Indoor Trajectory Reconstruction of Walking, Jogging, and Running Activities Based on a Foot-Mounted Inertial Pedestrian Dead-Reckoning System

Ceron, Jesus D.; Martindale, Christine F.; López, Diego M.; Kluge, Felix; Eskofier, Bjoern M.

doi:10.3390/s20030651

Cited by 6 publications

(5 citation statements)

References 30 publications

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“…In this work, only the data taken from the IMU placed on the left foot were used. In this component, our method previously published for the strides’ detection and the estimation of their length and orientation was used [ 22 ]. The result is a measurement of movement

, which consists of an estimate of the person’s new position represented by a point on the x-y-z coordinate plane.…”

Section: Proposed Methodsmentioning

confidence: 99%

Simultaneous Indoor Pedestrian Localization and House Mapping Based on Inertial Measurement Unit and Bluetooth Low-Energy Beacon Data

Ceron

Kluge

Küderle

et al. 2020

Sensors

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Indoor location estimation is crucial to provide context-based assistance in home environments. In this study, a method for simultaneous indoor pedestrian localization and house mapping is proposed and evaluated. The method fuses a person’s movement data from an Inertial Measurement Unit (IMU) with proximity and activity-related data from Bluetooth Low-Energy (BLE) beacons deployed in the indoor environment. The person’s and beacons’ localization is performed simultaneously using a combination of particle and Kalman Filters. We evaluated the method using data from eight participants who performed different activities in an indoor environment. As a result, the average participant’s localization error was 1.05 ± 0.44 m, and the average beacons’ localization error was 0.82 ± 0.24 m. The proposed method is able to construct a map of the indoor environment by localizing the BLE beacons and simultaneously locating the person. The results obtained demonstrate that the proposed method could point to a promising roadmap towards the development of simultaneous localization and home mapping system based only on one IMU and a few BLE beacons. To the best of our knowledge, this is the first method that includes the beacons’ data movement as activity-related events in a method for pedestrian Simultaneous Localization and Mapping (SLAM).

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, which consists of an estimate of the person’s new position represented by a point on the x-y-z coordinate plane.…”

Section: Proposed Methodsmentioning

confidence: 99%

Simultaneous Indoor Pedestrian Localization and House Mapping Based on Inertial Measurement Unit and Bluetooth Low-Energy Beacon Data

Ceron

Kluge

Küderle

et al. 2020

Sensors

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“…In Equation (10), ω ll is the right angular velocity of the left leg, and ω rl is the right angular velocity of the right leg. The leg angular velocity and gait segmentation are shown in Figure 4.…”

Section: Recognition Algorithms For Dynamic and Static Modementioning

confidence: 99%

“…Eric Foxlin [8] first studied the pedestrian navigation technology based on ZUPT of foot, which can effectively suppress the error divergence of inertial navigation system; In 2016, Tian, X. [9] et al studied and established the functional relationship between the zero-velocity detection threshold and the step frequency, which can accurately recognize the zero-velocity interval of walking under different step frequencies; In 2020, Jesus D. Ceron et al [10] proposed a foot-mounted navigation and positioning algorithm for walking, running and jogging. The positioning accuracy of the algorithm using complementary filtering is 8.8%; In 2021, Li, W. [11] studied a ZUPT navigation algorithm based on the constraint of biped maximum distance inequality.…”

Section: Introductionmentioning

confidence: 99%

A Method for Autonomous Multi-Motion Modes Recognition and Navigation Optimization for Indoor Pedestrian

Wang

Xiong

et al. 2022

Sensors

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The indoor navigation method shows great application prospects that is based on a wearable foot-mounted inertial measurement unit and a zero-velocity update principle. Traditional navigation methods mainly support two-dimensional stable motion modes such as walking; special tasks such as rescue and disaster relief, medical search and rescue, in addition to normal walking, are usually accompanied by running, going upstairs, going downstairs and other motion modes, which will greatly affect the dynamic performance of the traditional zero-velocity update algorithm. Based on a wearable multi-node inertial sensor network, this paper presents a method of multi-motion modes recognition for indoor pedestrians based on gait segmentation and a long short-term memory artificial neural network, which improves the accuracy of multi-motion modes recognition. In view of the short effective interval of zero-velocity updates in motion modes with fast speeds such as running, different zero-velocity update detection algorithms and integrated navigation methods based on change of waist/foot headings are designed. The experimental results show that the overall recognition rate of the proposed method is 96.77%, and the navigation error is 1.26% of the total distance of the proposed method, which has good application prospects.

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“…Considering the use of ML in shallow architecture, the authors in [144]- [146] implement SVM for motion recognition, achieving an accuracy between 85% to 99%. The authors in [152] use RF to classify the motion of a pedestrian using inertial information and images.…”

Section: Machine Learning In Inertial Navigation Systemsmentioning

confidence: 99%

A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

et al. 2021

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With the popularization of machine learning (ML) techniques and the increased chipset's performance, the application of ML to pedestrian localization systems has received significant attention in the last years. Several survey papers have attempted to provide a state-of-the-art overview, but they usually limit their scope to a particular type of positioning system or technology. In addition, they are written from the point of view of ML techniques and their practice, not from the point of view of the localization system and the specific problems that ML techniques can help to solve. This article is intended to offer a comprehensive state-of-the-art survey of the ML techniques that have been adopted over the last ten years to improve the performance of pedestrian localization systems, addressing the applicability of ML techniques in this domain, along with the main localization strategies. It concludes by indicating the underlying open issues and challenges associated with the existing systems, and possible future directions in which ML techniques could improve the performance of pedestrian localization systems. Among other open issues, most previous authors have focused their attention on position estimation accuracy, which wastes the potential of ML techniques to improve other performance parameters (e.g., response time, computational complexity, robustness, scalability or energy efficiency). This study shows that there is a strong trend towards the application of supervised learning. Consequently, there are many potential research opportunities in the use of other learning types, such as unsupervised and reinforcement learning, to improve the performance of pedestrian localization systems.

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Indoor Trajectory Reconstruction of Walking, Jogging, and Running Activities Based on a Foot-Mounted Inertial Pedestrian Dead-Reckoning System

Cited by 6 publications

References 30 publications

Simultaneous Indoor Pedestrian Localization and House Mapping Based on Inertial Measurement Unit and Bluetooth Low-Energy Beacon Data

Simultaneous Indoor Pedestrian Localization and House Mapping Based on Inertial Measurement Unit and Bluetooth Low-Energy Beacon Data

A Method for Autonomous Multi-Motion Modes Recognition and Navigation Optimization for Indoor Pedestrian

A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

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