493 research outputs found
Study of video quality assessment for telesurgery
elemedicine provides a transformative practice for access to and delivery of timely and high quality healthcare in resource-poor settings. In a typical scenario of telesurgery, surgical tasks are performed with one surgeon situated at the patient’s side and one expert surgeon from a remote site. In order to make telesurgery practice realistic and secure, reliable transmission of medical videos over large distances is essential. However, telesurgery videos that are communicated remotely in real time are vulnerable to distortions in signals due to data compression and transmission. Depending on the system and its applications, visual content received by the surgeons differs in perceived quality, which may incur implications for the performance of telesurgery tasks. To rigorously study the assessment of the quality of telesurgery videos, we performed both qualitative and quantitative research, consisting of semi-structured interviews and video quality scoring with human subjects. Statistical analyses are conducted and results show that compression artifacts and transmission errors significantly affect the perceived quality; and the effects tend to depend on the specific surgical procedure, visual content, frame rate, and the degree of distortion. The findings of the study are readily applicable to improving telesurgery systems
VIDEO PREPROCESSING BASED ON HUMAN PERCEPTION FOR TELESURGERY
Video transmission plays a critical role in robotic telesurgery because of the high bandwidth and high quality requirement. The goal of this dissertation is to find a preprocessing method based on human visual perception for telesurgical video, so that when preprocessed image sequences are passed to the video encoder, the bandwidth can be reallocated from non-essential surrounding regions to the region of interest, ensuring excellent image quality of critical regions (e.g. surgical region). It can also be considered as a quality control scheme that will gracefully degrade the video quality in the presence of network congestion. The proposed preprocessing method can be separated into two major parts. First, we propose a time-varying attention map whose value is highest at the gazing point and falls off progressively towards the periphery. Second, we propose adaptive spatial filtering and the parameters of which are adjusted according to the attention map. By adding visual adaptation to the spatial filtering, telesurgical video data can be compressed efficiently because of the high degree of visual redundancy removal by our algorithm. Our experimental results have shown that with the proposed preprocessing method, over half of the bandwidth can be reduced while there is no significant visual effect for the observer. We have also developed an optimal parameter selecting algorithm, so that when the network bandwidth is limited, the overall visual distortion after preprocessing is minimized
Congestion Control for Network-Aware Telehaptic Communication
Telehaptic applications involve delay-sensitive multimedia communication
between remote locations with distinct Quality of Service (QoS) requirements
for different media components. These QoS constraints pose a variety of
challenges, especially when the communication occurs over a shared network,
with unknown and time-varying cross-traffic. In this work, we propose a
transport layer congestion control protocol for telehaptic applications
operating over shared networks, termed as dynamic packetization module (DPM).
DPM is a lossless, network-aware protocol which tunes the telehaptic
packetization rate based on the level of congestion in the network. To monitor
the network congestion, we devise a novel network feedback module, which
communicates the end-to-end delays encountered by the telehaptic packets to the
respective transmitters with negligible overhead. Via extensive simulations, we
show that DPM meets the QoS requirements of telehaptic applications over a wide
range of network cross-traffic conditions. We also report qualitative results
of a real-time telepottery experiment with several human subjects, which reveal
that DPM preserves the quality of telehaptic activity even under heavily
congested network scenarios. Finally, we compare the performance of DPM with
several previously proposed telehaptic communication protocols and demonstrate
that DPM outperforms these protocols.Comment: 25 pages, 19 figure
Recent Advancements in Augmented Reality for Robotic Applications: A Survey
Robots are expanding from industrial applications to daily life, in areas such as medical robotics, rehabilitative robotics, social robotics, and mobile/aerial robotics systems. In recent years, augmented reality (AR) has been integrated into many robotic applications, including medical, industrial, human–robot interactions, and collaboration scenarios. In this work, AR for both medical and industrial robot applications is reviewed and summarized. For medical robot applications, we investigated the integration of AR in (1) preoperative and surgical task planning; (2) image-guided robotic surgery; (3) surgical training and simulation; and (4) telesurgery. AR for industrial scenarios is reviewed in (1) human–robot interactions and collaborations; (2) path planning and task allocation; (3) training and simulation; and (4) teleoperation control/assistance. In addition, the limitations and challenges are discussed. Overall, this article serves as a valuable resource for working in the field of AR and robotic research, offering insights into the recent state of the art and prospects for improvement
Telesurgery: Surgery in the Digital Age
The dawn of the digital age has transformed the way we now receive and provide healthcare. Today, providers have instant access to all of their patients’ information, just as patients can connect with their providers on their smartphones in minutes from nearly anywhere in the world
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