Development of a low cost wireless IMU using MEMS sensors for pedestrian navigation

Naeem, Muhammad; Arif, Sana; Qadeer, Nimra; Khan, Zeashan Hameed

doi:10.1109/c-code.2017.7918948

Cited by 3 publications

(7 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous authors have described the characteristics of existing systems [30]. A microelectromechanical system (MEMS) that has been detailed in a previous study [12]-the IMU Shimmer 3-has a three-axis gyroscope, a three-axis accelerometer, and a barometer with nine degrees of freedom. Such existing systems, however, have the limitation of high cost.…”

Section: Sensorsmentioning

confidence: 99%

“…Therefore, it is essential to add more value to pedestrian-restricted sensors [12]. The proposed indoor virtual beacon system includes three parts [3]: a monitoring access point, a location cloud service (which calculates the mobile device's location in nearby areas), and a proximity alert service (which triggers proximity-based alerts on the mobile device).…”

Section: Sensorsmentioning

confidence: 99%

“…Accelerometer [12,[34][35][36][37][38][39] Measures gravity, changes in capacitance, and acceleration and deceleration forces.…”

Section: Main Sensors In the Platformmentioning

confidence: 99%

“…Previous studies have focused on allocating many sensors in desired regions [11], resulting in problems related to power consumption. Pedestrian tracking sensors can be used to collect information, but require the Global Navigation Satellite System (GNSS) service [12]. Issues with mobile devices or failure to log out (e.g., from a work account) can expose such devices to dangerous privacy-related attacks [13].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Handheld Device-Based Indoor Localization with Zero Infrastructure (HDIZI)

Al-Sahly

Hassan

Saleem

et al. 2022

Sensors

View full text Add to dashboard Cite

The correlations between smartphone sensors, algorithms, and relevant techniques are major components facilitating indoor localization and tracking in the absence of communication and localization standards. A major research gap can be noted in terms of explaining the connections between these components to clarify the impacts and issues of models meant for indoor localization and tracking. In this paper, we comprehensively study the smartphone sensors, algorithms, and techniques that can support indoor localization and tracking without the need for any additional hardware or specific infrastructure. Reviews and comparisons detail the strengths and limitations of each component, following which we propose a handheld-device-based indoor localization with zero infrastructure (HDIZI) approach to connect the abovementioned components in a balanced manner. The sensors are the input source, while the algorithms are used as engines in an optimal manner, in order to produce a robust localizing and tracking model without requiring any further infrastructure. The proposed framework makes indoor and outdoor navigation more user-friendly, and is cost-effective for researchers working with embedded sensors in handheld devices, enabling technologies for Industry 4.0 and beyond. We conducted experiments using data collected from two different sites with five smartphones as an initial work. The data were sampled at 10 Hz for a duration of five seconds at fixed locations; furthermore, data were also collected while moving, allowing for analysis based on user stepping behavior and speed across multiple paths. We leveraged the capabilities of smartphones, through efficient implementation and the optimal integration of algorithms, in order to overcome the inherent limitations. Hence, the proposed HDIZI is expected to outperform approaches proposed in previous studies, helping researchers to deal with sensors for the purposes of indoor navigation—in terms of either positioning or tracking—for use in various fields, such as healthcare, transportation, environmental monitoring, or disaster situations.

show abstract

Section: Sensorsmentioning

confidence: 99%

Section: Sensorsmentioning

confidence: 99%

“…Accelerometer [12,[34][35][36][37][38][39] Measures gravity, changes in capacitance, and acceleration and deceleration forces.…”

Section: Main Sensors In the Platformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Handheld Device-Based Indoor Localization with Zero Infrastructure (HDIZI)

Al-Sahly

Hassan

Saleem

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Hence, the emergence of various high-precision indoor navigation technologies, including wireless signals [ 5 ], images [ 6 ], maps, etc. Among them, indoor pedestrian inertial navigation routes, based on inertial sensors, have attracted a lot of attention due to them being autonomous and not susceptible to external jamming [ 7 , 8 , 9 ]. Additionally, inertial sensors are becoming increasingly cheaper and more widely used in smart devices, which could support the use of pedestrian inertial navigation through the development of micro-electro-mechanical systems (MEMS).…”

Section: Introductionmentioning

confidence: 99%

A High-Precision Magnetic-Assisted Heading Angle Calculation Method Based on a 1D Convolutional Neural Network (CNN) in a Complicated Magnetic Environment

Wan

2020

Micromachines

View full text Add to dashboard Cite

Due to its widespread presence and independence from artificial signals, the application of geomagnetic field information in indoor pedestrian navigation systems has attracted extensive attention from researchers. However, for indoors environments, geomagnetic field signals can be severely disturbed by the complicated magnetic, leading to reduced positioning accuracy of magnetic-assisted navigation systems. Therefore, there is an urgent need for methods which screen out undisturbed geomagnetic field data for realizing the high accuracy pedestrian inertial navigation indoors. In this paper, we propose an algorithm based on a one-dimensional convolutional neural network (1D CNN) to screen magnetic field data. By encoding the magnetic data within a certain time window to a time series, a 1D CNN with two convolutional layers is designed to extract data features. In order to avoid errors arising from artificial labels, the feature vectors will be clustered in the feature space to classify the magnetic data using unsupervised methods. Our experimental results show that this method can distinguish the geomagnetic field data from indoors disturbed magnetic data well and further significantly improve the calculation accuracy of the heading angle. Our work provides a possible technical path for the realization of high-precision indoor pedestrian navigation systems.

show abstract

Human Motion Recognition Based On Inertial Sensor

Xing

Wei

Cao

et al. 2020

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

Human activity recognition (HAR) has attracted considerable research attentions from all walks of life due to its application in human-computer interaction, smart medical treatment and smart home health care. There are various ways of using different motion capture methods for HAR. Among which, HAR based on inertial sensor signals has recently emerged as a challenging and hot research topic. In this paper, we presented a inertial sensor-based method for HAR. Firstly, the variance, mean, information entropy, peak, etc. were extracted from the inertial data as robust features of the actions. Then, the robust features were further processed by Principal Component Analysis (PCA) to reduce the dimensions of features. Finally, the features with different actions label were used to train the Extreme Learning Machine (ELM) and Support Vector Machine (SVM) model. The result can achieve an mean accuracy of 83.49%, it also verified the effectiveness of our method.

show abstract

Development of a low cost wireless IMU using MEMS sensors for pedestrian navigation

Cited by 3 publications

References 17 publications

Handheld Device-Based Indoor Localization with Zero Infrastructure (HDIZI)

Handheld Device-Based Indoor Localization with Zero Infrastructure (HDIZI)

A High-Precision Magnetic-Assisted Heading Angle Calculation Method Based on a 1D Convolutional Neural Network (CNN) in a Complicated Magnetic Environment

Human Motion Recognition Based On Inertial Sensor

Contact Info

Product

Resources

About