Indoor positioning system using walking pattern classification

Cillis, Francesca De; Simio, Francesca De; Faramondi, Luca; Inderst, Federica; Pascucci, Federica; Setola, Roberto

doi:10.1109/med.2014.6961424

Cited by 14 publications

(5 citation statements)

References 12 publications

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“…Some studies mention their data collection processes in detail, while others do not mention the process, even though the sampling rate for data collection is critical. In [84], the mentioned data rate of IMU sensors was a sample frequency of 50 Hz, whereas in [92], the IMU sample rate was 100 Hz and the RFID sample rate was 5 Hz. In [93], the authors collected 100 magnetic data observations at 10 Hz for each reference point.…”

Section: Initial Datamentioning

confidence: 99%

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

Al-Sahly

Hassan

Saleem

et al. 2022

Sensors

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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.

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Section: Initial Datamentioning

confidence: 99%

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

Al-Sahly

Hassan

Saleem

et al. 2022

Sensors

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“…However, this is rarely described in literature, or the sensor's actual contribution is hidden behind machine learning and neural networks [Zha+18a]. Elevators can also be detected by examining precise gravity changes, yielding a hint on the change in altitude, by integrating the accelerometer's tilt compensated z-axis [Cil+14]. For a simple yes/no decision, elevators could be assumed when a change in altitude is indicated by the barometer, but no steps are detected.…”

Section: Activity-detectionmentioning

confidence: 99%

Smartphone-Based 3D Indoor Localization and Navigation

Ebner¹

2021

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During my master's thesis, which covered the essentials of Wi-Fi location estimation, I came in contact with the concepts of indoor localization, and multi sensor fusion. Having attracted my interest, I hereafter joined the Faculty of Computer Science and Business Information Systems at the University of Applied Sciences Würzburg-Schweinfurt as a research assistant. During that time I conducted the majority of my research on indoor localization and navigation, which finally lead to writing this work.I would like to thank my colleagues during that time, but especially my local doctoral adviser Prof. Dr. Frank Deinzer. Not only did he guide me towards earning a doctors degree, he also provided extensive feedback to all publications, always ensuring utmost scientific quality. I would also like to thank my external doctoral adviser and first referee Prof. Dr. habil. Marcin Grzegorzek for his trust, all provided feedback, and the warm welcome within his research group. I also owe gratitude to my co-workers Lukas, Toni, and Markus. Without hours of mutual feedback, technical input, paired programming, data acquisition, and networking, this work would not have been possible. Likewise, I also wish to thank Prof. Dr. Karsten Huffstadt and Prof. Dr. Arndt Balzer for helping with funding my initial employment.In order to develop a generic indoor localization and navigation solution, the presented system was deployed to and tested within several unique buildings. For this, special thanks go to Dr. Hellmuth Möhring from the RothenburgMuseum in Rothenburg ob der Tauber, Prof. Dr. Günther Moosbauer from the Gäubodenmuseum in Straubing, Angelika Schreiber from the Hutmuseum in Lindenberg, the Landesstelle für die nichtstaatlichen Museen in Bayern, and the Sparkassenstiftung. By providing access and enabling temporal hardware installations they allowed gathering data within a range of unique and representative public buildings, enhancing the quality of the developed system, and the experiments provided within this work.Thanks also go to Markus, Christin, Alexander, and Maximilian, not only for spending numerous hours of their spare time with proof reading and feedback, but also for all emotional support while conducting my research and writing this work. The same goes to my parents, for always supporting me, and my education.To Katharina, who has been a true friend, and will never be forgotten.

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“…On the basic of previous researches, multi-sensors have already been employed for traffic speed and travel time detecting [4][5][6]. Indoor positioning is another such field, with recent publications revealing the capability in healthcare monitoring [7,8], surveillance [9,10], and target group pattern generation [11,12]. Multi-sensing systems outperform traditional tracking techniques in managing the flow of signal and coordinating sensor actions [13].…”

Section: Introductionmentioning

confidence: 99%

Multi-sensing based target tracking by using decision-making strategy with spatial and temporal properties

Liu

Xiu-ju

Jin

et al. 2019

J Wireless Com Network

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Multi-sensing system for target tracking has been addressed by many researchers from different fields. In this work, a multi-sensing system within the time, space, and frequency domains is first described. The system development is based on sensor spatial and temporal characteristics, and therefore is reliable and stable. The frequency outcome of sensors is employed to segment the detection scope into different domains, and is simple to implement. For resolving the challenge of decision-making strategy, this work combines the spatial and temporal properties with the determination mechanism as well as optimizes the system devising-making it a very promising basis for the realization of tracking accuracy improvement. With this strategy, target detection results are obtained and evaluated by probability-based parameters. The proposed decision-making scheme receives a high detection accuracy as well as a good working performance according to statistical analysis, allowing a straightforward methodology for the configuration of multi-sensing system.

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Indoor positioning system using walking pattern classification

Cited by 14 publications

References 12 publications

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

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

Smartphone-Based 3D Indoor Localization and Navigation

Multi-sensing based target tracking by using decision-making strategy with spatial and temporal properties

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