Measuring Motion Capture Data Quality for Data Driven Human Motion Synthesis

Manns, Martin; Otto, Michael; Mauer, Markus Auf der

doi:10.1016/j.procir.2015.12.068

Cited by 23 publications

(6 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compare wrist trajectories: Motion captured wrist trajectories from ( 7) are compared with simulated ones from (8) using the FPCA approach (s. Sect. 6.2) that was presented in [20] and an RMS approach (s. Sect. 6.1).…”

Section: Methodsmentioning

confidence: 99%

“…The robot is proposed to perform gluing operation, while the human worker is fixing the position following the robot's motion. Considering only the joint motions, a methodology that extends a previously presented approach, that has not considered joint activities with robots, is proposed for measuring motion capture quality [20]. In order to gain insight into its principle applicability and accuracy, it is tested with two motion capture systems and two participants of different height and size for preliminary investigation and testing.…”

Section: Objectivementioning

confidence: 99%

“…The eigenvalues are used to analyze how the principal components are oriented, and this is useful to compare the measured data with the reference frame. The higher the explained variation, the higher the motion naturalness (c. [8,20]). In our aspect, the motions are supposed to be planar.…”

Section: Deviation With Respect To the Principal Componentsmentioning

confidence: 99%

See 2 more Smart Citations

Human motion quality and accuracy measuring method for human–robot physical interactions

Tuli

Manns

Zeller

2022

Intel Serv Robotics

Self Cite

View full text Add to dashboard Cite

In human–robot collaboration (HRC), human motion capture can be considered an enabler for switching autonomy between humans and robots to create efficient and safe operations. For this purpose, wearable motion tracking systems such as IMU and lighthouse-based systems have been used to transfer human joint motions into robot controller models. Due to reasons such as global positioning, drift, and occlusion, in some situations, e.g., HRC, both systems have been combined. However, it is still not clear if the motion quality (e.g., smoothness, naturalness, and spatial accuracy) is sufficient when the human operator is in the loop. This article presents a novel approach for measuring human motion quality and accuracy in HRC. The human motion capture has been implemented in a laboratory environment with a repetition of forty-cycle operations. Human motion, specifically of the wrist, is guided by the robot tool center point (TCP), which is predefined for generating circular and square motions. Compared to the robot TCP motion considered baseline, the hand wrist motion deviates up to 3 cm. The approach is valuable for understanding the quality of human motion behaviors and can be scaled up for various applications involving human and robot shared workplaces.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Objectivementioning

confidence: 99%

See 1 more Smart Citation

Human motion quality and accuracy measuring method for human–robot physical interactions

Tuli

Manns

Zeller

2022

Intel Serv Robotics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Besides classification, authors have reported using PCA-based features for walking motion synthesis [3,21,22], motion segmentation [23], keyframe extraction [24], mapping MoCap data to a servos system of a humanoid robot [4] (the authors named their approach Eigenposes), compensating for the effect of sensor position changes during a MoCap session [25], and the synchronization of motion sequences from different sources [26].…”

Section: Recent Work In the Application Of Pca For Human Motion Analysismentioning

confidence: 99%

Improving Human Motion Classification by Applying Bagging and Symmetry to PCA-Based Features

Hachaj

2019

Symmetry

View full text Add to dashboard Cite

This paper proposes a method for improving human motion classification by applying bagging and symmetry to Principal Component Analysis (PCA)-based features. In contrast to well-known bagging algorithms such as random forest, the proposed method recalculates the motion features for each “weak classifier” (it does not randomly sample a feature set). The proposed classification method was evaluated on a challenging (even to a human observer) motion capture recording dataset of martial arts techniques performed by professional karate sportspeople. The dataset consisted of 360 recordings in 12 motion classes. Because some classes of these motions might be symmetrical (which means that they are performed with a dominant left or right hand/leg), an analysis was conducted to determine whether accounting for symmetry could improve the recognition rate of a classifier. The experimental results show that applying the proposed classifiers’ bagging procedure increased the recognition rate (RR) of the Nearest-Neighbor (NNg) and Support Vector Machine (SVM) classifiers by more than 5% and 3%, respectively. The RR of one trained classifier (SVM) was higher when we did not use symmetry. On the other hand, the application of symmetry information for bagged NNg improved its recognition rate compared with the results without symmetry information. We can conclude that symmetry information might be helpful in situations in which it is not possible to optimize the decision borders of the classifier (for example, when we do not have direct information about class labels). The experiment presented in this paper shows that, in this case, bagging and mirroring might help find a similar object in the training set that shares the same class label. Both the dataset that was used for the evaluation and the implementation of the proposed method can be downloaded, so the experiment is easily reproducible.

show abstract

“…Their work is extended by Du et al [DMHF16] transferring the approach to scenarios related to assembly workshops. Furthermore, Manns et al investigate the influence of input data to the effectiveness of this approach [MOM16] and point out the considerable requirements in both quality and quantity for adapting the methodology to common shop-floor motions [MMM16].…”

Section: Statistical Motion Synthesismentioning

confidence: 99%

A Probabilistic Steering Parameter Model for Deterministic Motion Planning Algorithms

Agethen

Gaisbauer

Rukzio³

2018

Computer Graphics Forum

View full text Add to dashboard Cite

The simulation of two‐dimensional human locomotion in a bird's eye perspective is a key technology for various domains to realistically predict walk paths. The generated trajectories, however, are frequently deviating from reality due to the usage of simplifying assumptions. For instance, common deterministic motion planning algorithms predominantly utilize a set of static steering parameters (e.g. maximum acceleration or velocity of the agent) to simulate the walking behaviour of a person. This procedure neglects important influence factors, which have a significant impact on the spatio‐temporal characteristics of the finally resulting motion—such as the operator's physical conditions or the probabilistic nature of the human locomotor system. In overcome this drawback, this paper presents an approach to derive probabilistic motion models from a database of captured human motions. Although being initially designed for industrial purposes, this method can be applied to a wide range of use cases while considering an arbitrary number of dependencies (input) and steering parameters (output). To underline its applicability, a probabilistic steering parameter model is implemented, which models velocity, angular velocity and acceleration as a function of the travel distances, path curvature and height of a respective person. Finally, the technical performance and advantages of this model are demonstrated within an evaluation.

show abstract

Measuring Motion Capture Data Quality for Data Driven Human Motion Synthesis

Cited by 23 publications

References 20 publications

Human motion quality and accuracy measuring method for human–robot physical interactions

Human motion quality and accuracy measuring method for human–robot physical interactions

Improving Human Motion Classification by Applying Bagging and Symmetry to PCA-Based Features

A Probabilistic Steering Parameter Model for Deterministic Motion Planning Algorithms

Contact Info

Product

Resources

About