Unsupervised Movement Detection in Indoor Positioning Systems of Production Halls

TL;DR

This paper introduces a statistical and visual analytics approach for detailed movement detection in indoor positioning systems within production halls, effectively handling noise and sensor awakenings for real-time and offline applications.

Contribution

It presents a novel method that distinguishes between stops, moves, and sensor awakenings using only raw IPS data, enhancing analysis accuracy in industrial environments.

Findings

Improved movement detection accuracy in noisy IPS data

Effective differentiation between actual stops and sensor awakenings

Applicable for real-time monitoring and offline analysis in production halls

Abstract

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.