Volumetric Training

2 January 2021 20 mins read

Volumetric visual cue enhanced spatial perception in the tested MR training tasks, exhibiting increased co-presence and system usability while reducing mental workload and frustration.

Conventional training and remote collaboration systems allow users to see each other’s faces, heightening the sense of presence while sharing content like videos or slideshows. However, these methods lack depth information and a free 3D perspective of the training content. This paper investigates the impact of volumetric playback in a Mixed Reality (MR) spatial training system. We describe the MR system in a mechanical assembly scenario that incorporates various instruction delivery cues. Building upon previous research, four spatial instruction cues were explored; “Annotation”, “Hand gestures”, “Avatar”, and “Volumetric playback”. Through two user studies that simulated a real-world mechanical assembly task, we found that the volumetric visual cue enhanced spatial perception in the tested MR training tasks, exhibiting increased co-presence and system usability while reducing mental workload and frustration. We also found that the given tasks required less effort and mental load when eye gaze was incorporated. Eye gaze on its own was not perceived to be very useful, but it helped to compliment the hand gesture cues. Finally, we discuss limitations, future work and potential applications of our system.

We developed a system capable of recording and replaying instructions using four different visual cues. The prototype system was built with: an HMD (HTC Vive Pro Eye .3), three depth cameras (Azure Kinect), and Unity.4 running on a windows PC. For the assembly task, we used a motorcycle engine from a 2008 Hyosung GT 250R.5. The tasks were inspired by general engine maintenance procedures described in the service manual of the motorcycle.

We opted to use the see-through video capabilities of the HTC VIVE instead of an optical see-through AR headset as we required a very high level of precision in tracking over a large area. Additionally, portable HMD devices were ruled out as volumetric playback requires a large amount of computational resource that is not available in most mobile devices.

For recording instructions, the instructor performed real-world tasks while wearing the HMD, and the actions were saved onto the PC. Annotations were recorded by moving the HTC Vive handheld controller along the desired path while pressing the trigger button. The controller’s time and position were saved and played back in time-space synced format to recreate the instruction. Similarly, for recording gestures, the instructor’s hand movements were captured using the front-facing cameras of Vive HMD, which were played back to recreate the instruction. To record the Avatar representation, the instructor wore the HMD while holding the left and right controllers. The position and rotation of HMD and controllers were saved along with time while the instructions were performed. These values were applied to a skeleton rigged with inverse kinematics to create a virtual avatar representation. For volumetric capture, the Azure Kinect.6 cameras were used. Figure 1 shows an overview of the training system framework. The instructor performs the task while being captured by three Azure Kinect cameras placed 1.2 m apart along the vertices of an equilateral triangle to provide optimum coverage, as shown in Figure 2.

A total of 15 basic motorcycle engine maintenance tasks were selected from the service manual. We conducted an informal trial with three participants. The participants performed all fifteen tasks in all four conditions and the completion time was measured for each of the tasks. Participants also rated the tasks based on difficulty level. Analyzing the difficulty and completion time, we found that the difficult tasks tended to take the longest time. Based on this, we narrowed the task set down to nine tasks that could be classified into three hard, three medium, and three easy tasks. This classification was based on the number of individual actions required to complete the task. For example, in a medium difficulty task, the user takes the oil filler cap from the table and places it in its corresponding location. Whereas in one of the hard tasks, the user has to remove the banjo bolt that connects the radiator feed to the engine and thread it through the radiator feed, tightening it back to the engine. The final list of tasks is described in section 4.2. In any given condition, participants would receive a random subset of six instructions–two from each of the three categories. After completing an instruction, audio and visual confirmation was provided, after which the system moved on to the next instruction. This process was repeated until all of the instructions were completed and the completion time was logged at the end of each condition. In the next section, we present the user study conducted with our prototype system.

This paper presents an MR system for supernatural enhancement of training tasks that features visual cues such as annotation, hand gestures, avatar representation, and eye gaze as visual cues for instruction delivery. We found that participants felt more connected with the instructor as the main benefit of using volumetric playback as the visual cue. Based on the research questions and the results that we had, we conclude that using volumetric playback can significantly improve the sense of Social Presence and increase system usability in the MR training system. Additionally, volumetric playback reduced mental workload and frustration compared to Avatar representation and was the most preferred visual cue for our tasks.

Based on the results and feedback provided in the follow-up study that compared eye gaze with hand gestures, participants reported that the ability to see both eye-gaze and hand gestures simultaneously reduced the mental load and effort required to complete the tasks they were given, as they worked well to complement each other when one cue was lacking. They also generally ranked the gesture-only interface to be the best out of the three, as the eye gaze cue did not add much value and sometimes caused distractions by either providing misleading information or obstructing the view. Regarding the research questions and the results that were obtained, it can be concluded that hand gestures are more effective than eye gaze alone in AR assembly training. Even though combining both cues might not improve performance, it would be better to use the combination as it reduces the workload.

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