Authors:
Sascha Feldhorst
;
Mojtaba Masoudenijad
;
Michael ten Hompel
and
Gernot A. Fink
Affiliation:
TU Dortmund University, Germany
Keyword(s):
Motion Classification, Order Picking, Mobile Sensors, Pattern Recognition, Logistics, Materials Handling.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Classification
;
Computer Vision, Visualization and Computer Graphics
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Signal Processing
;
Software Engineering
;
Theory and Methods
Abstract:
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.