Motion Classification for Analyzing the Order Picking Process using Mobile Sensors

This contribution introduces a new concept to analyze the manual order picking process which is a key task in the field of logistics. The approach relies on a sensor-based motion classification already used in other domains like sports or medical science. Thereby, different sensor data, e. g. acceleration or rotation rate, are continuously recorded during the order picking process. With help of this data, the process can be analyzed to identify different motion classes, like walking or picking, and the time a subject spends in each class. Moreover, relevant motion classes within the order picking process are defined which were identified during field studies in two different companies. These classes are recognized by a classification system working with methods from the field of statistical pattern recognition. The classification is done with a supervised learning approach for which promising results can be shown.