A motion capture system for the study of human manufacturing repetitive motions

A robot learning from demonstration framework for skillful small parts assembly

“…Since human operators can never perform one task multiple times within the same period. Dynamic time wrapping is a populous temporal alignment method in LfD studies [15,30,32] including this work. In assembling phase, the human operator corrupts the wrench profile estimated by joints torques [13].…”

“…To make robotic assembly applicable to lowvolume high-mix production, one approach is to make the programming of assembly tasks so intuitive that can be accomplished by workers without traditional robot programming skills [26,29]. Learning from demonstration (LfD) [23], or programming by demonstration [9], is a paradigm that aims to transfer human's skills to robots by human demonstration instead of the unintuitive and tedious robot programming by expert robot users [10,30]. The strength of LfD pays in industrial assembly applications as some assembly processes are such complicate that can be neither easily scripted nor easily defined as an optimization problem, but can be easily demonstrated [28].…”

Manufacturing Operator Ergonomics: A Conceptual Digital Twin Approach to Detect Biomechanical Fatigue

“…The scope of this research is limited to the "lifting" motion only. A segmenting filter [68] was developed in R and implemented via RStudio…”

“…The segmenting filter uses sliding window type of segmentation algorithm, to provide a piecewise representation of the time series data. Authors would like to refer the reader to[68] for more information on the segmenting filter. The activity was divided based on "Motion with Load" and "Motion without Load."…”

Modeling and Simulation of Athlete’s Error Motion Recognition Based on Computer Vision

“…Existing human motion capture technology, from the principle of this paper, can be divided into mechanical, electromagnetic, acoustic, inertial, and optical motion capture. Mechanical, electromagnetic, acoustic, and inertial motion capture equipment used in different principles of different sensors have their advantages and disadvantages, generally, from the following aspects of evaluation: wood, capture accuracy, ease of use, applicable range, real-time, anti-interference, and so on [4,5]. Most of the optical motion capture is based on the principle of computer vision, for a point in three-dimensional space, if it can be two or more cameras at the same time for the two or more cameras to see, according to the same moment the camera to obtain the image and its calibration parameters, you can calculate the three-dimensional coordinates of the point [6].…”