WiMi Hologram Cloud Inc (WIMI)

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WiMi To Develop A Multi-User Eye Tracking Data Visualization System.

Source
https://www.newstrail.com/wimi-to-develop-a-multi-user-eye-tracking-data-visualization-system/

November 30, 2022

With the development of information technology, human-computer interaction has been extended from the simple input of mouse, keyboard, joystick, and other devices to interaction based on physiological information, posture, voice, etc. Eye tracking technology is a new kind of human-computer interaction technology. The eye is one of the most complex structures in the human body and the main channel through which humans acquire information. Eye movement tracking technology is to track eye movements by measuring the position of the eye’s fixation point or the eye or the movement of the eye relative to the head to monitor the user’s eye movement and fixation direction when looking at a specific target.

It is said that the WiMi Hologram Cloud, Inc. (NASDAQ:WIMI) is developing a multi-user eye movement tracking data visualization system for collaborative interaction. With the development of digital image processing technology and the deepening of research on computer-supported cooperative work, eye movement tracking technology will be applied in multi-user collaborative interaction. The perception of multi-user collaborative information will be realized through the sharing of eye movement data.

However, in multi-user collaborative interaction, text, voice, gesture, and other forms of interaction cannot accurately express the semantics of multi-user collaborative interaction, and how to form an organic cooperative body of multi-user visual attention resources has also become a difficulty. To solve this complex problem, WiMi is developing a multi-user eye-tracking data visualization system for collaborative interaction. Through the real-time acquisition, calculation, and transmission of the eye-movement tracking data of different users in the collaborative environment and the analysis and visualization of the eye-movement tracking data, the collaborative perception of the visual attention behavior and interaction intention in the process of multi-user collaborative interaction is realized. On this basis, the corresponding system architecture and modules are designed.

WiMi uses eye-tracking data visualization technology to reduce semantic ambiguity and improve the quality and efficiency of multi-user collaborative interaction. Through the sharing mechanism, individual users can match other users’ visual attention resources according to their goals and intentions to reduce their cognitive load and improve interaction efficiency and subjective experience.

To reasonably distribute the computing load generated by the recording and processing of eye-tracking data and expand the number of multiple users in the collaborative environment as much as possible, The multi-user collaborative eye-tracking calculation studied by WiMi adopts the client/ server (C/S) structure wherein, the server is responsible for recording the eye tracking data of the client, processing, controlling and forwarding it. The client is responsible for calculating, requesting, and visualizing the eye-tracking data. All the clients only communicate with the server. Instead of directly connecting, the clients transmit data through the server to realize mutual communication.
The advantage of this computing architecture is that the server provides a unified data forwarding mode, and all the client’s data is aggregated on the server side, which is convenient for the server to carry out suitable processing and distribution. The client supports eye-tracking calibration, eye-tracking data calculation, and other specific work and then sends it to the server.


The collaborative, interactive, multi-user eye-tracking data visualization system developed by WiMi can be applied to many industries and fields, such as education, intelligent driving, medical image analysis, and training. Instead of voice interaction, it can effectively assist users in cooperation and division of labor and enhance group cognition.
For example, two eye-movement tracking systems are used in the classroom teaching scenario to record the eye-movement data of pairs of students and share them as joint attention resources.

For the learning content of conceptual knowledge,
the more joint attention resources, the better the learning effect.
- In the car driving scene, the passenger sitting in the passenger seat can provide the driver with road condition information to compensate for the driver’s inadequate access to information due to the blind area of vision. Therefore, recording the passenger’s fixation point in the passenger seat and presenting it to the driver visually can help the driver perceive the relevant road condition information and improve driving safety.
- In the telemedicine scenario, members of the international medical consultation and surgery team come from different countries and regions, which may cause language communication barriers. Therefore, in the simulated surgery process, a combination of language and eye movement is used to give cooperative instructions so that different members can cooperate to complete the target selection task.

Experimental results show that the accuracy and efficiency of eye movement are higher than that of language collaboration. In software engineering, multi-user eye tracking and visualization of eye tracking data are used to promote multi-user collaboration and division of labor in collaborative work and improve code review efficiency.

In the collaborative work scenario, the eye tracker is used to record the eye movement fixation points of multiple users when they cooperate to complete the search task and display them on the public screen so that users can see the area that the collaborators are searching, to avoid repeated consciously and redundant search and improve efficiency. By analyzing the distribution law of eye movement fixation points when multiple users complete collaborative search tasks, it is found that users’ distribution of eye movement fixation points varies significantly under different interaction modes. Especially in the visual sharing of eye movement data, the distribution of eye movement fixation points of different users tends to compensate each other, the sense of division of labor is more apparent, and the task completion time is shorter, which significantly improves the efficiency of collaborative work.

Eye-tracking opens up a rich new experience of human-computer interaction. In the future, WiMi plans to combine eye-tracking technology with virtual reality so that users can have a more realistic immersive experience and accurately grasp the visual attention information of users, which brings new possibilities for creating virtual scenes with higher immersion and has broad application prospects in entertainment, shopping, medical treatment, and other fields.