Evaluation of Communication and Human Response Latency for (Human) Teleoperation

IEEE Transactions on Medical Robotics and Bionics(2024)

引用 0|浏览4
暂无评分
摘要
We previously introduced a novel mixed reality teleguidance system dubbed human teleoperation 1, 2, in which a human (expert) leader and a human (novice) follower are tightly coupled through mixed reality and haptics. Our first evaluation of human teleoperation is in the context of tele ultrasound, in which a sonographer or radiologist’s gestures are copied by a remote novice to carry out an ultrasound examination. In this paper, a communication system suitable for implementation of human teleoperation is presented and characterized in various network conditions, over Ethernet, Wi-Fi, 4G LTE, and 5G. To obtain a full understanding of latency in the system, the human response time is additionally characterized through a series of step response tests with 11 volunteers. The step responses were obtained by tracking the position of, and force exerted by, the human hand in response to a change in the mixed reality target. Different rendering methods were evaluated. The round-trip communication latency is 40±10 ms over 5G, and down to 1±0.6 ms over Ethernet for typical throughputs. The human response time to a step change in position depends on the step magnitude, but is between 485 to 535 ms, while the reaction time to a change in force is 150 to 200 ms. Both lag times are greatly decreased when tracking a smooth motion. Thus, we demonstrate that the system is network agnostic and can achieve good teleoperation performance and secure, low latency communication in appropriate network conditions. This brings the human teleoperation concept a step closer to human trials in a clinical environment, and the presented tools and concepts are applicable to any high-performance teleoperation system, and especially for mixed reality guidance.
更多
查看译文
关键词
Teleoperation,Human-Computer Interaction,Augmented Reality,Telemedicine,Communications
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要