Drift-free dynamic height sensor using MEMS IMU aided by MEMS pressure sensor

Tanigawa, M.; Luinge, Hendrik J.; Schipper, L.; Slycke, P.

doi:10.1109/wpnc.2008.4510374

Cited by 58 publications

(58 citation statements)

References 2 publications

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“…Tanigawa et al [23] uses barometer as an aid in removing accelerometer drift. Due to its excellent relative accuracy, barometer has been used for floor-change detection [24,25].…”

Section: Related Workmentioning

confidence: 99%

Using mobile phone barometer for low-power transportation context detection

Sankaran

Zhu

Guo

et al. 2014

Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems

108

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Accelerometer is the predominant sensor used for lowpower context detection on smartphones. Although lowpower, accelerometer is orientation and position-dependent, requires a high sampling rate, and subsequently complex processing and training to achieve good accuracy. We present an alternative approach for context detection using only the smartphone's barometer, a relatively new sensor now present in an increasing number of devices. The barometer is independent of phone position and orientation. Using a low sampling rate of 1 Hz, and simple processing based on intuitive logic, we demonstrate that it is possible to use the barometer for detecting the basic user activities of IDLE, WALKING, and VEHICLE at extremely lowpower. We evaluate our approach using 47 hours of realworld transportation traces from 3 countries and 13 individuals, as well as more than 900 km of elevation data pulled from Google Maps from 5 cities, comparing power and accuracy to Google's accelerometer-based Activity Recognition algorithm, and to Future Urban Mobility Survey's (FMS) GPS-accelerometer server-based application. Our barometer-based approach uses 32 mW lower power compared to Google, and has comparable accuracy to both Google and FMS. This is the first paper that uses only the barometer for context detection.

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“…Tanigawa et al [23] uses barometer as an aid in removing accelerometer drift. Due to its excellent relative accuracy, barometer has been used for floor-change detection [24,25].…”

Section: Related Workmentioning

confidence: 99%

Using mobile phone barometer for low-power transportation context detection

Sankaran

Zhu

Guo

et al. 2014

Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems

108

View full text Add to dashboard Cite

show abstract

“…Therefore, it is required to eliminate the accumulated errors regarding integration. One of the methods to eliminate the accumulated errors is ZUPT [9]- [10]. In this paper, the interval of zero-velocity is established by using the point for the detection of steps.…”

Section: Classification Of the Geographical Place Where The Pedestmentioning

confidence: 99%

“…However, the barometer also has such a disadvantage as the inconstant height output caused by the influence of the atmospheric pressure changing based on different time periods. In order to handle such a matter, the signals of the accelerator and the information provided by the ZUPT (Zero-velocity UPdaTe) method [8] [9] can be applied to KF (Kalman Filter) [10]. However, if the changes shown by the barometer are great, it is possible to have great errors for the measured height.…”

Section: Introductionmentioning

confidence: 99%

Height estimation scheme of low-cost pedestrian dead-reckoning system using Kalman Filter and walk condition estimation algorithm

Kim

Hwang

Choi

et al. 2013

2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

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In this paper, we propose a height estimation scheme for Inertial Measurement Unit (IMU)-based PDR system in indoor buildings. Basically, we utilize a barometer to estimate height position of pedestrian. In addition, we design a KF and a WSEA to minimize the estimation error due to the barometer.The proposed algorithm has been tested in a real test bed. The experimental results show that the proposed algorithms mitigate the estimation error.Yunki Kim is a ph.D candidate of the

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“…Most often these sensors are combined to form a sensor-fusion platform, whereby the measurements of multiple sources are combined to yield a more accurate answer than could have been found by using the sensors singularly. Some common successful combinations have included a height sensing inertial measurement unit (IMU), also known as an altitude heading reference system (AHRS), which combines a MEMS IMU with MEMS pressure sensor [6] and also accelerometers paired with RSSI (received signal strength indicators) [7].…”

Section: A State-of-the-art In Posture Identificationmentioning

confidence: 99%

Determining proximity warning and action zones for a magnetic proximity detection system

Jobes

Carr

DuCarme

et al. 2011

2011 IEEE Industry Applications Society Annual Meeting

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-Researchers at the National Institute for Occupational Safety and Health (NIOSH) are developing intelligent software for use with electromagnetic proximity detection systems. The technology accurately locates workers around mining machines in real time. With the accurate locations of the workers around the equipment being known, their safety status can be evaluated. If a worker is located dangerously close to a machine, the machine can be partially or completely disabled to protect the worker from striking, pinning and entanglement hazards according to pre-defined logic. The technology is particularly applicable to mobile underground mining machines which offer difficult safety challenges in that operators generally work in close proximity to these machines in very restricted spaces. With use of the intelligent proximity detection system, nuisance alarms and failures to alarm are also expected to be sharply reduced. An effective proximity warning and action zone scheme is necessary for safe implementation and will improve the acceptance of a magnetic proximity detection system by underground workers.Index Terms -mining equipment, magnetic sensors, sensor systems, sensor fusion, occupational safety.

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Drift-free dynamic height sensor using MEMS IMU aided by MEMS pressure sensor

Cited by 58 publications

References 2 publications

Using mobile phone barometer for low-power transportation context detection

Using mobile phone barometer for low-power transportation context detection

Height estimation scheme of low-cost pedestrian dead-reckoning system using Kalman Filter and walk condition estimation algorithm

Determining proximity warning and action zones for a magnetic proximity detection system

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