Abstract
Virtual Glove (VG) is a low-cost computer vision system that utilizes two orthogonal LEAP motion sensors to provide detailed 4D hand tracking in real–time. VG can find many applications in the field of human-system interaction, such as remote control of machines or tele-rehabilitation. An innovative and efficient data-integration strategy, based on the velocity calculation, for selecting data from one of the LEAPs at each time, is proposed for VG. The position of each joint of the hand model, when obscured to a LEAP, is guessed and tends to flicker. Since VG uses two LEAP sensors, two spatial representations are available each moment for each joint: the method consists of the selection of the one with the lower velocity at each time instant. Choosing the smoother trajectory leads to VG stabilization and precision optimization, reduces occlusions (parts of the hand or handling objects obscuring other hand parts) and/or, when both sensors are seeing the same joint, reduces the number of outliers produced by hardware instabilities. The strategy is experimentally evaluated, in terms of reduction of outliers with respect to a previously used data selection strategy on VG, and results are reported and discussed. In the future, an objective test set has to be imagined, designed, and realized, also with the help of an external precise positioning equipment, to allow also quantitative and objective evaluation of the gain in precision and, maybe, of the intrinsic limitations of the proposed strategy. Moreover, advanced Artificial Intelligence-based (AI-based) real-time data integration strategies, specific for VG, will be designed and tested on the resulting dataset.
Highlights
Computer vision is becoming increasingly important in addressing a wide range of application areas, including human action recognition [4, 16], aerial image
To integrate data from both LEAP sensors and to solve the problem of data wasting, we have considered the possibility offered by Machine Learning [24], ML, and Deep Learning [36], DL, but, though very effective, they could be either too slow or too computationally expensive to be used in a low cost machine (VG system is imagined for accurate and, in the same time, low-cost human-system interaction [20])
The Virtual Glove (VG) hardware consists of a rigid support, equipped with lodges for the orthogonal LEAP sensors Fig. 1a
Summary
Computer vision is becoming increasingly important in addressing a wide range of application areas, including human action recognition [4, 16], aerial image. The results of long-planned, critical, costly, and challenging operations depend on their proper use, that requires precise recording and reproduction of the operator’s hand and finger movements Both non-vision and vision-based gesture recognition are usually employed to finely track the hand and all its joints. LEAP is a high-resolution 3D hand-sensing device which allows the freehand natural interaction, crucial for the implementation of real-time, realistic VR systems [6, 30] It uses 3 IR light sources and two detectors o obtain 3D visual information saved and reproduced almost simultaneously (more than 60fps) from the server.
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