scholarly journals Highly Accurate Step Counting at Various Walking States Using Low-Cost Inertial Measurement Unit Support Indoor Positioning System

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3186 ◽  
Author(s):  
Van Pham ◽  
Duc Nguyen ◽  
Nhu Dang ◽  
Hong Pham ◽  
Van Tran ◽  
...  
Keyword(s):  
Low Cost ◽  
Indoor Positioning ◽  
Measurement Unit ◽  
Health Apps ◽  
Average Accuracy ◽  
Step Counting ◽  
Health Monitoring Systems ◽  
Commercial Applications ◽  
Public Datasets ◽  
Indoor Positioning System

Accurate step counting is essential for indoor positioning, health monitoring systems, and other indoor positioning services. There are several publications and commercial applications in step counting. Nevertheless, over-counting, under-counting, and false walking problems are still encountered in these methods. In this paper, we propose to develop a highly accurate step counting method to solve these limitations by proposing four features: Minimal peak distance, minimal peak prominence, dynamic thresholding, and vibration elimination, and these features are adaptive with the user’s states. Our proposed features are combined with periodicity and similarity features to solve false walking problem. The proposed method shows a significant improvement of 99.42% and 96.47% of the average of accuracy in free walking and false walking problems, respectively, on our datasets. Furthermore, our proposed method also achieves the average accuracy of 97.04% on public datasets and better accuracy in comparison with three commercial step counting applications: Pedometer and Weight Loss Coach installed on Lenovo P780, Health apps in iPhone 5s (iOS 10.3.3), and S-health in Samsung Galaxy S5 (Android 6.01).

scholarly journals Indoor Localization System Using Wireless Sensor Network

2018 ◽  
pp. 29-38
Keyword(s):  
Indoor Localization ◽  
Low Cost ◽  
Wide Band ◽  
Human Movement ◽  
Indoor Positioning ◽  
Positioning System ◽  
Wireless System ◽  
Anchor Nodes ◽  
Indoor Positioning System ◽  
Better Than

Building a precise low cost indoor positioning and navigation wireless system is a challenging task. The accuracy and cost should be taken together into account. Especially, when we need a system to be built in a harsh environment. In recent years, several researches have been implemented to build different indoor positioning system (IPS) types for human movement using wireless commercial sensors. The aim of this paper is to prove that it is not always the case that having a larger number of anchor nodes will increase the accuracy. Two and three anchor nodes of ultra-wide band with or without the commercial devices (DW 1000) could be implemented in this work to find the Localization of objects in different indoor positioning system, for which the results showed that sometimes three anchor nodes are better than two and vice versa. It depends on how to install the anchor nodes in an appropriate scenario that may allow utilizing a smaller number of anchors while maintaining the required accuracy and cost.

scholarly journals A Low Cost Indoor Positioning System Using Bluetooth Low Energy

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 136858-136871
Author(s):  
Lu Bai ◽  
Fabio Ciravegna ◽  
Raymond Bond ◽  
Maurice Mulvenna
Keyword(s):  
Low Cost ◽  
Indoor Positioning ◽  
Low Energy ◽  
Positioning System ◽  
Bluetooth Low Energy ◽  
Indoor Positioning System

scholarly journals Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1401
Author(s):  
Haq Nawaz ◽  
Ahsen Tahir ◽  
Nauman Ahmed ◽  
Ubaid U. Fayyaz ◽  
Tayyeb Mahmood ◽  
...  
Keyword(s):  
Low Power ◽  
Indoor Localization ◽  
Low Cost ◽  
Smart Homes ◽  
Indoor Positioning ◽  
High Accuracy ◽  
Positioning System ◽  
Telemetry Data ◽  
Ultra Low Power ◽  
Indoor Positioning System

Global navigation satellite systems have been used for reliable location-based services in outdoor environments. However, satellite-based systems are not suitable for indoor positioning due to low signal power inside buildings and low accuracy of 5 m. Future smart homes demand low-cost, high-accuracy and low-power indoor positioning systems that can provide accuracy of less than 5 m and enable battery operation for mobility and long-term use. We propose and implement an intelligent, highly accurate and low-power indoor positioning system for smart homes leveraging Gaussian Process Regression (GPR) model using information-theoretic gain based on reduction in differential entropy. The system is based on Time Difference of Arrival (TDOA) and uses ultra-low-power radio transceivers working at 434 MHz. The system has been deployed and tested using indoor measurements for two-dimensional (2D) positioning. In addition, the proposed system provides dual functionality with the same wireless links used for receiving telemetry data, with configurable data rates of up to 600 Kbauds. The implemented system integrates the time difference pulses obtained from the differential circuitry to determine the radio frequency (RF) transmitter node positions. The implemented system provides a high positioning accuracy of 0.68 m and 1.08 m for outdoor and indoor localization, respectively, when using GPR machine learning models, and provides telemetry data reception of 250 Kbauds. The system enables low-power battery operation with consumption of <200 mW power with ultra-low-power CC1101 radio transceivers and additional circuits with a differential amplifier. The proposed system provides low-cost, low-power and high-accuracy indoor localization and is an essential element of public well-being in future smart homes.

