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Published in:
2021 | OriginalPaper | Chapter
Deep Learning-Enabled Real Time In-Site Quality Inspection Based On Gesture Classification
Authors : Ioan-Matei Sarivan, Stefan Andreas Baumann, Daniel Díez Álvarez, Felix Euteneuer, Matthias Reichenbach, Ulrich Berger, Ole Madsen, Simon Bøgh
Published in: Advances in Automotive Production Technology – Theory and Application
Publisher: Springer Berlin Heidelberg
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Abstract
In this paper we present a novel method for performing in site real time quality inspection (QI) and consequently, digitalization of manual processes performed by human workers. It complements and improves our previous work in this area, which makes use of telemetry gathered from a smartwatch to classify manual actions as successful or unsuccessful. This new methodology provides the worker with a real time capable, robust and more accurate quality inspector. This work enhances the existing system through the elimination of input from the user by making use of a BIOX bracelet that detects gestures. The signal processing and classification methods are simplified and optimised by using assembled neural networks thus merging together the data gathered from multiple signal sources. Consequently, the overall QI system is improved with around 70%, thus furthering the necessary development needed to have a system ready to be used on a production environment.