Inertial Sensor Based Indoor Localization and Monitoring System for Emergency Responders

Zhang, Rui; Höflinger, Fabian; Reindl, L.

doi:10.1109/jsen.2012.2227593

Cited by 116 publications

(71 citation statements)

References 20 publications

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“…Although much of the work during this time has been dedicated to recognising activities associated with daily living, especially for monitoring the elderly or other populations at risk, there have been attempts to extend HAR to more dynamic activities and environments. Zhang et al [5], for example, present an indoor location tracking system for emergency first responders, each of whom is required to wear six IMUs, that maintains a model (based on Kalman filters) of the tracked bodies. Williamson et al [6], instead, use Gaussian Staircase and Partial Least Squares regression to estimate the loads carried by first responders and soldiers from IMU data.…”

Section: Related Workmentioning

confidence: 99%

Human activity recognition for emergency first responders via body-worn inertial sensors

Scheurer

Tedesco

Brown

et al. 2017

2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

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Access to the full text of the published version may require a subscription. Abstract-Every year over 75 000 firefighters are injured and 159 die in the line of duty. Some of these accidents could be averted if first responders had better information about the information on the ground. The SAFESENS project is developing a novel monitoring system for first responders designed to provide response team leaders with timely and reliable information about their firefighters' status during operations, based on data from wireless inertial measurement units. In this paper we investigate if Gradient Boosted Trees (GBT) could be used for recognising 17 activities, selected in consultation with first responders, from inertial data. By arranging these into more general groups we generate three additional classification problems which are used for comparing GBT with k-Nearest Neighbors (kNN) and Support Vector Machines (SVM). The results show that GBT outperforms the other two approaches for three of these four problems, and that it leads to a generalisation error that is more balanced across the target activities than SVM. Rights

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Section: Related Workmentioning

confidence: 99%

Human activity recognition for emergency first responders via body-worn inertial sensors

Scheurer

Tedesco

Brown

et al. 2017

2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

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“…In the experiment result shows that kalman filter deals with GPS accuracy decreases and magnetometer measurement noise. Rui Zhang et al [7] present's indoor localization and monitoring system for an emergency responder with modified Kalman filters for reducing magnetic field disturbance in indoor area. In the experiment result shows that was success to track people indoor in walking and running.…”

Section: Related Workmentioning

confidence: 99%

An Implementation of Error Minimization Position Estimate in Wireless Inertial Measurement Unit using Modification ZUPT

Darmawan

Dewanto

Pramadihanto

2016

emitter

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Position estimation using WIMU (Wireless Inertial Measurement Unit) is one of emerging technology in the field of indoor positioning systems. WIMU can detect movement and does not depend on GPS signals. The position is then estimated using a modified ZUPT (Zero Velocity Update) method that was using Filter Magnitude Acceleration (FMA), Variance Magnitude Acceleration (VMA) and Angular Rate (AR) estimation. Performance of this method was justified on a six-legged robot navigation system. Experimental result shows that the combination of VMA-AR gives the best position estimation.

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“…The commonly used method of using magnetometers is straightforward and consist of three steps [25]: a) leveling the magnetometer readings by using the accelerometers; b) calculating the magnetic heading by using the leveled magnetometers; and c) calculating the true heading by a declination angle to the magnetic heading. This is based on the assumption that the LMF is simply the geomagnetic field and thus declination angle can be calculated from the IGRF model [26] .…”

Section: A Difference Between Outdoors and Indoorsmentioning

confidence: 99%

Autonomous Calibration of MEMS Gyros in Consumer Portable Devices

Georgy²,

Niu

et al. 2015

IEEE Sensors J.

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This paper presents a real-time calibration method for gyro sensors in consumer portable devices. The calibration happens automatically without the need for external equipment or user intervention. Multi-level constraints, including the pseudo-observations, the accelerometer and magnetometer measurements, and the quasi-static attitude updates, are used to make the method reliable and accurate under natural user motions. Walking tests with the Samsung Galaxy S3 and S4 smartphones showed that the method estimate promising calibration results even under challenging motion modes such as dangling and pocket, and in challenging indoor environments with frequent magnetic interferences. A 1530-437X (c)

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Inertial Sensor Based Indoor Localization and Monitoring System for Emergency Responders

Cited by 116 publications

References 20 publications

Human activity recognition for emergency first responders via body-worn inertial sensors

Human activity recognition for emergency first responders via body-worn inertial sensors

An Implementation of Error Minimization Position Estimate in Wireless Inertial Measurement Unit using Modification ZUPT

Autonomous Calibration of MEMS Gyros in Consumer Portable Devices

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