---
_id: '4312'
abstract:
- lang: eng
  text: Computer-aided assistance systems are entering the world of work and production.
    Such systems utilize augmented- and virtual-reality for operator training and
    live guidance as well as mobile maintenance and support. This is particularly
    important in the modern production reality of ever-changing products and `lot
    size one' customization of production.This paper focuses on the application of
    machine learning approach to extend the functionality of assistance systems. Machine
    learning provides tools to analyse large amounts of data and extract meaningful
    information. The goal here is to recognize the movement of an operator which would
    enable automatic display of instructions relevant to them.We present the challenges
    facing machine learning applications in human-centered assistance systems and
    a framework to assess machine learning approaches feasible for this scenario.
    The approach is assessed on a historical data set and then deployed in a work
    station for live testing. The post-hoc, or historical, analysis yields promising
    results. The ad-hoc, or live, analysis is a complex task and the results are affected
    by multiple factors, most of which are introduced by the human influence.The contribution
    of this paper is an approach to adapt state- of-the-art machine learning to operator
    movement recognition with a special focus on approaches to spatial time series
    data pre-processing. Presented experiment results validate the approach and show
    that it performs well in a real-world scenario.
author:
- first_name: Marta
  full_name: Fullen, Marta
  last_name: Fullen
- first_name: Alexander
  full_name: Maier, Alexander
  id: '11158'
  last_name: Maier
- first_name: Arthur
  full_name: Nazarenko, Arthur
  last_name: Nazarenko
- first_name: Sascha
  full_name: Jenderny, Sascha
  last_name: Jenderny
- first_name: Carsten
  full_name: Röcker, Carsten
  id: '61525'
  last_name: Röcker
citation:
  ama: 'Fullen M, Maier A, Nazarenko A, Jenderny S, Röcker C. Machine Learning for
    Assistance Systems: Pattern-Based Approach to Online Step Recognition. In: IEEE,
    ed. <i>2019 IEEE 17th International Conference on Industrial Informatics (INDIN)</i>.
    Piscataway, NJ : IEEE; 2019:296-302. doi:<a href="https://doi.org/10.1109/INDIN41052.2019.8972122">10.1109/INDIN41052.2019.8972122</a>'
  apa: 'Fullen, M., Maier, A., Nazarenko, A., Jenderny, S., &#38; Röcker, C. (2019).
    Machine Learning for Assistance Systems: Pattern-Based Approach to Online Step
    Recognition. In IEEE (Ed.), <i>2019 IEEE 17th International Conference on Industrial
    Informatics (INDIN)</i> (pp. 296–302). Piscataway, NJ : IEEE. <a href="https://doi.org/10.1109/INDIN41052.2019.8972122">https://doi.org/10.1109/INDIN41052.2019.8972122</a>'
  bjps: '<b>Fullen M <i>et al.</i></b> (2019) Machine Learning for Assistance Systems:
    Pattern-Based Approach to Online Step Recognition. In IEEE (ed.), <i>2019 IEEE
    17th International Conference on Industrial Informatics (INDIN)</i>. Piscataway,
    NJ : IEEE, pp. 296–302.'
  chicago: 'Fullen, Marta, Alexander Maier, Arthur Nazarenko, Sascha Jenderny, and
    Carsten Röcker. “Machine Learning for Assistance Systems: Pattern-Based Approach
    to Online Step Recognition.” In <i>2019 IEEE 17th International Conference on
    Industrial Informatics (INDIN)</i>, edited by IEEE, 296–302. Piscataway, NJ :
    IEEE, 2019. <a href="https://doi.org/10.1109/INDIN41052.2019.8972122">https://doi.org/10.1109/INDIN41052.2019.8972122</a>.'
  chicago-de: 'Fullen, Marta, Alexander Maier, Arthur Nazarenko, Sascha Jenderny und
    Carsten Röcker. 2019. Machine Learning for Assistance Systems: Pattern-Based Approach
    to Online Step Recognition. In: <i>2019 IEEE 17th International Conference on
    Industrial Informatics (INDIN)</i>, hg. von IEEE, 296–302. Piscataway, NJ : IEEE.
