You Are Sensing, but Are You Biased?

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 where uncalibrated data classification only reaches ~ 85% and ~ 80% respectively This can be a make or break factor in the use of accelerometer and gyroscope data in applications requiring high accuracy e g sports, health, games and others