scholarly journals Smartphone-Based Inertial Odometry for Blind Walkers

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4033
Author(s):  
Peng Ren ◽  
Fatemeh Elyasi ◽  
Roberto Manduchi
Keyword(s):  
Particle Filtering ◽  
Inertial Sensors ◽  
State Of The Art ◽  
Inertial Sensor ◽  
Mean Shift ◽  
Gait Pattern ◽  
Comparative Assessment ◽  
Pedestrian Tracking ◽  
Step Counter ◽  
Assisted Navigation

Pedestrian tracking systems implemented in regular smartphones may provide a convenient mechanism for wayfinding and backtracking for people who are blind. However, virtually all existing studies only considered sighted participants, whose gait pattern may be different from that of blind walkers using a long cane or a dog guide. In this contribution, we present a comparative assessment of several algorithms using inertial sensors for pedestrian tracking, as applied to data from WeAllWalk, the only published inertial sensor dataset collected indoors from blind walkers. We consider two situations of interest. In the first situation, a map of the building is not available, in which case we assume that users walk in a network of corridors intersecting at 45° or 90°. We propose a new two-stage turn detector that, combined with an LSTM-based step counter, can robustly reconstruct the path traversed. We compare this with RoNIN, a state-of-the-art algorithm based on deep learning. In the second situation, a map is available, which provides a strong prior on the possible trajectories. For these situations, we experiment with particle filtering, with an additional clustering stage based on mean shift. Our results highlight the importance of training and testing inertial odometry systems for assisted navigation with data from blind walkers.

scholarly journals Self-Contained Pedestrian Tracking With Mems Sensors

2021 ◽  
Author(s):  
Chengliang Huang
Keyword(s):  
Particle Filtering ◽  
Stance Phase ◽  
Field Experiments ◽  
Inertial Sensors ◽  
Tracking System ◽  
Dead Reckoning ◽  
Pedestrian Tracking ◽  
Indoor Wireless ◽  
Wireless Positioning ◽  
Pedestrian Dead Reckoning

Due to the limitations of current indoor wireless positioning technologies, a novel positioning/tracking solution has to be explored and developed, in order to locate a person anywhere anytime without any infrastructure. The purpose of this thesis is to present the result of the first phase of a long-period research to find such a solution and develop a practical system. In this thesis, using inertial sensors for positioning of people is selected to replace wireless solutions, considering the development of micro-electromechanical systems. A sensing module consisting of accelerometers, rate gyroscopes and magnetometers used to monitor human kinetics. In order to make this proposal practical, a synergy of existing strapdown inertial navigation and pedestrian dead-reckoning is proposed to improve the accuracy of positioning. Furthermore, the cyclic alternation of stance phase and swing phase in human walking is used to reduce errors accumulating during projection and integration of sensed accelerometer signals. Other than the improvement of some existing methods to detect stance phase and reset the velocity, several new methods are proposed to remove the integral drift during both phases of a human stride. The algorithm to calculate heading of on the sensing module is also deduced to limit the integral drift of rate gyroscopes. All the methods and algorithms are applied in field experiments with carefully chosen sensing module mounted on human footwear. The results show promising accuracy of tracking, hence validate the feasibility of self-contained pedestrian tracking system with inertial sensors. Further work, especially with map correlation and particle filtering, will be done in the coming phases of the project to make the system applicable both outdoor and indoor.

scholarly journals Estimation and Error Analysis for Optomechanical Inertial Sensors

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6101
Author(s):  
Patrick Kelly ◽  
Manoranjan Majji ◽  
Felipe Guzmán
Keyword(s):  
Kalman Filter ◽  
Inertial Sensors ◽  
State Of The Art ◽  
Inertial Sensor ◽  
Measurement Noise ◽  
Sensor Model ◽  
Sensing Technology ◽  
Operation Period ◽  
Extended Operation ◽  
Calibration Step

A sensor model and methodology to estimate the forcing accelerations measured using a novel optomechanical inertial sensor with the inclusion of stochastic bias and measurement noise processes is presented. A Kalman filter for the estimation of instantaneous sensor bias is developed; the outputs from this calibration step are then employed in two different approaches for the estimation of external accelerations applied to the sensor. The performance of the system is demonstrated using simulated measurements and representative values corresponding to a bench-tested 3.76 Hz oscillator. It is shown that the developed methods produce accurate estimates of the bias over a short calibration step. This information enables precise estimates of acceleration over an extended operation period. These results establish the feasibility of reliably precise acceleration estimates using the presented methods in conjunction with state of the art optomechanical sensing technology.

scholarly journals Self-Contained Pedestrian Tracking With Mems Sensors

2021 ◽  
Author(s):  
Chengliang Huang
Keyword(s):  
Particle Filtering ◽  
Stance Phase ◽  
Field Experiments ◽  
Inertial Sensors ◽  
Tracking System ◽  
Dead Reckoning ◽  
Pedestrian Tracking ◽  
Indoor Wireless ◽  
Wireless Positioning ◽  
Pedestrian Dead Reckoning

