HEAD: smootH Estimation of wAlking Direction with a handheld device embedding inertial, GNSS, and magnetometer sensors

Perul, Johan; Renaudin, Valérie

doi:10.1002/navi.389

Cited by 3 publications

(1 citation statement)

References 22 publications

(26 reference statements)

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“…It appeared interesting to make a comparison with an older solution based on classical signal processing techniques instead of recent AI tools: SmartWalk [41]. It is a pedestrian positioning algorithm fusing data from a tri-axis accelerometer, a tri-axis gyroscope, a tri-axis magnetometer, a barometer, and a GNSS receiver.…”

Section: Smartwalkmentioning

confidence: 99%

Analysis of the Recent AI for Pedestrian Navigation With Wearable Inertial Sensors

Renaudin

Kone

et al. 2023

J. Ind. Sea. Pos. Nav.

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Wearable devices embedding inertial sensors enable autonomous, seamless, and low-cost pedestrian navigation. Appealing it is, the approach faces several challenges: measurement noises, different device-carrying modes, different user dynamics, and individual walking characteristics. Recent research applies Artificial Intelligence (AI) to improve inertial navigation's robustness and accuracy. Our analysis identifies 2 main categories of AI approaches depending on the inertial signals segmentation: either using human gait events (steps or strides) or fixed-length inertial data segments. A theoretical analysis of the fundamental assumptions is carried out for each category. Two state-of-the-art AI algorithms (SELDA, RoNIN), representative of each category, and a gait-driven non-AI method (SmartWalk) are evaluated in a 2,17 km long open access dataset, representative of the diversity of pedestrians' mobility surroundings (open-sky, indoors, forest, urban, parking lot). SELDA is an AI-based stride length estimation algorithm, RoNIN is an AI-based positioning method, and SmartWalk is a gait-driven non-AI positioning method. The experimental assessment shows the distinct features in each category and their limits with respect to the underlying hypotheses. On average, SELDA, RoNIN, and SmartWalk achieve 8 m, 22 m, and 17 m average positioning errors (RMSE) respectively, on six testing tracks recorded with two volunteers in various environments.

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Section: Smartwalkmentioning

confidence: 99%

Analysis of the Recent AI for Pedestrian Navigation With Wearable Inertial Sensors

Renaudin

Kone

et al. 2023

J. Ind. Sea. Pos. Nav.

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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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Towards Smarter Positioning through Analyzing Raw GNSS and Multi-Sensor Data from Android Devices: A Dataset and an Open-Source Application

Grenier,

Lohan,

Ometov

et al. 2023

Electronics

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The state-of-the-art Android environment, available on a major market share of smartphones, provides an open playground for sensor data gathering. Moreover, the rise in new types of devices (e.g., wearables/smartwatches) is further extending the market opportunities with a variety of new sensor types. The existing implementations of biometric/medical sensors can allow the general public to directly access their health measurements, such as Electrocardiogram (ECG) or Oxygen Saturation (SpO2). This access greatly increases the possible applications of these devices with the combination of all the onboard sensors that are broadly in use nowadays. In this study, we look beyond the current state of the art into the positioning capacities of Android smart devices and wearables, with a focus on raw Global Navigation Satellite Systems (GNSS) measurements that are still mostly lacking in the research world. We develop a novel open-source Android application working in both smartphone and smartwatch environments for multi-sensor measurement data logging that also includes GNSS, an Inertial Navigation System (INS) magnetometer, and a barometer. Four smartphones and one smartwatch are used to perform surveys in different scenarios. The extraction of GNSS raw data from a wearable device has not been reported yet in the literature and no open-source app has existed so far for extracting GNSS data from wearables. Not only the developed app but also the results of these measurement surveys are provided as an open-access dataset. We start by defining our methodology and the acquisition protocol, and we dive into the structure of the dataset files. We also propose a first analysis of the data logged and evaluate the data according to several performance metrics. A discussion reviewing the capacities of smart devices for advanced positioning is proposed, as well as the current open challenges.

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HEAD: smootH Estimation of wAlking Direction with a handheld device embedding inertial, GNSS, and magnetometer sensors

Cited by 3 publications

References 22 publications

Analysis of the Recent AI for Pedestrian Navigation With Wearable Inertial Sensors

Analysis of the Recent AI for Pedestrian Navigation With Wearable Inertial Sensors

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

Towards Smarter Positioning through Analyzing Raw GNSS and Multi-Sensor Data from Android Devices: A Dataset and an Open-Source Application

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