超越法向力：使用3D传感器捕捉和分类剪切运动。
Moving past normal force: capturing and classifying shear motion using 3D sensors.
作者信息
Kwan Calvin, Salud Lawrence, Ononye Chiagozie, Zhao Shenshen, Pugh Carla
机构信息
Northwestern University, Department of Surgery, Feinberg School of Medicine.
出版信息
Stud Health Technol Inform. 2012;173:245-9.
Abstract
In our previous research, we used clinical breast examination models instrumented with direct (normal) force sensors for training and assessment. A weakness of the normal force sensors is the ability to delineate, in detail, all of the performance measures we wish to understand. This study incorporated the use of newly developed shear force sensors to extend a framework for quantifying hands-on performance.
摘要
在我们之前的研究中，我们使用配备直接（正常）力传感器的临床乳房检查模型进行训练和评估。正常力传感器的一个缺点是无法详细描绘我们希望了解的所有性能指标。本研究采用了新开发的剪切力传感器，以扩展用于量化实际操作性能的框架。
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A simulation-based assessment of clinical breast examination technique: do patient and clinician factors affect clinician approach?基于模拟的临床乳腺检查技术评估：患者和临床医生因素是否会影响临床医生的检查方法？
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