Experimental setup for motion capturing two humans jointly handling an object

In manual assembly, human motion capture is difficult. Reproducibly capturing two humans who are jointly handling a single part requires synchronization, communication between humans, and correcting deviations. This paper proposes an experimental setup to capture and analyze human motion data using an overhead light projector that dynamically and visually guides collaborating humans to initial and target positions. The experimental setup comprises full-body, hand-finger, and gaze-tracking sensors. Cyclic synchronization helps to reduce drift, correct position errors, and make the method easily reproducible and robust for collecting human motion data. The accuracy of object motion, which is handled by humans to execute a cooperative task, is assessed. Preliminary results show a promising output that can be scaled up for human-robot interactions using digital human models e.g., to evaluate collaboration actions and identify shared roles.

Authors

Institutions

Publication Details

Journal
Procedia CIRP
Published
2025-01-01
DOI
https://doi.org/10.1016/j.procir.2025.02.145
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Experimental setup for motion capturing two humans jointly handling an object

Rashid A. Saeed, Tadele Belay Tuli, Martin Manns
Procedia CIRP
Hand Gesture Recognition Systems
article

Experimental setup for motion capturing two humans jointly handling an object

Rashid A. Saeed, Tadele Belay Tuli, Martin Manns
article en

Abstract

In manual assembly, human motion capture is difficult. Reproducibly capturing two humans who are jointly handling a single part requires synchronization, communication between humans, and correcting deviations. This paper proposes an experimental setup to capture and analyze human motion data using an overhead light projector that dynamically and visually guides collaborating humans to initial and target positions. The experimental setup comprises full-body, hand-finger, and gaze-tracking sensors. Cyclic synchronization helps to reduce drift, correct position errors, and make the method easily reproducible and robust for collecting human motion data. The accuracy of object motion, which is handled by humans to execute a cooperative task, is assessed. Preliminary results show a promising output that can be scaled up for human-robot interactions using digital human models e.g., to evaluate collaboration actions and identify shared roles.

Procedia CIRPVol. 134
University of Siegen (DE), Folkwang University of the Arts (DE)
Deutsche Forschungsgemeinschaft, HORIZON EUROPE Framework Programme
Openalex Percentile: Top 87%
Hand Gesture Recognition Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.