Indoor localization system for emergency responders with ultra low-power radio landmarks

Simon, Nikolas; Bordoy, Joan; Höflinger, Fabian; Wendeberg, Johannes; Schink, Marc; Tannhauser, Robert; Reindl, L.; Schindelhauer, Christian

doi:10.1109/i2mtc.2015.7151285

Cited by 28 publications

(12 citation statements)

References 14 publications

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“…Our focus is to use inertial sensors for detecting the respiration rate. Inertial sensors are already used for different applications (Simon et al, 2015;Hoeflinger et al, 2012a;Hoeflinger et al, 2011). In applications for human tracking they are already integrated into shoes or clothes (Hoeflinger et al, 2012b;Zhang et al, 2013) for detecting the body movement and measuring the path.…”

Section: State Of the Artmentioning

confidence: 99%

Measuring Respiration and Heart Rate using Two Acceleration Sensors on a Fully Embedded Platform

Vertens¹,

Fischer²,

Heyde³

et al. 2015

Proceedings of the 3rd International Congress on Sport Sciences Research and Technology Support

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We present a novel system which measures the respiration rate using two three-axis accelerometers strapped to the chest and the back of a person. Respiration and heart rate are used as a measurement to determine an athlete's fitness level during the exercise phase. Common respiration rate measurement methods require devices which are mostly stationary or at least clunky and uncomfortable to wear for an extended period of time. By using techniques such as differential measurement, pre-measurement optimization, adaptive filtering and peak detection we are able to obtain respiration rate even when the athlete is running fast. Our system is low-cost, small, and, by using a digital signal processor, fully capable of processing the data in an online way. Results are compared with a reference spirometer and have shown a very low normalized root mean square error (NRMSE) down to 1.42 %. Additionally, the heart rate of an athlete can be measured with the same sensor setup and similar methods.

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Section: State Of the Artmentioning

confidence: 99%

Measuring Respiration and Heart Rate using Two Acceleration Sensors on a Fully Embedded Platform

Vertens¹,

Fischer²,

Heyde³

et al. 2015

Proceedings of the 3rd International Congress on Sport Sciences Research and Technology Support

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“…The other three solutions lack a navigation system. Only Simon et al [7], has a solution that gives feedback about the indoor position to the person using the system. In terms of deployment, all solutions except Li et al [6] need a previous installation.…”

Section: Resultsmentioning

confidence: 99%

“…As an alternative, in 2014, Simon et al [7] proposed the pre-deployment of some radio landmarks (beacons) that would communicate with the mobile equipment of a firefighter with an inertial measurement unit (IMU) in order to improve localization. The Radio landmarks would use 868 MHz.…”

Section: State Of the Artmentioning

confidence: 99%

Augmented Reality for Emergency Situations in Buildings with the Support of Indoor Localization

Codina

Castells-Rufas

Carrabina

et al. 2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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Augmented reality is showing a continuous evolution due to the increasing number of smart glasses that are being used for different applications (e.g. training, marketing, industry, risk avoidance, etc.). In this paper, we present an implementation that uses augmented reality (AR) for emergency situations in smart buildings by means of indoor localization through the use of sub-GHz beacons. This also includes the mapping of emergency elements in the three-dimensional building, together with some example cases.

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“…Today, many different indoor localization systems are available based on different methods. A large number of them use radio frequency (RF) signals for localization [3][4][5][6]. The RF-Systems use the propagation of radio waves.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Indoor Localization Augmentation by Self-Calibration and Machine Learning

Bordoy

Schott

Xie

et al. 2020

Sensors

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An acoustic transmitter can be located by having multiple static microphones. These microphones are synchronized and measure the time differences of arrival (TDoA). Usually, the positions of the microphones are assumed to be known in advance. However, in practice, this means they have to be manually measured, which is a cumbersome job and is prone to errors. In this paper, we present two novel approaches which do not require manual measurement of the receiver positions. The first method uses an inertial measurement unit (IMU), in addition to the acoustic transmitter, to estimate the positions of the receivers. By using an IMU as an additional source of information, the non-convex optimizers are less likely to fall into local minima. Consequently, the success rate is increased and measurements with large errors have less influence on the final estimation. The second method we present in this paper consists of using machine learning to learn the TDoA signatures of certain regions of the localization area. By doing this, the target can be located without knowing where the microphones are and whether the received signals are in line-of-sight or not. We use an artificial neural network and random forest classification for this purpose.

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Indoor localization system for emergency responders with ultra low-power radio landmarks

Cited by 28 publications

References 14 publications

Measuring Respiration and Heart Rate using Two Acceleration Sensors on a Fully Embedded Platform

Measuring Respiration and Heart Rate using Two Acceleration Sensors on a Fully Embedded Platform

Augmented Reality for Emergency Situations in Buildings with the Support of Indoor Localization

Acoustic Indoor Localization Augmentation by Self-Calibration and Machine Learning

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