# Presentation: 617348

## User Specific Data Visualization in Collaborative Virtual Environments

- Denis Gračanin
*Department of Computer Science*
*Virginia Tech*
- March 31, 2004

## Collaborative Virtual Environments

- Collaborative virtual environments (CVEs) are networked virtual reality systems that support group activities.
- The central concept is a shared virtual world, a computer-generated space where participants can meet and interact.
- Users are embodied in the world by an avatar, a three-dimensional object that represents their movement, action, and presence in the space.
- The essential achievement of CVEs is that they combine the participants and the information that they access and manipulate in a single common space, and thus facilitate users’ collaborating on shared tasks.

## Challenges

- The supporting platforms and infrastructures have been restricted to application development.
- Immaturity of the product.
- Limited functionality.
- Poor application development support
- Lack of understanding of the nature of CVEs, due to the immaturity of the technologies (as compared to the variety of usability guidelines available for more-conventional interface technologies).

## CVE Layers 

**Presence & Immersion, **
**Comfort, Satisfaction**

**Distribution/**
**Architectural properties**

**Simulation **
**performance**

**User Level**

**Network Level**

**Application Level**

**Input **
**performance**

**System/task **
**performance**

**Total latency, fault tolerance, Task efficiency & accuracy, App. dependent metrics**

**Image, acoustic and tactile / haptic rendering metrics**

**Resolution/Accuracy, ****Working volume,**** Lag, Robustness, Encumbrance, Registration quality, Sociability**

**Heterogeneity, Scalability,**
**Openness,Transparency, Adaptability**

**Bandwidth, Latency, Jitter, Packet Loss rate, Packet error rate, Reliability**

## Some Interesting Problems

- What are possible metaphores for a shared virtual world?
- What is a relationship between user, application, and network level performances?
- How to insure consistency and collaboration among users?
- How to formally describe user interface and interaction between users and between user and application?
- How to “customize” individual user’s view while maintaining consistency?
- How to simulate large-scale CVEs?

## User Level Characteristics

- Immersion:
- Extensiveness, surround, inclusion, vividness, match.
- Presence:
- Psychological feeling of “being there.”
- Contributing factors: control factors , sensory factors, distraction factors, realism factors.
- Evaluation: Questionnaires and physiological parameters like heart rate, sweaty palms, breathing.
- Comfort and learning time:
- Wireless, light, accurate, safe devices preferred.
- Overall satisfaction:
- Synthetic metric, evaluated using statistical analysis.

## Subjective View

- CVEs use a shared virtual world for interactions and collaboration among users.
- Most current CVEs provide a highly objective virtual environment.
- That is, each user is presented with the same virtual world in the same way, albeit from different viewpoints.
- Some degree of subjectivity is necessary to support collaboration in virtual environments.
- Subjective view, in simple words, means “what you see is not what I see.”
- In a collaborative environment, the user may only want to see the part of the scene that is of interest.

## Shared User Interface 

- When a user interacts with an interface, the user changes the state of the user interface and also observes the result of the state change.
- When multiple users collaborate in a VE, it is required that the collaborating users keep a consistent understanding of the shared interface.
- This requires a separation between the collaborative interface specific information from the underlying application functionality.

## Formalism?

- The application functionality must be the same or nearly the same for all users in the CVE but what an individual user can do may be very different.
- The user may have available only a subset of all the controls and the same control may be presented differently to different users.
- Consistency maintained by the underlying Petri net.

## User Interest

- Objects can be classified according to user interest and marked accordingly.
- The challenge is how to assess user interest to crate appropriate subjective view.
- Manual configuration is not feasible for dynamic scene.
- A machine learning process in a CVE is used to map between subjective interest and related object attributes.

## Interest Level

- Assumptions:
- If a user manipulates one object, then the object is obviously of interest to the user.
- If the user focuses on an object for a certain amount of time during a past time period, then the user has interest in that object.
- If the user keeps focusing back on an object during the past time period, the user has interest in that object.
- Three parameters are used to decide whether an object is of interest to the user:
- Accumulated focus time,
- Focus frequency,
- Manipulation frequency.

## Providing Subjective View

- Different rendering methods for the graphics objects with different interest levels.
- For example, different color, wireframe or texture display, point rendering,or even obscuring todifferentiate objectsaccording to subjectiveinterest.
- Using CUBE game forimplementation and acase study

## Case Study

- Subjective interests determined by a Bayes network from parameters such as object identifier, distance and direction between object and local player.
- A focused object is detected: its direction from the user is within a field of view threshold.

## Interest Level

- The interest level is dependent on manipulation frequency and the focus time.
- Three manipulation levels, four focus levels, three distance, and three direction levels for data segmentation.
- User interest level for an individual object is from zero to six.
- Three distinct user interaction modes can be detected.

## Case Study Results

- The lower the interest level, the higher the life time, and vice versa.
- Indicates effectiveness of subjective view in helping the user focus on the task and completes it earlier.
- Mean interest is generally lower for subjective view.

## Conclusion

- Subjective views depend on changing user interests.
- Interest level for objects are determined by exploring human factors and establishing mapping between interest and object attributes.
- Subjective views can improve user and task performance.
- Subjective views can be used to address some CVE related network issues.
**Acknowledgment:** this presentation includes some material from papers with my Ph.D. students, Jianghui Ying and Yunxian Zhou.