Due to the limitations of current indoor wireless positioning technologies, a novel positioning/tracking solution has to be explored and developed, in order to locate a person anywhere anytime without any infrastructure. The purpose of this thesis is to present the result of the first phase of a long-period research to find such a solution and develop a practical system. In this thesis, using inertial sensors for positioning of people is selected to replace wireless solutions, considering the development of micro-electromechanical systems. A sensing module consisting of accelerometers, rate gyroscopes and magnetometers used to monitor human kinetics. In order to make this proposal practical, a synergy of existing strapdown inertial navigation and pedestrian dead-reckoning is proposed to improve the accuracy of positioning. Furthermore, the cyclic alternation of stance phase and swing phase in human walking is used to reduce errors accumulating during projection and integration of sensed accelerometer signals. Other than the improvement of some existing methods to detect stance phase and reset the velocity, several new methods are proposed to remove the integral drift during both phases of a human stride. The algorithm to calculate heading of on the sensing module is also deduced to limit the integral drift of rate gyroscopes. All the methods and algorithms are applied in field experiments with carefully chosen sensing module mounted on human footwear. The results show promising accuracy of tracking, hence validate the feasibility of self-contained pedestrian tracking system with inertial sensors. Further work, especially with map correlation and particle filtering, will be done in the coming phases of the project to make the system applicable both outdoor and indoor.

scholarly journals Multiple particle filtering for tracking wireless agents via Monte Carlo likelihood approximation

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Stephan Schlupkothen ◽  
Gerd Ascheid
Keyword(s):  
Monte Carlo ◽  
Computational Complexity ◽  
Particle Filtering ◽  
Gaussian Approximation ◽  
State Of The Art ◽  
Approximation Techniques ◽  
Accurate Localization ◽  
Likelihood Approximation ◽  
Current State ◽  
Multiple Particle

Abstract The localization of multiple wireless agents via, for example, distance and/or bearing measurements is challenging, particularly if relying on beacon-to-agent measurements alone is insufficient to guarantee accurate localization. In these cases, agent-to-agent measurements also need to be considered to improve the localization quality. In the context of particle filtering, the computational complexity of tracking many wireless agents is high when relying on conventional schemes. This is because in such schemes, all agents’ states are estimated simultaneously using a single filter. To overcome this problem, the concept of multiple particle filtering (MPF), in which an individual filter is used for each agent, has been proposed in the literature. However, due to the necessity of considering agent-to-agent measurements, additional effort is required to derive information on each individual filter from the available likelihoods. This is necessary because the distance and bearing measurements naturally depend on the states of two agents, which, in MPF, are estimated by two separate filters. Because the required likelihood cannot be analytically derived in general, an approximation is needed. To this end, this work extends current state-of-the-art likelihood approximation techniques based on Gaussian approximation under the assumption that the number of agents to be tracked is fixed and known. Moreover, a novel likelihood approximation method is proposed that enables efficient and accurate tracking. The simulations show that the proposed method achieves up to 22% higher accuracy with the same computational complexity as that of existing methods. Thus, efficient and accurate tracking of wireless agents is achieved.

scholarly journals Reference in-vitro dataset for inertial-sensor-to-bone alignment applied to the tibiofemoral joint

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ive Weygers ◽  
Manon Kok ◽  
Thomas Seel ◽  
Darshan Shah ◽  
Orçun Taylan ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Ground Truth ◽  
Transformation Matrices ◽  
True Model ◽  
Advance Research ◽  
Manual Palpation ◽  
Visualization Tools ◽  
Inertial Measurements

AbstractSkin-attached inertial sensors are increasingly used for kinematic analysis. However, their ability to measure outside-lab can only be exploited after correctly aligning the sensor axes with the underlying anatomical axes. Emerging model-based inertial-sensor-to-bone alignment methods relate inertial measurements with a model of the joint to overcome calibration movements and sensor placement assumptions. It is unclear how good such alignment methods can identify the anatomical axes. Any misalignment results in kinematic cross-talk errors, which makes model validation and the interpretation of the resulting kinematics measurements challenging. This study provides an anatomically correct ground-truth reference dataset from dynamic motions on a cadaver. In contrast with existing references, this enables a true model evaluation that overcomes influences from soft-tissue artifacts, orientation and manual palpation errors. This dataset comprises extensive dynamic movements that are recorded with multimodal measurements including trajectories of optical and virtual (via computed tomography) anatomical markers, reference kinematics, inertial measurements, transformation matrices and visualization tools. The dataset can be used either as a ground-truth reference or to advance research in inertial-sensor-to-bone-alignment.

scholarly journals Inertial Sensor Reliability and Validity for Static and Dynamic Balance in Healthy Adults: A Systematic Review

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5167
Author(s):  
Nicky Baker ◽  
Claire Gough ◽  
Susan J. Gordon
Keyword(s):  
Systematic Review ◽  
Discriminant Validity ◽  
Inertial Sensors ◽  
Postural Sway ◽  
Inertial Sensor ◽  
Dynamic Balance ◽  
Healthy Adults ◽  
Gait Velocity ◽  
Time Data ◽  
Targeted Interventions

