2021
DOI: 10.3390/s21113748
|View full text |Cite
|
Sign up to set email alerts
|

Metrological Evaluation of Human–Robot Collaborative Environments Based on Optical Motion Capture Systems

Abstract: In the context of human–robot collaborative shared environments, there has been an increase in the use of optical motion capture (OMC) systems for human motion tracking. The accuracy and precision of OMC technology need to be assessed in order to ensure safe human–robot interactions, but the accuracy specifications provided by manufacturers are easily influenced by various factors affecting the measurements. This article describes a new methodology for the metrological evaluation of a human–robot collaborative… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

1
3
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
6
2

Relationship

1
7

Authors

Journals

citations
Cited by 8 publications
(4 citation statements)
references
References 28 publications
1
3
0
Order By: Relevance
“…This finding was unexpected, as subjects were told to accelerate during the development of the curved experiments to analyse the performance of accelerated models (CA, CTRA) compared to non-accelerated models (CV, CTRV). However, we found that the acceleration estimated from the curved baseline trajectories ( ) was on average 0.04 ± 0.09 m/s 2 , coherent with reference data for normal walking reported in the literature [ 9 ]. This value was possibly too small to make a difference in performance between the two groups of models, as confirmed by the multiple comparison test.…”
Section: Discussionsupporting
confidence: 88%
See 1 more Smart Citation
“…This finding was unexpected, as subjects were told to accelerate during the development of the curved experiments to analyse the performance of accelerated models (CA, CTRA) compared to non-accelerated models (CV, CTRV). However, we found that the acceleration estimated from the curved baseline trajectories ( ) was on average 0.04 ± 0.09 m/s 2 , coherent with reference data for normal walking reported in the literature [ 9 ]. This value was possibly too small to make a difference in performance between the two groups of models, as confirmed by the multiple comparison test.…”
Section: Discussionsupporting
confidence: 88%
“…Robots can plan their motion to adjust their actions better for more efficient and safe collaboration with humans [ 8 ]. The prediction is based on information coming from the monitorisation of the human with environmental sensors, or sensors mounted on the robot, or wearables [ 9 , 10 , 11 ]. A viable real-time prediction of human trajectory must consider the sensors and signals that will be available and whether it will be possible to achieve the reactivity or fast response that the application demands.…”
Section: Introductionmentioning
confidence: 99%
“…As early as the 1980s and 1990s, motion capture technology has gradually been active in all major film industries. With the development of motion capture technology, motion capture technology has gradually evolved from traditional wearable device [2] technology to many optical motion capture system technologies based on computer vision principles [3].…”
Section: Development Of Human Posture Technologymentioning
confidence: 99%
“…Optical motion capture is limited by factors such as light field and expensive optical cameras [2] , while inertial motion capture offers advantages. Zhao et al [3] proposed a lightweight and highly precise wearable human motion capture system based on inertial sensors.…”
Section: Introductionmentioning
confidence: 99%