Honourable mention award at CHI 2018
Our joint paper “Which one is me? Identifying Oneself on Public Displays” with LMU Munich won a Best Paper Honourable Mention Award at ACM CHI 2018.
![]() | Mohamed Khamis; Christian Becker; Andreas Bulling; Florian Alt Which one is me? Identifying Oneself on Public Displays Inproceedings Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 287:1-287:12, 2018, (best paper honourable mention award). @inproceedings{khamis18b_chi, title = {Which one is me? Identifying Oneself on Public Displays}, author = {Mohamed Khamis and Christian Becker and Andreas Bulling and Florian Alt}, url = {https://perceptual.mpi-inf.mpg.de/files/2018/01/khamis18b_chi.pdf https://www.youtube.com/watch?v=yG5_RBrnRx0}, doi = {10.1145/3173574.3173861}, year = {2018}, date = {2018-01-01}, booktitle = {Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI)}, journal = {Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI)}, pages = {287:1-287:12}, abstract = {While user representations are extensively used on public displays, it remains unclear how well users can recognize their own representation among those of surrounding users. We study the most widely used representations: abstract objects, skeletons, silhouettes and mirrors. In a prestudy (N=12), we identify five strategies that users follow to recognize themselves on public displays. In a second study (N=19), we quantify the users' recognition time and accuracy with respect to each representation type. Our findings suggest that there is a significant effect of (1) the representation type, (2) the strategies performed by users, and (3) the combination of both on recognition time and accuracy. We discuss the suitability of each representation for different settings and provide specific recommendations as to how user representations should be applied in multi-user scenarios. These recommendations guide practitioners and researchers in selecting the representation that optimizes the most for the deployment's requirements, and for the user strategies that are feasible in that environment.}, note = {best paper honourable mention award}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } While user representations are extensively used on public displays, it remains unclear how well users can recognize their own representation among those of surrounding users. We study the most widely used representations: abstract objects, skeletons, silhouettes and mirrors. In a prestudy (N=12), we identify five strategies that users follow to recognize themselves on public displays. In a second study (N=19), we quantify the users' recognition time and accuracy with respect to each representation type. Our findings suggest that there is a significant effect of (1) the representation type, (2) the strategies performed by users, and (3) the combination of both on recognition time and accuracy. We discuss the suitability of each representation for different settings and provide specific recommendations as to how user representations should be applied in multi-user scenarios. These recommendations guide practitioners and researchers in selecting the representation that optimizes the most for the deployment's requirements, and for the user strategies that are feasible in that environment. |