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Continuing our previous work on gesture tracking in the environment of arthroplasty, and because of the complexity and potential complications, there is a need for a new approach for any application of hand gestures in a continuous digital health ecosystem, that could be easily monitored and executed. With this approach, we tend to capture hand gestures in some predefined space. The first step is to find a mathematical model that will fit some well-known kernel shapes. If a motion is not derived from the kernel shapes, the full search for motion vectors with pairing and computation of vectors could be performance exhaustive, so we propose the combination of motion based on motion prediction on kernel shapes. To predict motion parameters from specific features in specific medical data-acquiring models, we must know the visualization requirements of the continuous digital health system that will synthesize the motion. It is also necessary to relate these parameters to the actions that will enable data compression in storing all the motions in hand gestures of medical specialists controlling and manipulating medical data. This motion prediction is focusing on a specific object visualization framework that we use as an input for a gesture- based human-machine interface applicable to any continuous digital health system, which we described in this paper.

In the environment of arthroplasty operations made in orthopedic hospitals, it’s very important that patients don’t have significant leg length discrepancy (LLD), which is usually mentioned as an LLD of more than 1 cm (according to some authors even more than 5 mm is significant), so it’s important to try to avoid it because significant LLD could be a reason of other complication [1], [2], [3], [4], [5], [6], [7], [8], [9], [10] and [11]. Unfortunately, according to the literature, LLD-like complications of total hip arthroplasty are not so rare and it counts between 1% and 32% of all total hip arthroplasty [1], [12] and [13]. We strongly believe that the solution proposed by this paper could reduce these problems. Because of the complexity and potential complications, there is a need for a new approach that could be easily monitored and executed. With this approach, we will avoid the fixed markers, which could lead to various complications and prolong the operation. Our solution will make it possible to conduct millimeter precision accuracy controlled by gestures while obtaining several degrees of freedom for the surgeon. In addition, we will develop instruments that could stay outside the patient, creating minimum invasive surgery, which is not part of the current solutions