This concept paper draws from our previous research on individual grip force data collected from biosensors placed on specific anatomical locations in the dominant and non dominant hands of operators performing a robot assisted precision grip task for minimally invasive endoscopic surgery. The specificity of the robotic system on the one hand, and that of the 2D image guided task performed in a real world 3D space on the other, constrain the individual hand and finger movements during task performance in a unique way. Our previous work showed task specific characteristics of operator expertise in terms of specific grip force profiles, which we were able to detect in thousands of highly variable individual data. This concept paper is focused on two complementary data analysis strategies that allow achieving such a goal. In contrast with other sensor data analysis strategies aimed at minimizing variance in the data, it is in this case here necessary to decipher the meaning of the full extent of intra and inter individual variance in the sensor data by using the appropriate statistical analyses, as shown in the first part of this paper. Then, it is explained how the computation of individual spatio temporal grip force profiles permits detecting expertise specific differences between individual users. It is concluded that these two analytic strategies are complementary. They enable drawing meaning from thousands of biosensor data reflecting human grip performance and its evolution with training, while fully taking into account their considerable inter and intra individual variability.
翻译:本概念文件借鉴了我们以前对在主要和非主要操作者主要和非主要操作者手中特定解剖地点的生物传感器上收集的个人控制力数据的研究。一方面,机器人系统的特殊性,另一方面,在现实世界3D空间中执行的2D图像指导任务的特殊性,限制了任务执行期间个人手和手指的移动。我们以前的工作显示了操作者在特定控制力特征方面的具体专长特点,我们能够检测到数千个高度可变的个人数据。本概念文件侧重于两个辅助性的数据分析战略,以便实现这一目标。与其他旨在尽量减少数据差异的传感器数据分析战略不同,在此情况下,有必要使用本文件第一部分所显示的适当的统计分析,破解传感器数据中内部和个体之间差异的全部含义。然后,我们解释了个人抽吸力特征的计算如何使个人用户之间能够发现具体的专门知识差异。与旨在尽量减少数据差异的其他传感器数据分析战略相比,这里有必要使用适当的统计分析方法,从而破解传感器数据中个人之间差异的全部含义,同时从生物演化和人类内部演化中充分理解。