TY - JOUR
T1 - M-AR
T2 - A Visual Representation of Manual Operation Precision in AR Assembly
AU - Wang, Zhuo
AU - Bai, Xiaoliang
AU - Zhang, Shusheng
AU - He, Weiping
AU - Wang, Yang
AU - Han, Dechuan
AU - Wei, Sili
AU - Wei, Bingzhao
AU - Chen, Chengkun
N1 - Publisher Copyright:
© 2021 Taylor & Francis Group, LLC.
PY - 2021
Y1 - 2021
N2 - The research of augmented reality physical tasks for human-computer collaboration is still an attractive dynamic research field. In recent years, AR Instruction supporting collaborative assembly has been developed, which can transfer graphic annotation between man and machine. However, there is little research on high precision manual assembly. This paper proposes a high-precision cognitive AR representation (M-AR) for manual operation guidance, which aims to transform a high-precision assembly relationship into a series of visual information to meet the user’s cognition of high-precision task intention. Using this representation, we conducted a within-subject case study to compare it with original AR representation (original AR) and low-precision cognitive AR representation. The results showed that there were significant differences in execution time and operation errors. The user’s subjective feedback also showed that M-AR supporting cognitive feedback can significantly improve a user’s cognitive experience in a physical task.
AB - The research of augmented reality physical tasks for human-computer collaboration is still an attractive dynamic research field. In recent years, AR Instruction supporting collaborative assembly has been developed, which can transfer graphic annotation between man and machine. However, there is little research on high precision manual assembly. This paper proposes a high-precision cognitive AR representation (M-AR) for manual operation guidance, which aims to transform a high-precision assembly relationship into a series of visual information to meet the user’s cognition of high-precision task intention. Using this representation, we conducted a within-subject case study to compare it with original AR representation (original AR) and low-precision cognitive AR representation. The results showed that there were significant differences in execution time and operation errors. The user’s subjective feedback also showed that M-AR supporting cognitive feedback can significantly improve a user’s cognitive experience in a physical task.
UR - http://www.scopus.com/inward/record.url?scp=85104089294&partnerID=8YFLogxK
U2 - 10.1080/10447318.2021.1909278
DO - 10.1080/10447318.2021.1909278
M3 - 文章
AN - SCOPUS:85104089294
SN - 1044-7318
VL - 37
SP - 1799
EP - 1814
JO - International Journal of Human-Computer Interaction
JF - International Journal of Human-Computer Interaction
IS - 19
ER -