Motion Capturing in Learning Factories-Technologies and Application Scenarios

Victor Bittencourt; Sri Kolla; Sebastian Thiede

doi:10.1007/978-3-031-98883-7_22

Motion Capturing in Learning Factories-Technologies and Application Scenarios

Victor Bittencourt^*, Sri Kolla, Sebastian Thiede

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

This paper explores the integration of motion capturing (mocap) technologies within Learning Factory (LF) environments, focusing on their application in ergonomic assessment. This study employed both optical and inertial mocap systems to record and analyze the movements of workers in a simulated manufacturing scenario. The collected data was used to compute ergonomic scores, aiming to enhance worker well-being in performing manufacturing tasks. A comparison between optical marker-less and inertial mocap systems was conducted, assessing factors such as intrusiveness, setup complexity, technical effort, and ergonomic scores. To further extend the existing LF, a new and heavier product, a battery pack, was introduced to simulate more demanding manufacturing tasks. This study provides valuable insights into the role of mocap technologies in improving worker ergonomics and enhancing the realism of LF scenarios.

Original language	English
Title of host publication	Advancing Learning Factories
Subtitle of host publication	Enabling Future-Ready Skills - Proceedings of the 15th Conference on Learning Factories, 2025
Editors	Louis Louw, Vera Hummel, Imke de Kock, Konrad von Leipzig, Vera Hummel
Place of Publication	Cham
Publisher	Springer
Pages	184-191
Number of pages	8
ISBN (Electronic)	978-3-031-98883-7
ISBN (Print)	978-3-031-98882-0
DOIs	https://doi.org/10.1007/978-3-031-98883-7_22
Publication status	Published - 2025
Event	15th International Conference on Learning Factories, CLF 2025 - Stellenbosch University, Stellenbosch, South Africa Duration: 25 Mar 2025 → 28 Mar 2025 Conference number: 15 https://clf2025.org/

Publication series

Name	Lecture Notes in Networks and Systems
Publisher	Springer
Volume	1546
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	15th International Conference on Learning Factories, CLF 2025
Abbreviated title	CLF 2025
Country/Territory	South Africa
City	Stellenbosch
Period	25/03/25 → 28/03/25
Internet address	https://clf2025.org/

Keywords

NLA
Ergonomic assessment
Ergonomics
Human centered manufacturing
Industry 5.0
Learning factories
Motion capturing
Digital human modeling

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-98883-7_22

Cite this

Bittencourt, V., Kolla, S., & Thiede, S. (2025). Motion Capturing in Learning Factories-Technologies and Application Scenarios. In L. Louw, V. Hummel, I. de Kock, K. von Leipzig, & V. Hummel (Eds.), Advancing Learning Factories: Enabling Future-Ready Skills - Proceedings of the 15th Conference on Learning Factories, 2025 (pp. 184-191). (Lecture Notes in Networks and Systems; Vol. 1546). Springer. https://doi.org/10.1007/978-3-031-98883-7_22

Bittencourt, Victor ; Kolla, Sri ; Thiede, Sebastian. / Motion Capturing in Learning Factories-Technologies and Application Scenarios. Advancing Learning Factories: Enabling Future-Ready Skills - Proceedings of the 15th Conference on Learning Factories, 2025. editor / Louis Louw ; Vera Hummel ; Imke de Kock ; Konrad von Leipzig ; Vera Hummel. Cham : Springer, 2025. pp. 184-191 (Lecture Notes in Networks and Systems).

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title = "Motion Capturing in Learning Factories-Technologies and Application Scenarios",

abstract = "This paper explores the integration of motion capturing (mocap) technologies within Learning Factory (LF) environments, focusing on their application in ergonomic assessment. This study employed both optical and inertial mocap systems to record and analyze the movements of workers in a simulated manufacturing scenario. The collected data was used to compute ergonomic scores, aiming to enhance worker well-being in performing manufacturing tasks. A comparison between optical marker-less and inertial mocap systems was conducted, assessing factors such as intrusiveness, setup complexity, technical effort, and ergonomic scores. To further extend the existing LF, a new and heavier product, a battery pack, was introduced to simulate more demanding manufacturing tasks. This study provides valuable insights into the role of mocap technologies in improving worker ergonomics and enhancing the realism of LF scenarios.",

keywords = "NLA, Ergonomic assessment, Ergonomics, Human centered manufacturing, Industry 5.0, Learning factories, Motion capturing, Digital human modeling",

author = "Victor Bittencourt and Sri Kolla and Sebastian Thiede",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 15th International Conference on Learning Factories, CLF 2025, CLF 2025 ; Conference date: 25-03-2025 Through 28-03-2025",

year = "2025",

doi = "10.1007/978-3-031-98883-7\_22",

language = "English",

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series = "Lecture Notes in Networks and Systems",

publisher = "Springer",

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editor = "Louis Louw and Vera Hummel and \{de Kock\}, Imke and \{von Leipzig\}, Konrad and Vera Hummel",

