Efficient Medical Video Streaming by Pre-Processing and Network Traffic Prioritization in Real-Time

Balagalla, Umaya Bhashini; Sivanatham, Savansi; Munasinghe, Kusala; Subasinghe, Akila; Alwis, Chamitha de; Wijewardhana, Uditha Lakmal; Dharmaweera, Madushanka Nishan

doi:10.1109/atc.2019.8924511

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…The deformation of the soft tissue was measured based on the distance between the humerus bone surface and ultrasound probe surface. Before measuring the soft tissue deformation, we enhanced the echo image resolutions using low‐pass and median filters to reduce the speckle noise, as expressed by Attlas and Gupta (2014) and Balagalla et al (2018). Moreover, the morphological dilation technique (Dougherty & Lotufo, 2003; Maragos & Schafer, 1987) and the Canny edge detection method (Canny, 1986) were employed to enhance the boundary recognition between the humerus bone surface and echo image background.…”

Section: Data Processing and Analysismentioning

confidence: 99%

Analyzing soft tissue stiffness of human upper arms during physical dynamic and quasi‐static impacts in human–machine interaction

Rajaei

Fujikawa

Yamada

2023

Hum Ftrs & Erg Mfg Svc

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Knowledge of the changes in the behavior of human soft tissue stiffness during physical impact in human–machine interaction (HMI) plays a vital role in the development of biofidelity testing devices such as a human dummy. These testing devices are widely applied as an effective means to validate the safety of machinery during dynamic or static contact with humans in HMI. In this study, we assess changes in soft tissue stiffness in the upper arm of individuals under both dynamic (0.7 and 0.25 m/s) and quasi‐static (QS) impacts under a constrained contact condition. Three impactor shapes (cylindrical, cubic, and spherical) are used in this study. Impact experiments are conducted using impactors attached to a pendulum. The soft‐tissue displacement is determined using an ultrasound device. The impact force‐displacement curves illustrate the nonlinear behavior of the soft tissue stiffness under both dynamic and QS impacts. By utilizing the “Linear Mixed Model” statistical analysis, we found that changes in the impact velocity significantly influenced the changes in the nonlinear behavior of soft tissue stiffness while there was no significant effect of the changes in the impactor shape on the nonlinear behavior of the soft tissue stiffness. Additionally, we revealed that the changes in the soft tissue stiffness are influenced by the size of the contact area. Moreover, we demonstrated a range of changes in soft tissue stiffness for different impact velocities, which provide valuable information for developing future validation test devices in HMI, such as the design and evaluation of dummy skin.

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Section: Data Processing and Analysismentioning

confidence: 99%

Analyzing soft tissue stiffness of human upper arms during physical dynamic and quasi‐static impacts in human–machine interaction

Rajaei

Fujikawa

Yamada

2023

Hum Ftrs & Erg Mfg Svc

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“…In order to transmit these large data, different data compression algorithms have to be applied. It is also important that image streaming is a delay-sensitive operation, as the physician/researcher needs to receive the image data as soon as possible [1,2].…”

Section: Network Data Streaming In Different Areas Of Digital Medicinementioning

confidence: 99%

“…The 𝐵 𝑖𝑑 , and so the 𝐵 ℎ𝑠 , is determined each time the client program displays a new frame to the user or receives a new frame from the server. The method of determining 𝐵 𝑖𝑑 is described by Equation (1).…”

Section: The Developed Client-side Buffering Rulesmentioning

confidence: 99%

Real-Time Network Video Data Streaming in Digital Medicine

Vincze,

Molnar,

Kozlovszky

2023

Computers

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Today, the use of digital medicine is becoming more and more common in medicine. With the use of digital medicine, health data can be shared, processed, and visualized using computer algorithms. One of the problems currently facing digital medicine is the rapid transmission of large amounts of data and their appropriate visualization, even in 3D. Advances in technology offer the possibility to use new image processing, networking, and visualization solutions for the evaluation of medical samples. Because of the resolution of the samples, it is not uncommon that it takes a long time for them to be analyzed, processed, and shared. This is no different for 3D visualization. In order to be able to display digitalized medical samples in 3D at high resolution, a computer with computing power that is not necessarily available to doctors and researchers is needed. COVID-19 has shown that everyday work must continue even when there is a physical distance between the participants. Real-time network streaming can provide a solution to this, by creating a 3D environment that can be shared between doctors/researchers in which the sample being examined can be visualized. In order for this 3D environment to be available to everyone, it must also be usable on devices that do not have high computing capacity. Our goal was to design a general-purpose solution that would allow users to visualize large amounts of medical imaging data in 3D, regardless of the computational capacity of the device they are using. With the solution presented in this paper, our goal was to create a 3D environment for physicians and researchers to collaboratively evaluate 3D medical samples in an interdisciplinary way.

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Efficient Medical Video Streaming by Pre-Processing and Network Traffic Prioritization in Real-Time

Cited by 2 publications

References 19 publications

Analyzing soft tissue stiffness of human upper arms during physical dynamic and quasi‐static impacts in human–machine interaction

Analyzing soft tissue stiffness of human upper arms during physical dynamic and quasi‐static impacts in human–machine interaction

Real-Time Network Video Data Streaming in Digital Medicine

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