Augmented Reality in Healthcare: Transforming Medical Training and Procedures

Introduction

Augmented reality plays a vital role in healthcare industry and being used by many physicians and surgeons during interventional procedures. It is predominantly use for training purpose for medical students, doctors and especially surgeons using distributed prototype. Augmented reality guide doctors in complex surgery with the help of headset and cover specific patient anatomy before surgery. A distributed training tool for endotracheal incubation (ETI) has introduced for practice and allow students for view the internal anatomy. Hence the understandability of actions is informative through human patient simulator.

Augmented Reality Medical Training Prototype

A technique to improve respiratory framework and aviation route board depends on increased reality test system. To acquire visual and material feeling of appropriate ETI technique is conceivable through consolidated human patient test system and 3D AR representation of aviation route life structures.

The trainee can view virtual 3D model of lungs superimposed by human patient simulator with the help of head mounted display and visualize the entire functions of lungs through internal anatomy as shown in the Figure 1.

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The lungs and trachea are visualized with relative position of endotracheal tube during interact with human patient simulator. Through the visual feedback one can realize the flow and work of lungs without touching a real patient. Augmented reality tool is possible to permit instructor both local and remote participants to train coincidently. Through distributed augmented reality one can understand clear action of lung in detail through human patient simulator.

Hardware Tools for System Setup

The three types of tracking probe (collections of LED) has predominately used for find the optical tracking system such as head tracking probe, HPS tracking probe and ETT tracking probe.

The head tracking probe is placed on head with HMD for find the position and orientation of 3D models. HPS tracking probe is placed on patient’s chin to determine the location and the endotracheal tube tracking probe on intubation tube and the tracking data has obtain is currently updated around 30 hertz.

Endotracheal Intubation Training

At the point when the 3D models are anatomically exact, learner wears the head mounted show and can see the inner life systems which is superimposed on human patient test system. The preparation apparatus will uncover virtual 3D models of life structures which is related to human patient test system during method is cultivated. At the point when the information outline the client's head present and endotracheal intubation present is determined by following framework that is used to recorded the virtual models with their genuine correlatives. During the information is gathered at 30hertz from optical following framework and must be conveyed quickly through that way members can see 3D models and their relative stance while endotracheal intubation system.

The correlated outcome frequency (V0), allows to estimate the capability of distributed infrastructure,

V0 = /txy (1)

Where, txy represents the average communication delay between two participants in seconds. The deployed prototype on local area network that has average delay of 1ms. Hence infrastructure supports V0 = 1/0.001s= 1000 updates/second.

Simulation with 3D Models

Graphical user interface is interface between participants and virtual model of lungs. The relevant data of lungs are shown through human patient simulator by trainee to the participants. The simulation parameters are possible to change by trainee such as the breath rate of lungs simultaneously participants can deformable model of the lungs which is superimposed by human patient simulator. There is an impediment of activity recurrence which is constrained by human-PC reaction time incorporates perceptual, subjective and process durations included by normal about 240ms.

Deployment of 3D models over same network are defined as

Vk < V0 (2)

Where, Vk represents the average action frequency initiated by members (Vk = 1/0.24 ͌ 4) and V0 =1000 update/second.

Adaptive Synchronization Algorithm for distributed interactive method:

The mechanism of event-based is targeted tracking system of shared screen for participants and which upgrade cycle is higher than the network latency (V < V0). Control packet object is providing information about position and orientation of an object actions. Also scaling, translation and rotation has takes place with the use of object data.

The synchronization calculation utilizes two ways to deal with actuate the assortment of data:

• A node measures the round-trip time to its every second time intervals.
• A fixed limit is at first utilized at each node to develop the postponed history.

To access the synchronization algorithm the amount of drift orientation between node of shared 3D object that acts on object of participating nodes. The experiment of drift behavior can be seen using ASA algorithm. The graph illustrates cycles between communication delay and jitter where the fluctuations can be seen among levels.

The repay of versatile synchronization calculation of float esteem is diminished and proceeds with the consistent level. An impact of buffering and other framework strings at the system of working framework level by clarified of sinusoidal state of pattern line. Th float edge is kept up steady an incentive with no update and roughly littler than normal float esteem.

Experimental Results and Analysis

The experiment's mathematical framework is based on the dynamics of oil drops moving in an electric field, represented by the following equations:

1. mg=k⋅v(f) - Equation 1: Describing the force balance during free fall.
2. qE=mg+k⋅v(r) - Equation 2: Capturing the dynamics when moving upwards against gravity.
3. q=mg((v(f)+v(r))/Ev(f) - Equation 3: Deriving the charge on an oil drop.

These equations facilitate the calculation of the electric charge carried by oil drops, leading to insights into the quantized nature of electric charges.

Conclusion

The work model of augmented reality for lungs has integrated using tools and tracking data for participants in different angle. Also, adaptive synchronization algorithm used for display the cycle between transmission of data and for impact of network latency. Hence the methods shown as part of augmented reality works for healthcare and how utilized for trainers.

References

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2. F. Hamza-Lup and J. P. Rolland,” Scene Synchronization for Real-Time Interaction in Distributed Mixed Reality and Virtual Reality Environments, “PRESENCE: Teleoperators and Virtual Environments, vol. 13 (2004).
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4. D. Mills, “Internet Time Synchronization: The Network Time Protocol,” IEEE Trans. Communications, vol. 39, pp. 1482-1493 (1991).