scholarly journals Wi-Fi-Based Effortless Indoor Positioning System Using IoT Sensors

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1496 ◽  
Author(s):  
Muhammad Ali ◽  
Soojung Hur ◽  
Yongwan Park
Keyword(s):  
Indoor Environment ◽  
Automatic Generation ◽  
Indoor Positioning ◽  
Propagation Loss ◽  
Positioning System ◽  
Site Analysis ◽  
Raster Maps ◽  
Average Accuracy ◽  
Online Feedback ◽  
Indoor Positioning System

Wi-Fi positioning based on fingerprinting has been considered as the most widely used technology in the field of indoor positioning. The fingerprinting database has been used as an essential part of the Wi-Fi positioning system. However, the offline phase of the calibration involves a laborious task of site analysis which involves costs and a waste of time. We offer an indoor positioning system based on the automatic generation of radio maps of the indoor environment. The proposed system does not require any effort and uses Wi-Fi compatible Internet-of-Things (IoT) sensors. Propagation loss parameters are automatically estimated from the online feedback of deployed sensors and the radio maps are updated periodically without any physical intervention. The proposed system leverages the raster maps of an environment with the wall information only, against computationally extensive techniques based on vector maps that require precise information on the length and angles of each wall. Experimental results show that the proposed system has achieved an average accuracy of 2 m, which is comparable to the survey-based Wi-Fi fingerprinting technique.

Indoor Positioning Using FM Radio

2012 ◽  
pp. 199-211
Author(s):  
Andrei Papliatseyeu ◽  
Venet Osmani ◽  
Oscar Mayora
Keyword(s):  
Experimental Study ◽  
High Performance ◽  
Short Range ◽  
Low Cost ◽  
Indoor Positioning ◽  
Positioning System ◽  
Fm Radio ◽  
Indoor Positioning System ◽  
Indoor Localisation

This paper presents an indoor positioning system based on FM radio. The system is built on commercially available short-range FM transmitters. This is the first experimental study of FM performance for indoor localisation. FM radio possesses a number of features, which make it distinct from other localisation technologies. Despite the low cost and off-the-shelf components, this FM positioning system reaches a high performance, comparable to other positioning technologies such as Wi-Fi. The authors’ experiments have yielded a median accuracy of 1.0 m and in 95% of cases the error is below 5 m.

scholarly journals A Low Cost Indoor Positioning System Using Computer Vision

2019 ◽  
Vol 11 (4) ◽  
pp. 8-25 ◽  
Author(s):  
Youssef N. Naggar ◽  
◽  
Ayman H. Kassem ◽  
Mohamed S. Bayoumi
Keyword(s):  
Computer Vision ◽  
Low Cost ◽  
Indoor Positioning ◽  
Positioning System ◽  
Indoor Positioning System

scholarly journals A CSI-Based Indoor Positioning System Using Single UWB Ranging Correction

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6447
Author(s):  
Keliu Long ◽  
Darryl Franck Nsalo Kong ◽  
Kun Zhang ◽  
Chuan Tian ◽  
Chong Shen
Keyword(s):  
Indoor Localization ◽  
Low Cost ◽  
Indoor Positioning ◽  
Ultra Wideband ◽  
Localization System ◽  
Initial Stage ◽  
Positioning Stage ◽  
Two Stages ◽  
Indoor Positioning System

A fingerprint-based localization system is an economic way to solve an indoor positioning problem. However, the traditional off-line fingerprint collection stage is a time-consuming and laborious process which limits the use of fingerprint-based localization systems. In this paper, based on ubiquitous Wireless Fidelity (Wi-Fi) equipment and a low-cost Ultra-Wideband (UWB) ranging system (with only one UWB anchor), a ready-to-use indoor localization system is proposed to realize long-term and high-accuracy indoor positioning. More specifically, in this system, it is divided into two stages: (1) an initial stage, and (2) a positioning stage. In the initial stage, an Inertial Measure Unit (IMU) is used to calculate the position using Pedestrian Dead Reckon (PDR) algorithm within a preset number of steps, and the location-related fingerprints are collected to train a Convolutional Neural Network (CNN) regression model; simultaneously, in order to make the UWB ranging system adapt to the Non-Line-of-Sight (NLoS) environment, the increments of acceleration and angular velocity in IMU and the increments of single UWB ranging measures are correlated to pre-train a Supported Vector Regression (SVR). After reaching the threshold of time or step number, the system is changed into a positioning stage, and the CNN predicts the position calibrated by corrected UWB ranging. At last, a series of practical experiments are conducted in the real environment; the experiment results show that, due to the corrected UWB ranging measures calibrating the CNN parameters in every positioning period, this system has stable localization results in a comparative long-term range. Additionally, it has the advantages of stability, low cost, anti-noise, etc.

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