A Comprehensive Overview of Inertial Sensor Calibration Techniques

Poddar, Shashi; Kumar, V. K.; Kumar, Amod

doi:10.1115/1.4034419

Cited by 69 publications

(49 citation statements)

References 65 publications

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“…Although MEMS technology has greatly reduced the size and cost of motion sensors, MEMS sensors are usually less accurate than their optical counterparts due to various types of error. In general, these errors can be categorized as deterministic and random: random errors are usually caused by electronic noise interfering with the output of sensors, which change over time and have to be modeled stochastically; deterministic errors are produced by manufacturing imperfections and can be classified into three categories: bias, scaling factor, and nonorthogonality misalignment errors [5], [6].…”

Section: Calibration Backgroundmentioning

confidence: 99%

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SensorID: Sensor Calibration Fingerprinting for Smartphones

Zhang

Beresford

Sheret

2019

2019 IEEE Symposium on Security and Privacy (SP)

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Sensors are an essential component of many computer systems today. Mobile devices are a good example, containing a vast array of sensors from accelerometers and GPS units, to cameras and microphones. Data from these sensors are accessible to application programmers who can use this data to build context-aware applications. Good sensor accuracy is often crucial, and therefore manufacturers often use perdevice factory calibration to compensate for systematic errors introduced during manufacture. In this paper we explore a new type of fingerprinting attack on sensor data: calibration fingerprinting. A calibration fingerprinting attack infers the perdevice factory calibration data from a device by careful analysis of the sensor output alone. Such an attack does not require direct access to any calibration parameters since these are often embedded inside the firmware of the device and are not directly accessible by application developers. We demonstrate the potential of this new class of attack by performing calibration fingerprinting attacks on the inertial measurement unit sensors found in iOS and Android devices. These sensors are good candidates because access to these sensors does not require any special permissions, and the data can be accessed via both a native app installed on a device and also by JavaScript when visiting a website on an iOS and Android device. We find we are able to perform a very effective calibration fingerprinting attack: our approach requires fewer than 100 samples of sensor data and takes less than one second to collect and process into a device fingerprint that does not change over time or after factory reset. We demonstrate that our approach is very likely to produce globally unique fingerprints for iOS devices, with an estimated 67 bits of entropy in the fingerprint for iPhone 6S devices. In addition, we find that the accelerometer of Google Pixel 2 and Pixel 3 devices can also be fingerprinted by our approach.

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Section: Calibration Backgroundmentioning

confidence: 99%

“…A myriad of calibration techniques have been proposed to calculate the gain matrix and bias vector during manufacture. Overall, these methods can be divided into four groups: highprecision equipment, multi-position, Kalman filter, and vision based [5]. Manufacturers can choose to only calibrate the bias vector to lower the cost.…”

Section: Calibration Backgroundmentioning

confidence: 99%

SensorID: Sensor Calibration Fingerprinting for Smartphones

Zhang

Beresford

Sheret

2019

2019 IEEE Symposium on Security and Privacy (SP)

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“…Kalman-filtering based methods have been employed successfully for bias removal in sensors [27,42] but require constant processing and thus their energy cost is significant, thus making them not suitable to use in our setting [31]. Previously presented non-Kalman based schemes [21,30] require manual parameter tweaking and/or special equipment and they all involve at least some user interaction and are not adaptive (i.e. one-shot calibration).…”

Section: Introductionmentioning

confidence: 99%

You Are Sensing, but Are You Biased?

Grammenos

Mascolo

Crowcroft

2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Mobile devices are becoming pervasive to our daily lives: they follow us everywhere and we use them for much more than just communication. These devices are also equipped with a myriad of different sensors that have the potential to allow the tracking of human activities, user patterns, location, direction and much more. Following this direction, many movements including sports, quantified self, and mobile health ones are starting to heavily rely on this technology, making it pivotal that the sensors offer high accuracy. However, heterogeneity in hardware manufacturing, slight substrate differences, electronic interference as well as external disturbances are just few of the reasons that limit sensor output accuracy which in turn hinders sensor usage in applications which need very high granularity and precision, such as quantified-self applications. Although, calibration of sensors is a widely studied topic in literature to the best of our knowledge no publicly available research exists that specifically tackles the calibration of mobile phones and existing methods that can be adapted for use in mobile devices not only require user interaction but they are also not adaptive to changes. Additionally, alternative approaches for performing more granular and accurate sensing exploit body-wide sensor networks using mobile phones and additional sensors; as one can imagine these techniques can be bulky, tedious, and not particularly user friendly. Moreover, existing techniques for performing data corrections post-acquisition can produce inconsistent results as they miss important context information provided from the device itself; which when used, has been shown to produce better results without a imposing a significant power-penalty. In this paper we introduce a novel multiposition calibration scheme that is specifically targeted at mobile devices. Our scheme exploits machine learning techniques to perform an adaptive, power-efficient auto-calibration procedure with which achieves high output sensor accuracy when compared to state of the art techniques without requiring any user interaction or special equipment beyond device itself. Moreover, the energy costs associated with our approach are lower than the alternatives (such as Kalman filter based solutions) and the overall power penalty is < 5% when compared against power usage that is exhibited when using uncalibrated traces; thus, enabling our technique to be used efficiently on a wide variety of devices. Finally, our evaluation illustrates that calibrated signals offer a tangible benefit in classification accuracy, ranging from 3 to 10%, over uncalibrated ones when using state of the art classifiers; on the other hand when using simpler SVM classifiers the classification improvement is boosted ranging from 8% to 12% making lower performing classifiers much more reliable. Additionally, we show that for similar activities which are hard to distinguish otherwise, we reach an accuracy of > 95% when using neural network classifiers and > 88% when using SVM classifiers...

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“…Systematic errors include bias, scale factors and nonorthogonality errors [51]. The bias error is the deviation of the output from the zero level when the input to the sensor is zero.…”

Section: Error Modelsmentioning

confidence: 99%

“…Calibration without additional equipment can be also separated into two basic approaches [51]. The first one uses measurements acquired in specific stationary positions or during specified movements, which are utilized to compute calibration parameters based on different basic principles.…”

Section: Calibration Principlesmentioning

confidence: 99%

New methods in the application of inertial and magnetic sensors in online pattern recognition problems

Šarčević

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A Comprehensive Overview of Inertial Sensor Calibration Techniques

Cited by 69 publications

References 65 publications

SensorID: Sensor Calibration Fingerprinting for Smartphones

SensorID: Sensor Calibration Fingerprinting for Smartphones

You Are Sensing, but Are You Biased?

New methods in the application of inertial and magnetic sensors in online pattern recognition problems

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