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Motion marker discovery from inertial body sensors for enhancing objective assessment of robotic surgical skills

机译:从惯性人体传感器中发现运动标记,以增强对机器人手术技能的客观评估

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Robotic surgery simulators are increasingly being used for education in several surgical specialties. However, the surgical skills assessment provided by the robotic simulator is only the statistical evaluation of the stimulus input from the surgeons to the hand and foot actuators, ignoring other body movements and postures that are critical to surgical performance. As a result, performance assessments to surgical experts are not significantly higher than those achieved by novices, and surgical residents do not get the meaningful feedback to improve their skills. In order to enhance the assessment of robotic surgical skills, we employed inertial body sensors and developed a novel motion marker discovery method to determine the differences in motion patterns between expert surgeons and novices on a robotic simulator (Mimic Technologies, Seattle, WA). 4 wireless inertial body sensor nodes capable of 6 degree of freedom sensing were unobtrusively attached to a subject on left wrist, right wrist, right upper arm, and left ankle and used to measure motion during a task. 6 experts and 26 novices participated and performed the Energy Dissection task. Preliminary findings suggested that training with inertial sensor nodes can enhance the robotic surgical training process in a limited amount of time.
机译:机器人外科手术模拟器越来越多地用于几个外科专业的教育中。但是,由机器人模拟器提供的手术技能评估只是对从外科医生到手和脚执行器的刺激输入的统计评估,而忽略了对手术性能至关重要的其他身体运动和姿势。结果,对外科专家的绩效评估并没有比新手获得的评估高得多,并且外科住院医师没有获得有意义的反馈来提高他们的技能。为了增强对机器人手术技能的评估,我们采用了惯性人体传感器并开发了一种新颖的运动标记物发现方法,以确定机器人模拟器(华盛顿州西雅图市的Mimic Technologies)上的专家外科医生与新手之间的运动方式差异。将能够进行6个自由度感测的4个无线惯性人体传感器节点毫不干扰地连接到左手腕,右手腕,右上臂和左脚踝上的对象,并用于在任务期间测量运动。 6位专家和26位新手参加并执行了“能量解剖”任务。初步发现表明,使用惯性传感器节点进行训练可以在有限的时间内增强机器人外科手术训练过程。

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