Enhancing Multi-user Interaction with Multi-touch Tabletop Displays Using Hand Tracking

Dohse, K. C.; Dohse, Thomas; Still, Jeremiah D.; Parkhurst, Derrick

doi:10.1109/achi.2008.11

Cited by 56 publications

(40 citation statements)

References 12 publications

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“…On the other hand it was noted that users of Multi-touch displayed fewer grammatical errors than those of multi-mouse. A study by [12] shows that Multi-touch tabletops increase the awareness and common ground of group members working collaboratively to achieve a specific outcome, as well as increasing the effectiveness of group tasks and obligations [13]. From the aforementioned research studies, it can be concluded that Multi-touch tabletops enhance group interaction and therefore enhance the realization of group goals.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Learning Skills in Multi-touch Tables for UML Software Design

Basheri¹,

Munro²,

Burd³

et al. 2013

IJACSA

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Abstract-The use of Multi-touch interfaces for collaborative learning has received significant attention. Their ability to synchronously accommodate multiple users is an advantage in co-located collaborative design tasks. This paper explores the Multi-touch interface's potential in collaborative Unified Modeling Language diagramming by comparing it to a PC-based tool, looking at the Collaborative Learning Skills and amount of physical interactions in both conditions. The results show that even though participants talked more in the PC-based condition, the use of the Multi-touch table increased the amount of physical interactions, and encouraged the "Creative Conflict" skills amongst the team members.

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Section: Related Workmentioning

confidence: 99%

Collaborative Learning Skills in Multi-touch Tables for UML Software Design

Basheri¹,

Munro²,

Burd³

et al. 2013

IJACSA

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“…The problem of matching touches with users must, however, be solved by additional tracking systems based on analysis of the user position with additional external cameras and is subject to errors [14].…”

Section: Related Workmentioning

confidence: 99%

Using multiple sensors for reliable markerless identification through supervised learning

Albarelli

Bergamasco

Celentano

et al. 2013

Machine Vision and Applications

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In many interaction models involving an active surface, there is a need to identify the specific object that performs an action. This is the case, for instance, when interactive contents are selected through differently shaped physical objects, or when a two-way communication is sought as the result of a touch event. When the technological facility is based on image processing, fiducial markers become the weapon of choice in order to associate a tracked object to its identity. Such approach, however, requires a clear and unoccluded view of the marker itself, which is not always the case. We came across this kind of hurdle during the design of a very large multi-touch interactive table. In fact, the thickness of the glass and the printed surface, which were required for our system, produced both blurring and occlusion at a level such that markers were completely unreadable. To overcome these limitations we propose an identification approach based on SVM that exploits the correlation between the optical features of the blob, as seen by the camera, and the data coming from active sensors available on the physical object that interacts with the table. This way, the recognition has been cast into a classification problem that can be solved through a standard

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“…Although it is designed for tablets, the principles they use could be extended to tabletops in the near future. Dohse et al (Dohse, Dohse, Still, & Parkhurst, 2008) propose the use of an external camera situated above the surface to infer the position of the users capturing their hands and arms. Schmidt et al present HandsDown (Schmidt, Chong, & Gellersen, 2010), a way of user identification based on the contour of the hands and the posterior tracking of the user using also an external camera.…”

Section: Related Workmentioning

confidence: 99%

“…Some drawbacks are identified by their respective authors, such as that the users are tied to a fixed location (Dietz & Leigh, 2001) (Dohse, Dohse, Still, & Parkhurst, 2008), the recognition is not very accurate when several users are working on the same side of the table (Dohse, Dohse, Still, & Parkhurst, 2008), and the number of users is limited (Dietz & Leigh, 2001) (Harrison, Sato, & Poupyrev, 2012) (Marquardt, Kiemer, Ledo, Boring, & Greenberg, 2011) (Meyer & Schmidt, 2010) (Roth, Schmidt, & Güldenring, 2010). Also, the use of wearable hardware (Marquardt, Kiemer, Ledo, Boring, & Greenberg, 2011) (Meyer & Schmidt, 2010) (Roth, Schmidt, & Güldenring, 2010) may result uncomfortable for some people, and it usually requires a previous registration from the users (also observed in (Schmidt, Chong, & Gellersen, 2010)), which is not a very natural way of starting the interactions.…”

Section: Related Workmentioning

confidence: 99%

Evaluating heuristics for tabletop user segmentation based on simultaneous interaction

Garcia-Sanjuan

Jaén

Catalá

2013

Expert Systems with Applications

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ElsevierGarcía Sanjuan, F.; Jaén Martínez, FJ.; Catalá Bolós, A. (2013) AbstractDifferentiating between users that interact on a tabletop could be beneficial for collaborative tasks to support territoriality-oriented features such as a more efficient space management or a better presentation of the contents. In this paper, we design a novel algorithm for the user differentiation or segmentation based on the simultaneous manipulation of controls. This is a potential differentiating factor that has remained unexplored so far, and in combination with other factors may become relevant to successfully accomplish such differentiation task. Basically it relies on the idea that users manipulate digital elements with a single hand, and hence, if two controls are being used at the same time, they most likely belong to different users. On the generic algorithm, three different versions have been implemented that include several heuristics to address the problem. The comparison under a simulated experiment shows that the heuristic involving more knowledge on distances on user controls performed better according to different goodness functions. This shows promising to further development and refinement of the approach by expanding it with other potential factors to eventually build a robust user differentiation subsystem.

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Enhancing Multi-user Interaction with Multi-touch Tabletop Displays Using Hand Tracking

Cited by 56 publications

References 12 publications

Collaborative Learning Skills in Multi-touch Tables for UML Software Design

Collaborative Learning Skills in Multi-touch Tables for UML Software Design

Using multiple sensors for reliable markerless identification through supervised learning

Evaluating heuristics for tabletop user segmentation based on simultaneous interaction

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