Design and implementation of application based on smartphone sensors

Pan, Jiabao; Qian, Xicheng; Zhang, Bo

doi:10.1145/3145777.3145797

Cited by 2 publications

(2 citation statements)

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“…Consequently, measured displacement is shown by (7)- (9). As a result, displacement error is shown by (10)- (12), where Z is the noise vector.…”

Section: A Noise Cancellation Using Zuptmentioning

confidence: 99%

“…Assuming linear motion, α is constant throughout the motion, hence this constant can be removed with the sensor constant bias using long term averaging while the device is at rest. In general, in case of curvilinear motion, α is not constant, and removing the gravity component in this case is more complex and can be addressed by using techniques described in [12]. Curvilinear motion is out of the scope of this paper and will be investigated in future work.…”

Section: A Noise Cancellation Using Zuptmentioning

confidence: 99%

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Effect of Sensor Error on the Assessment of Seismic Building Damage

Ibrahim

El‐Tawil

2020

IEEE Trans. Instrum. Meas.

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Natural disasters affect structural health of buildings, thus directly impacting public safety. Continuous structural monitoring can be achieved by deploying an internet of things (IoT) network of distributed sensors in buildings to capture floor movement. These sensors can be used to compute the displacements of each floor, which can then be employed to assess building damage after a seismic event. The peak relative floor displacement is computed, which is directly related to damage level according to government standards. With this information, the building inventory can be classified into immediate occupancy (IO), life safety (LS) or collapse prevention (CP) categories. In this work, we propose a zero velocity update (ZUPT) technique to minimize displacement estimation error. Theoretical derivation and experimental validation are presented. In addition, we investigate modeling sensor error and interstory drift ratio (IDR) distribution. Moreover, we discuss the impact of sensor error on the achieved building classification accuracy.

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“…Consequently, measured displacement is shown by (7)- (9). As a result, displacement error is shown by (10)- (12), where Z is the noise vector.…”

Section: A Noise Cancellation Using Zuptmentioning

confidence: 99%

Section: A Noise Cancellation Using Zuptmentioning

confidence: 99%