Robust Pedestrian Dead Reckoning for Multiple Poses in Smartphones

Park, Soyoung; Lee, Jae Hong; Park, Chan Gook

doi:10.1109/access.2021.3070647

Cited by 18 publications

(6 citation statements)

References 44 publications

(49 reference statements)

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“…• RMS for roll and pitch and 0.5 • RMS for yaw. The step detection algorithm in [39] was also used. The true walking direction was calculated by differentiating the position obtained from the inertial navigation system (INS)/GNSS(RTK) solution of the MTi-680G at 1-second intervals, and the position was measured with an accuracy of CEP 1cm + 1ppm.…”

Section: A Experimental Setupmentioning

confidence: 99%

Smartwatch-Based Kinematic Walking Direction Estimation Using Paired Principal Component Analysis

Park,

Lee,

Park

et al. 2024

IEEE Access

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The dynamic behavior of pedestrians causes a misalignment problem between the sensor orientation and the walking direction, which hinders the performance of pedestrian dead reckoning (PDR) systems. Pedestrians wearing smartwatches are constantly faced with this problem when running. In this paper, we propose a novel kinematic modeling of arm swing that segments arm swing motion from the movement of the center of mass of the body. The proposed decomposition method allows for effective negation of the sensor outputs due to the redundant motion that obstructs the estimation of the true walking direction. The correct direction vector is computed by deducting the direction vector of the arm swing from that of the entire motion, which are both derived from performing two separate principal component analyses (PCA). The performance of the proposed method was evaluated through several experiments. In the running track experiment, the proposed method demonstrates the best performance, with 57% -70% performance improvement compared to the existing methods. In the general scenario involving both walking and running, the proposed method outperforms the baseline method by 56%, improving the generality of the PCA-based methods.INDEX TERMS Gait kinematics, inertial sensors, pedestrian dead reckoning (PDR), principal component analysis (PCA), smartwatch, walking direction estimation.

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Section: A Experimental Setupmentioning

confidence: 99%

Smartwatch-Based Kinematic Walking Direction Estimation Using Paired Principal Component Analysis

Park,

Lee,

Park

et al. 2024

IEEE Access

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“…With IMU sensors, the PDR method estimates the step length and the heading of the device user or pedestrian. It can then obtain pedestrian-relative locations based on the previous position or a known position [ 24 ]. The advantage of the PDR method is that it has a low cost of installation and does not require extra UD sensors [ 25 ].…”

Section: Ilarfmentioning

confidence: 99%

An Indoor Location-Based Augmented Reality Framework

Jiang

Subakti

2023

Sensors

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This paper proposes an indoor location-based augmented reality framework (ILARF) for the development of indoor augmented-reality (AR) systems. ILARF integrates an indoor localization unit (ILU), a secure context-aware message exchange unit (SCAMEU), and an AR visualization and interaction unit (ARVIU). The ILU runs on a mobile device such as a smartphone and utilizes visible markers (e.g., images and text), invisible markers (e.g., Wi-Fi, Bluetooth Low Energy, and NFC signals), and device sensors (e.g., accelerometers, gyroscopes, and magnetometers) to determine the device location and direction. The SCAMEU utilizes a message queuing telemetry transport (MQTT) server to exchange ambient sensor data (e.g., temperature, light, and humidity readings) and user data (e.g., user location and user speed) for context-awareness. The unit also employs a web server to manage user profiles and settings. The ARVIU uses AR creation tools to handle user interaction and display context-aware information in appropriate areas of the device’s screen. One prototype AR app for use in gyms, Gym Augmented Reality (GAR), was developed based on ILARF. Users can register their profiles and configure settings when using GAR to visit a gym. Then, GAR can help users locate appropriate gym equipment based on their workout programs or favorite exercise specified in their profiles. GAR provides instructions on how to properly use the gym equipment and also makes it possible for gym users to socialize with each other, which may motivate them to go to the gym regularly. GAR is compared with other related AR systems. The comparison shows that GAR is superior to others by virtue of its use of ILARF; specifically, it provides more information, such as user location and direction, and has more desirable properties, such as secure communication and a 3D graphical user interface.

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“…PDR was initially used in those GNSS-denied environments, such as indoor positioning. In the last decade, a lot of literature [ 20 , 21 , 22 ] involving PDR has been published, and these previous works provided in-depth analysis and innovations, such as stride length estimation and heading estimation. Due to the fact that GNSS observation open access is a few years later than MEMS MARG integration into smartphones, early research on GNSS/PDR fusion commonly used professional- or vehicle-grade GNSS receivers, such as u-blox.…”

Section: Related Workmentioning

confidence: 99%

Multi-Phase Fusion for Pedestrian Localization Using Mass-Market GNSS and MEMS Sensors

Liu

Ying

Dai

et al. 2023

Sensors

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Precise pedestrian positioning based on smartphone-grade sensors has been a research hotspot for several years. Due to the poor performance of the mass-market Micro-Electro-Mechanical Systems (MEMS) Magnetic, Angular Rate, and Gravity (MARG) sensors, the standalone pedestrian dead reckoning (PDR) module cannot avoid long-time heading drift, which leads to the failure of the entire positioning system. In outdoor scenes, the Global Navigation Satellite System (GNSS) is one of the most popular positioning systems, and smartphone users can use it to acquire absolute coordinates. However, the smartphone’s ultra-low-cost GNSS module is limited by some components such as the antenna, and so it is susceptible to serious interference from the multipath effect, which is a main error source of smartphone-based GNSS positioning. In this paper, we propose a multi-phase GNSS/PDR fusion framework to overcome the limitations of standalone modules. The first phase is to build a pseudorange double-difference based on smartphone and reference stations, the second phase proposes a novel multipath mitigation method based on multipath partial parameters estimation (MPPE) and a Double-Difference Code-Minus-Carrier (DDCMC) filter, and the third phase is to propose the joint stride lengths and heading estimations of the two standalone modules, to reduce the long-time drift and noise. The experimental results demonstrate that the proposed multipath error estimation can effectively suppress the double-difference multipath error exceeding 4 m, and compared to other methods, our fusion method achieves a minimum error RMSE of 1.63 m in positioning accuracy, and a minimum error RMSE of 4.71 m in long-time robustness for 20 min of continuous walking.

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Robust Pedestrian Dead Reckoning for Multiple Poses in Smartphones

Cited by 18 publications

References 44 publications

Smartwatch-Based Kinematic Walking Direction Estimation Using Paired Principal Component Analysis

Smartwatch-Based Kinematic Walking Direction Estimation Using Paired Principal Component Analysis

An Indoor Location-Based Augmented Reality Framework

Multi-Phase Fusion for Pedestrian Localization Using Mass-Market GNSS and MEMS Sensors

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