An Indoor Continuous Positioning Algorithm on the Move by Fusing Sensors and Wi-Fi on Smartphones

Li, Huaiyu; Chen, Xiuwan; Jing, Guifei; Wang, Yuan; Cao, Yanfeng; Li, Fēi; Zhang, Xinlong; Han, Xiao

doi:10.3390/s151229850

Cited by 26 publications

(20 citation statements)

References 50 publications

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“…Therefore, a supplementary approach must be adopted to achieve a robust and precise indoor positioning and tracking system. For example, the authors of [28,44,45,47,129,130,131,132,133,134,135] combined PDR with other approaches to improve localization accuracy (e.g., GPS, ultrasound ranging, active RFID, WiFi signatures, and Chirp Spread Spectrum (CSS) radio beacons). The accuracy of these approaches can be greatly improved compared with stand-alone PDR systems, with errors reaching below 1.7 m. Hence, in this review, a scheme fusing a PDR system with a magnetic indoor positioning technique is recommended.…”

Section: Discussionmentioning

confidence: 99%

A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time

Wang

Yang

Dong

2017

Sensors

264

144

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Rapid growth of the aged population has caused an immense increase in the demand for healthcare services. Generally, the elderly are more prone to health problems compared to other age groups. With effective monitoring and alarm systems, the adverse effects of unpredictable events such as sudden illnesses, falls, and so on can be ameliorated to some extent. Recently, advances in wearable and sensor technologies have improved the prospects of these service systems for assisting elderly people. In this article, we review state-of-the-art wearable technologies that can be used for elderly care. These technologies are categorized into three types: indoor positioning, activity recognition and real time vital sign monitoring. Positioning is the process of accurate localization and is particularly important for elderly people so that they can be found in a timely manner. Activity recognition not only helps ensure that sudden events (e.g., falls) will raise alarms but also functions as a feasible way to guide people's activities so that they avoid dangerous behaviors. Since most elderly people suffer from age-related problems, some vital signs that can be monitored comfortably and continuously via existing techniques are also summarized. Finally, we discussed a series of considerations and future trends with regard to the construction of "smart clothing" system.

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Section: Discussionmentioning

confidence: 99%

A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time

Wang

Yang

Dong

2017

Sensors

264

144

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“…where PL (dBm) is the path loss at a distance d in meters versus an increase in the distance from the transmitter in meter; Pl o is the power received at a reference distance 0 in 1 m [48]; k is a path loss index, which relies on particular propagation environment, and its value is large under impedance (i.e., obstacles and wall) [15]; and σ is a zero-mean Gaussian random variable (in decibels).…”

Section: Log-normal Shadowing Modelmentioning

confidence: 99%

Path Loss Model-Based PSO for Accurate Distance Estimation in Indoor Environments

Hashim¹,

Mohammed²,

Gharghan³

2018

jcm

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Wireless sensor networks (WSNs) and their applications have received considerable interest in the last few years. In WSNs, accurate path loss models should be considered to achieve a successful distribution of several nodes. In this work, two path loss models are proposed to evaluate the distance between two ZigBee WSNs. First, a path loss model based on conventional Log-Normal Shadowing Model (LNSM) is derived using the collected received signal strength indicator (RSSI) of the ZigBee in real time. Second, a new path loss model based on Particle Swarm Optimization (PSO) algorithm hybridized with Polynomial Equation (PE) is proposed. The PSO algorithm is used to select the optimum coefficients of PE. These coefficients can be utilized to optimize the distance estimation error based on the curve fitting. Therefore, the new path loss model called hybrid PE-PSO is innovated in this work. The hybrid PE-PSO model considerably improves the distance estimation accuracy compared with the LNSM. Results show that the hybrid PE-PSO achieves 85% improvement in distance error compared with the traditional LNSM. The mean absolute error of 0.77 m is obtained for distance estimation, which outperforms that by state of the arts.

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“…Considering the variation of Wi-Fi RSS, computation cost involved in fingerprint matching, and utilization of the low-cost inertial sensors built-in smartphones, the refresh rate model of the Wi-Fi signal is introduced in [21] to optimize the Wi-Fi localization, and meanwhile the affinity propagation clustering is discussed in [22] to reduce the computation cost for the positioning. At the same time, an empirical model constructed from the individual height and peak-to-peak magnitude of the acceleration in each step is developed in [23] to estimate the stride length of the pedestrian.…”

Section: Related Workmentioning

confidence: 99%

Wi-Fi/MARG Integration for Indoor Pedestrian Localization

Tian

Jin

Zhou

et al. 2016

Sensors

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With the wide deployment of Wi-Fi networks, Wi-Fi based indoor localization systems that are deployed without any special hardware have caught significant attention and have become a currently practical technology. At the same time, the Magnetic, Angular Rate, and Gravity (MARG) sensors installed in commercial mobile devices can achieve highly-accurate localization in short time. Based on this, we design a novel indoor localization system by using built-in MARG sensors and a Wi-Fi module. The innovative contributions of this paper include the enhanced Pedestrian Dead Reckoning (PDR) and Wi-Fi localization approaches, and an Extended Kalman Particle Filter (EKPF) based fusion algorithm. A new Wi-Fi/MARG indoor localization system, including an Android based mobile client, a Web page for remote control, and a location server, is developed for real-time indoor pedestrian localization. The extensive experimental results show that the proposed system is featured with better localization performance, with the average error 0.85 m, than the one achieved by using the Wi-Fi module or MARG sensors solely.

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An Indoor Continuous Positioning Algorithm on the Move by Fusing Sensors and Wi-Fi on Smartphones

Cited by 26 publications

References 50 publications

A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time

A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time

Path Loss Model-Based PSO for Accurate Distance Estimation in Indoor Environments

Wi-Fi/MARG Integration for Indoor Pedestrian Localization

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