Deep Learning-Enabled Real Time In-Site Quality Inspection Based On Gesture Classification

Ioan-Matei Sarivan, Stefan Andreas Baumann, Daniel Díez Alvarez, Felix Euteneuer, Matthias Reichenbach, Ulrich Berger, Ole Madsen, Simon Bøgh

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review


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 pro- cessing 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.
Original languageEnglish
Title of host publicationAdvances in Automotive Production Technology - Theory and Application : Stuttgart Conference on Automotive Production (SCAP2020)
EditorsPhilipp Weißgraeber, Frieder Heieck, Clemens Ackermann
Number of pages9
Place of PublicationARENA2036, Stuttgart
PublisherSpringer Nature
Publication date2021
Article number10
ISBN (Print)978-3-662-62961-1
ISBN (Electronic)978-3-662-62962-8
Publication statusPublished - 2021
EventStuttgart Conference on Automotive Production 2020 - Stuttgart, Germany
Duration: 2 Nov 202010 Nov 2020


ConferenceStuttgart Conference on Automotive Production 2020
Internet address


  • Deep Learning
  • Signal Processing
  • Smart Wearable Devices
  • MFCC
  • Convolutional Neural Network
  • Accelerometer data
  • Smart Manufacturing


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