  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.
