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U.S. Food and Drug Administration

A Link-Level Traffic Modeling Method for Networked Medical Extended Reality (MXR) Applications

Catalog of Regulatory Science Tools to Help Assess New Medical Devices 

This regulatory science tool is a method that models the link-level traffic patterns in medical extended reality (MXR) applications, which is intended to help recognize application-specific data transmission requirements in IP-based connected medical devices.

Technical Description

This RST offers a traffic modeling method for characterizing IP-based application traffic flows carried by communication links in networked medical extended reality (MXR) applications [1]. This tool is intended to assist users (e.g., medical device manufacturers, communication solution providers, and test labs) to:

  • Capture the application’s unique traffic transmission pattern considering temporal resolutions for occurrences and load variations,
  • Help in translating device data communication requirements into salient transmission specifications and
  • Assess the performance of various network connectivity modalities in serving a device by replicating the device’s traffic in simulated and real-time link load tests. 

The RST acquires MXR traffic models using a data-oriented workflow that consists of general procedures and considerations for modeling traffic patterns in various MXR applications. For example, the tool correlates network traffic with specific MXR deployments (e.g., remote rendering, display mirroring) and user events such as head rotation, hand movements, and image downloads, which aids in assessing MXR connectivity risks associated with application-specific operations. The tool also provides implementation examples of traffic models such as pseudo code for replicating link traffic and enabling comparison of complexity among operational algorithms.

Intended Purpose 

The RST provides a data-oriented workflow for obtaining link-level traffic patterns in communication links of MXR applications and depicting statistical variations of link traffic load, which can help the user  clarify quality of service (QoS) requirements on link connectivity for supporting specific MXR device functions. This tool aids medical device innovators in efficiently recognizing their device’s transmission needs. Additionally, it offers reference traffic for network engineers to design and optimize connectivity solutions for emerging MXR use cases, aiding in the development of new communication protocols for network QoS.

Testing

Tool utility was showcased through a comparison between the replicated traffic load and the measured load from actual MXR application instances for three different use cases encompassing varied user behaviors, traffic loads, and messaging protocols [1]. Additionally, the usability of the obtain traffic models was confirmed through a network simulation study where the traffic models were converted into the NS-3 code and used with a 5G mmWave network simulator developed by the National Institute of Standards and Technology (NIST) to evaluate the impact of different 5G mmWave base station positions on the link QoS fulfillment [2]. 

Limitations

  • This RST does not establish or imply acceptable MXR traffic QoS thresholds.
  • This RST does not establish test acceptance criteria.
  • This RST does not define device requirements or specifications for users. The designers should refer to their device specifications and corresponding evaluation needs to identify and customize the link traffic profile following the general principles outlined in this tool.
  • Users should verify the adequacy of MXR traffic models acquired for their applications by referring to pertinent product design documentation and communication protocols.
  • The test data from the MXR traffic model demonstration [2] was specific to the tested software and simulation environments, and should not generalize network performance or connectivity behavior for communication networks or 5G-enabled MXR devices.

Supporting Documentation

Full description of the link-level traffic modeling method including end-to-end workflow, measurement setup illustration, application-specific implementation details, and verification discussions was provided in the article below:

1. Y. Liu and M. O. Al Kalaa, "Link-Level Traffic Modeling of Medical Extended Reality (MXR) Applications," in IEEE Access, vol. 12, pp. 39166-39185, 2024, doi: 10.1109/ACCESS.2024.3374230.

A use case example of applying the MXR traffic modeling findings in a networked medical device evaluation study was reported in the article below:

2. T. Ropitault, Y. Liu, R. Rouil and M. O. Al Kalaa, “A Simulation Study of mmWave 5G-Enabled Medical Extended Reality (MXR),” 2024 IEEE International Conference on Communications Workshops (ICC Workshops), Denver, CO, USA, 2024, pp. 1907-1912, doi: 10.1109/ICCWorkshops59551.2024.10615578.

Python code demos in Jupyter Notebook documents with MXR traffic model examples and RST workflow showcases: available on GitHub repo (https://github.com/dbp-osel/MXR-Traffic-Modeling/tree/main)

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