Naslov
Data Compression in Gesture-Based Human Machine Interface for Continuous Digital Health

Autori
Žagar, Martin ; Mutka, Alan

Izvornik
TELEMATIQUE (1856-4194) 22 (2023), 1; 69-76

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Data Compression ; Gesture recognition ; Human-machine interface ; Continues Digital Health

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA