Making Sense of Complex Sensor Data Streams

TL;DR

This paper introduces two complementary data analysis strategies for interpreting complex biosensor data on grip force during robot-assisted surgery, enabling detection of expertise-related differences despite high variability.

Contribution

It presents novel statistical and spatio-temporal analysis methods to interpret high-variability sensor data in surgical grip force studies.

Findings

Effective deciphering of intra and inter individual variance.

Detection of expertise-specific grip force profiles.

Complementary analysis strategies enhance understanding of sensor data.

Abstract

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.