteristics. However, in practical applications, the PDR algorithm faces challenges such as the inability to provide absolute positioning due to unknown starting points and cumulative errors over time. To address these challenges, this paper proposes a head-mounted pedestrian positioning system (HPPS) integrating fisheye camera/real-time kinematic (RTK)/PDR based on factor graph optimization (FGO). First, a skyward-facing fisheye camera identifies non-line-of-sight (NLOS) signals caused by architectural obstructions, improving the absolute positioning accuracy of RTK. Next, an adaptive pedestrian gait detection and step length estimation algorithm based on head-mounted inertial measurement units (IMU) is developed to enhance PDR robustness. Finally, the FGO framework integrates the inputs of the fisheye camera, RTK, and PDR to achieve seamless indoor-outdoor positioning. Experimental results demonstrate that the proposed system achieves horizontal root mean square error (RMSE) values of less than 0.42 m and 0.35 m in two distinct outdoor substation environments and less than 1.36 m in indoor-outdoor scenarios. This work highlights the integration of precise positioning technologies within the Internet of Things (IoT) framework, advancing smart grid applications and enabling effective location tracking in complex environments.

In recent years pedestrian dead reckoning (PDR) has been a popular indoor localization technique that takes advantage of inertial navigation sensors (INS) of smartphones due to its availability. Despite the convenience of INS, noise has been a recurring problem in PDR which encourages many researches on noise filtering. This paper proposes a model that utilizes empirical mode decomposition (EMD) as the noise filter. EMD is an unconventional filter being applied in PDR to remove noise by processing the raw signal before implementing step counts, step lengths and heading estimation. The experiment made using the proposed model was able to perform the standard PDR technique without overlaps in heading estimation.

Pedestrian Dead Reckoning (PDR) is a good alternative positioning method in Global Navigation Satellite Systems (GNSS)-challenged environments. It has the advantage in terms of infrastructure-independent, while the disadvantage is that, the estimated errors grow rapidly with time. The Digital Terrestrial Multimedia Broadcasting (DTMB) signals have strong transmission power, which contributes to better urban propagation and building penetration than GNSS signals. So, in this paper, an enhanced PDR algorithm aided with DTMB signals is proposed. In the study, a complete software-defined radio receiver is developed to estimate the Doppler speed and range information from DTMB signals. To reduce the accumulated errors of the PDR, the Extended Kalman Filter (EKF) algorithm is carried out to fuse the information of Doppler speed and ranging from the DTMB signals, as well as the information of walking speed and heading from the PDR. The results of the field tests showed that the 95% positioning error of the proposed algorithm is less than 3.94 m, while the PDR only is less than 9.19 m, which suggests the effectiveness of the proposed method to provide continuous positioning by fusing DTMB signals with PDR.

Repeated abobotulinumtoxinA injections benefit walking speed, step length and cadence in adults with spastic hemiparesis due to stroke or traumatic brain injury

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.

Pedestrian dead reckoning (PDR) uses built-in sensors in smartphones to track user positions, offering both versatility and portability. However, diversities among individuals and their behavior patterns decrease positioning accuracy. In this study, a two-stage personalized pedestrian dead reckoning based on neural networks (P2Net) is proposed. The first stage employs a human activity and smartphone location recognition (HSR) module, integrating a convolutional neural network with focal loss (FLCNN) to recognize nine modes, which constrains the subsequent optimized PDR procedure. In the second stage, a hybrid feature temporal attention network (HFTAN) is constructed to achieve generalized step length estimation across individual diversities. Temporal features via temporal convolutional network (TCN) and physical features are combined to generate hybrid features with time-series modeling capability and interpretability, which are then fed into a bi-directional long short-term memory (BiLSTM) model with a feature attention mechanism to estimate pedestrian step length. Experimental results demonstrate that P2Net achieves a class average accuracy of 92.75% for nine modes, and the total traveled distance error (TTDE) under various states is within 10%, outperforming other mentioned positioning methods.