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}

Bittencourt, V , Kolla, S & Thiede, S 2025, Motion Capturing in Learning Factories-Technologies and Application Scenarios. in L Louw, V Hummel, I de Kock, K von Leipzig & V Hummel (eds), Advancing Learning Factories: Enabling Future-Ready Skills - Proceedings of the 15th Conference on Learning Factories, 2025. Lecture Notes in Networks and Systems, vol. 1546, Springer, Cham, pp. 184-191, 15th International Conference on Learning Factories, CLF 2025, Stellenbosch, South Africa, 25/03/25. https://doi.org/10.1007/978-3-031-98883-7_22

Motion Capturing in Learning Factories-Technologies and Application Scenarios. / Bittencourt, Victor ; Kolla, Sri ; Thiede, Sebastian.
Advancing Learning Factories: Enabling Future-Ready Skills - Proceedings of the 15th Conference on Learning Factories, 2025. ed. / Louis Louw; Vera Hummel; Imke de Kock; Konrad von Leipzig; Vera Hummel. Cham: Springer, 2025. p. 184-191 (Lecture Notes in Networks and Systems; Vol. 1546).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Motion Capturing in Learning Factories-Technologies and Application Scenarios

AU - Bittencourt, Victor

AU - Kolla, Sri

AU - Thiede, Sebastian

N1 - Conference code: 15

PY - 2025

Y1 - 2025

N2 - This paper explores the integration of motion capturing (mocap) technologies within Learning Factory (LF) environments, focusing on their application in ergonomic assessment. This study employed both optical and inertial mocap systems to record and analyze the movements of workers in a simulated manufacturing scenario. The collected data was used to compute ergonomic scores, aiming to enhance worker well-being in performing manufacturing tasks. A comparison between optical marker-less and inertial mocap systems was conducted, assessing factors such as intrusiveness, setup complexity, technical effort, and ergonomic scores. To further extend the existing LF, a new and heavier product, a battery pack, was introduced to simulate more demanding manufacturing tasks. This study provides valuable insights into the role of mocap technologies in improving worker ergonomics and enhancing the realism of LF scenarios.

AB - This paper explores the integration of motion capturing (mocap) technologies within Learning Factory (LF) environments, focusing on their application in ergonomic assessment. This study employed both optical and inertial mocap systems to record and analyze the movements of workers in a simulated manufacturing scenario. The collected data was used to compute ergonomic scores, aiming to enhance worker well-being in performing manufacturing tasks. A comparison between optical marker-less and inertial mocap systems was conducted, assessing factors such as intrusiveness, setup complexity, technical effort, and ergonomic scores. To further extend the existing LF, a new and heavier product, a battery pack, was introduced to simulate more demanding manufacturing tasks. This study provides valuable insights into the role of mocap technologies in improving worker ergonomics and enhancing the realism of LF scenarios.

KW - NLA

KW - Ergonomic assessment

KW - Ergonomics

KW - Human centered manufacturing

KW - Industry 5.0

KW - Learning factories

KW - Motion capturing

KW - Digital human modeling

UR - https://www.scopus.com/pages/publications/105018671319

U2 - 10.1007/978-3-031-98883-7_22

DO - 10.1007/978-3-031-98883-7_22

M3 - Conference contribution

AN - SCOPUS:105018671319

SN - 978-3-031-98882-0

T3 - Lecture Notes in Networks and Systems

SP - 184

EP - 191

BT - Advancing Learning Factories

A2 - Louw, Louis

A2 - Hummel, Vera

A2 - de Kock, Imke

A2 - von Leipzig, Konrad

A2 - Hummel, Vera

PB - Springer

CY - Cham

T2 - 15th International Conference on Learning Factories, CLF 2025

Y2 - 25 March 2025 through 28 March 2025

ER -

Bittencourt V , Kolla S , Thiede S. Motion Capturing in Learning Factories-Technologies and Application Scenarios. In Louw L, Hummel V, de Kock I, von Leipzig K, Hummel V, editors, Advancing Learning Factories: Enabling Future-Ready Skills - Proceedings of the 15th Conference on Learning Factories, 2025. Cham: Springer. 2025. p. 184-191. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-98883-7_22

Motion Capturing in Learning Factories-Technologies and Application Scenarios

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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