Compared to laboratory equipment inertial sensors are inexpensive and portable, permitting the measurement of postural sway and balance to be conducted in any setting. This systematic review investigated the inter-sensor and test-retest reliability, and concurrent and discriminant validity to measure static and dynamic balance in healthy adults. Medline, PubMed, Embase, Scopus, CINAHL, and Web of Science were searched to January 2021. Nineteen studies met the inclusion criteria. Meta-analysis was possible for reliability studies only and it was found that inertial sensors are reliable to measure static standing eyes open. A synthesis of the included studies shows moderate to good reliability for dynamic balance. Concurrent validity is moderate for both static and dynamic balance. Sensors discriminate old from young adults by amplitude of mediolateral sway, gait velocity, step length, and turn speed. Fallers are discriminated from non-fallers by sensor measures during walking, stepping, and sit to stand. The accuracy of discrimination is unable to be determined conclusively. Using inertial sensors to measure postural sway in healthy adults provides real-time data collected in the natural environment and enables discrimination between fallers and non-fallers. The ability of inertial sensors to identify differences in postural sway components related to altered performance in clinical tests can inform targeted interventions for the prevention of falls and near falls.

scholarly journals Development and Experimental Evaluation of a Low-Cost Cooperative UAV Localization Network Prototype

2018 ◽  
Vol 7 (4) ◽  
pp. 42 ◽  
Author(s):  
Salil Goel ◽  
Allison Kealy ◽  
Bharat Lohani
Keyword(s):  
Information Sharing ◽  
Inertial Sensors ◽  
Low Cost ◽  
Wide Band ◽  
Cooperative Networks ◽  
Global Navigation Satellite Systems ◽  
Cooperative Localization ◽  
Cooperative Network ◽  
Precise Positioning ◽  
Assisted Navigation

Precise localization is one of the key requirements in the deployment of UAVs (Unmanned Aerial Vehicles) for any application including precision mapping, surveillance, assisted navigation, search and rescue. The need for precise positioning is even more relevant with the increasing automation in UAVs and growing interest in commercial UAV applications such as transport and delivery. In the near future, the airspace is expected to be occupied with a large number of unmanned as well as manned aircraft, a majority of which are expected to be operating autonomously. This paper develops a new cooperative localization prototype that utilizes information sharing among UAVs and static anchor nodes for precise positioning of the UAVs. The UAVs are retrofitted with low-cost sensors including a camera, GPS receiver, UWB (Ultra Wide Band) radio and low-cost inertial sensors. The performance of the low-cost prototype is evaluated in real-world conditions in partially and obscured GNSS (Global Navigation Satellite Systems) environments. The performance is analyzed for both centralized and distributed cooperative network designs. It is demonstrated that the developed system is capable of achieving navigation grade (2–4 m) accuracy in partially GNSS denied environments, provided a consistent communication in the cooperative network is available. Furthermore, this paper provides experimental validation that information sharing is beneficial to improve positioning performance even in ideal GNSS environments. The experiments demonstrate that the major challenges for low-cost cooperative networks are consistent connectivity among UAV platforms and sensor synchronization.

WTrack: HMM-based walk pattern recognition and indoor pedestrian tracking using phone inertial sensors

2014 ◽  
Vol 18 (8) ◽  
pp. 1901-1915 ◽  
Author(s):  
Xiaoguang Niu ◽  
Meng Li ◽  
Xiaohui Cui ◽  
Jin Liu ◽  
Shubo Liu ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Inertial Sensors ◽  
Pedestrian Tracking ◽  
Indoor Pedestrian Tracking

scholarly journals Gait Variability and Complexity during Single and Dual-Task Walking on Different Surfaces in Outdoor Environment

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4792
Author(s):  
Denisa Nohelova ◽  
Lucia Bizovska ◽  
Nicolas Vuillerme ◽  
Zdenek Svoboda
Keyword(s):  
Dual Task ◽  
Inertial Sensor ◽  
Cognitive Task ◽  
Real Life ◽  
Gait Pattern ◽  
Gait Variability ◽  
Outdoor Environment ◽  
Manual Task ◽  
Lower Trunk ◽  
Trunk Acceleration

Nowadays, gait assessment in the real life environment is gaining more attention. Therefore, it is desirable to know how some factors, such as surfaces (natural, artificial) or dual-tasking, influence real life gait pattern. The aim of this study was to assess gait variability and gait complexity during single and dual-task walking on different surfaces in an outdoor environment. Twenty-nine healthy young adults aged 23.31 ± 2.26 years (18 females, 11 males) walked at their preferred walking speed on three different surfaces (asphalt, cobbles, grass) in single-task and in two dual-task conditions (manual task—carrying a cup filled with water, cognitive task—subtracting the number 7). A triaxial inertial sensor attached to the lower trunk was used to record trunk acceleration during gait. From 15 strides, sample entropy (SampEn) as an indicator of gait complexity and root mean square (RMS) as an indicator of gait variability were computed. The findings demonstrate that in an outdoor environment, the surfaces significantly impacted only gait variability, not complexity, and that the tasks affected both gait variability and complexity in young healthy adults.

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