Unsupervised Movement Detection in Indoor Positioning Systems of Production Halls
This addresses noise and accuracy issues in IPS data for logistics monitoring in industrial settings, but it is incremental as it builds on existing movement detection methods.
The paper tackles the problem of analyzing noisy indoor positioning system (IPS) data in production halls, proposing a statistical procedure that distinguishes stops, moves, and undesired awakenings, with validation in a real-world case study.
Consider indoor positioning systems (IPS) in production halls where objects equipped with sensors send their current position. Beside its large volume, the analyzation of the resulting raw data is challenging due to the susceptibility towards noise. Reasons are accuracy issues and undesired awakenings of sensors that occur due to the dynamics of logistic processes (e.g.~vibrations of passing forklifts). We propose a tailor-made statistical procedure for these challenges and combine visual analytics with movement detection. Contrary to common stay-point algorithms, we do not only distinguish between stops and moves, but also consider undesired awakenings. This leads to a more detailed interpretation scheme offering usages for online (e.g.~monitoring of orders) and offline applications (e.g.~detection of problematic areas). The approach does not require other information than the raw IPS output and enables an ad-hoc analysis. We underline our findings in an extensive case study with real IPS data of our industry partner.