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Erschienen in:
2019 | OriginalPaper | Buchkapitel
2. Identifying Sensors via Statistical Analysis of Body-Worn Inertial Sensor Data
verfasst von : Philipp M. Scholl, Kristof Van Laerhoven
Erschienen in: Human Activity Sensing
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Abstract
Every benchmark dataset that contains inertial data (acceleration, rate-of-turn, magnetic flux) requires a thorough description of the datasets itself. This description tends often to be unstructured, and supplied to researchers as a conventional description, and in many cases crucial details are not available anymore. In this chapter, we argue that each sensor modality exhibits particular statistical properties that allow to reconstruct the modality solely from the sensor data itself. In order to investigate this, tri-axial inertial sensor data from five publicly available datasets are analysed. We found that in particular three statistical properties, the mode, the kurtosis, and the number of modes tend to be sufficient for classification of sensor modality—requiring as the only assumption that the sampling rate and sample format are known, and the fact that that acceleration and magnetometer data is present in the dataset. With those assumption in place, we found that \(98\%\) of all 1003 data points were successfully classified.