    doi:<a href="https://doi.org/10.1109/INDIN41052.2019.8972122,">10.1109/INDIN41052.2019.8972122,</a>
    .'
  din1505-2-1: '<span style="font-variant:small-caps;">Fullen, Marta</span> ; <span
    style="font-variant:small-caps;">Maier, Alexander</span> ; <span style="font-variant:small-caps;">Nazarenko,
    Arthur</span> ; <span style="font-variant:small-caps;">Jenderny, Sascha</span>
    ; <span style="font-variant:small-caps;">Röcker, Carsten</span>: Machine Learning
    for Assistance Systems: Pattern-Based Approach to Online Step Recognition. In:
    <span style="font-variant:small-caps;">IEEE</span> (Hrsg.): <i>2019 IEEE 17th
    International Conference on Industrial Informatics (INDIN)</i>. Piscataway, NJ
     : IEEE, 2019, S. 296–302'
  havard: 'M. Fullen, A. Maier, A. Nazarenko, S. Jenderny, C. Röcker, Machine Learning
    for Assistance Systems: Pattern-Based Approach to Online Step Recognition, in:
    IEEE (Ed.), 2019 IEEE 17th International Conference on Industrial Informatics
    (INDIN), IEEE, Piscataway, NJ , 2019: pp. 296–302.'
  ieee: 'M. Fullen, A. Maier, A. Nazarenko, S. Jenderny, and C. Röcker, “Machine Learning
    for Assistance Systems: Pattern-Based Approach to Online Step Recognition,” in
    <i>2019 IEEE 17th International Conference on Industrial Informatics (INDIN)</i>,
    IEEE, Ed. Piscataway, NJ : IEEE, 2019, pp. 296–302.'
  mla: 'Fullen, Marta, et al. “Machine Learning for Assistance Systems: Pattern-Based
    Approach to Online Step Recognition.” <i>2019 IEEE 17th International Conference
    on Industrial Informatics (INDIN)</i>, edited by IEEE, IEEE, 2019, pp. 296–302,
    doi:<a href="https://doi.org/10.1109/INDIN41052.2019.8972122">10.1109/INDIN41052.2019.8972122</a>.'
  short: 'M. Fullen, A. Maier, A. Nazarenko, S. Jenderny, C. Röcker, in: IEEE (Ed.),
    2019 IEEE 17th International Conference on Industrial Informatics (INDIN), IEEE,
    Piscataway, NJ , 2019, pp. 296–302.'
  ufg: '<b>Fullen, Marta et. al. (2019)</b>: Machine Learning for Assistance Systems:
    Pattern-Based Approach to Online Step Recognition, in: IEEE (Hg.): <i>2019 IEEE
    17th International Conference on Industrial Informatics (INDIN)</i>, Piscataway,
    NJ , S. 296–302.'
  van: 'Fullen M, Maier A, Nazarenko A, Jenderny S, Röcker C. Machine Learning for
    Assistance Systems: Pattern-Based Approach to Online Step Recognition. In: IEEE,
    editor. 2019 IEEE 17th International Conference on Industrial Informatics (INDIN).
    Piscataway, NJ : IEEE; 2019. p. 296–302.'
conference:
  end_date: 2019-07-25
  location: Helsinki, Finland,
  name: 17th International Conference on Industrial Informatics (INDIN)
  start_date: 2019-07-22
corporate_editor:
- IEEE
date_created: 2021-01-06T13:59:10Z
date_updated: 2023-03-15T13:49:51Z
department:
- _id: DEP5023
doi: 10.1109/INDIN41052.2019.8972122
keyword:
- augmented reality
- computer based training
- data handling
- industrial training
- learning (artificial intelligence)
- time series
language:
- iso: eng
page: 296 - 302
place: 'Piscataway, NJ '
publication: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN)
publication_identifier:
  isbn:
  - 978-1-7281-2927-3
  issn:
  - 2378-363X
publication_status: published
publisher: IEEE
status: public
title: 'Machine Learning for Assistance Systems: Pattern-Based Approach to Online
  Step Recognition'
type: book_chapter
user_id: '15514'
year: 2019
...