Towards Segmentation and Labelling of Motion Data in Manufacturing Scenarios

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

28 Downloads (Pure)


There is a significant interest to evaluate the occupational exposure that manufacturing operators are subjected throughout the working day. The objective evaluation of occupational exposure with direct measurements and the need for automatic annotation of relevant events arose. The current work proposes the use of a self similarity matrix (SSM) as a tool to flag events that may be of importance to be analyzed by ergonomic teams. This way, data directly retrieved from the work environment will be summarized and segmented into sub-sequences of interest over a multi-timescale approach. The process occurs under 3 timescale levels: Active working periods, working cycles, and in-cycle activities. The novelty function was used to segment non-active and active working periods with an F1-score of 95%. while the similarity function was used to correctly segment 98% of working cycle with a duration error of 6.12%. In addition, this method was extended into examples of multi time scale segmentation with the intent of providing a summary of a time series as well as support in data labeling tasks, by means of a query-by-example process to detect all subsequences.

Original languageEnglish
Title of host publicationBiomedical Engineering Systems and Technologies - 14th International Joint Conference, BIOSTEC 2021, Revised Selected Papers
EditorsClaudine Gehin, Bruno Wacogne, Alexandre Douplik, Ronny Lorenz, Bethany Bracken, Cátia Pesquita, Ana Fred, Ana Fred, Hugo Gamboa
Place of PublicationCham
Number of pages22
ISBN (Electronic)978-3-031-20664-1
ISBN (Print)978-3-031-20663-4
Publication statusPublished - 2022
Event14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021 - Virtual, Online
Duration: 11 Feb 202113 Feb 2021

Publication series

NameCommunications in Computer and Information Science
Volume1710 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021
CityVirtual, Online


  • Industry
  • Inertial
  • Labeling
  • Musculoskeletal disorders
  • Segmentation
  • Self-similarity matrix
  • Summarization
  • Time series
  • Unsupervised


Dive into the research topics of 'Towards Segmentation and Labelling of Motion Data in Manufacturing Scenarios'. Together they form a unique fingerprint.

Cite this