AccelPrint: Imperfections of Accelerometers Make Smartphones Trackable

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Deep Learning for Sensor-based Human Activity Recognition (2020, background): “In fact, owing to the imperfections in the production process, sensor chips show variation in the same conditions [37].”
Scaling Human Activity Recognition via Deep Learning-based Domain Adaptation (2018, background): “Indeed, the accelerometer sensor on different smartphones (of the same model) are known to exhibit perceptible differences [3], and the AR accuracy is known to degrade [4] due to such instance-specific variations in the sensor observations across different devices.”
A Survey of Intrusion Detection for In-Vehicle Networks (2020, methodology): “Choi et al. proposed a new method, which can extract suitable statistical features from the sampled signal S ( k ) [69] based on the method proposed by Dey et al. [70].”
Smart Devices are Different: Assessing and MitigatingMobile Sensing Heterogeneities for Activity Recognition (2015, background): “accelerometer fingerprint, which the authors define via its biases and variations [19].”
WALNUT: Waging Doubt on the Integrity of MEMS Accelerometers with Acoustic Injection Attacks (2017, methodology): “Recent studies show that gyroscopes and accelerometers can leak personal information [12], [13], [14], [15], [16].”