  Wearable sensor systems with transmitting capabilities are currently employed
for the biometric screening of exercise activities and other performance data.
Such technology is generally wireless and enables the noninvasive monitoring of
signals to track and trace user behaviors in real time. Examples include
signals relative to hand and finger movements or force control reflected by
individual grip force data. As will be shown here, these signals directly
translate into task, skill, and hand specific, dominant versus non dominant
hand, grip force profiles for different measurement loci in the fingers and
palm of the hand. The present study draws from thousands of such sensor data
recorded from multiple spatial locations. The individual grip force profiles of
a highly proficient left handed exper, a right handed dominant hand trained
user, and a right handed novice performing an image guided, robot assisted
precision task with the dominant or the non dominant hand are analyzed. The
step by step statistical approach follows Tukeys detective work principle,
guided by explicit functional assumptions relating to somatosensory receptive
field organization in the human brain. Correlation analyses in terms of Person
Product Moments reveal skill specific differences in covariation patterns in
the individual grip force profiles. These can be functionally mapped to from
global to local coding principles in the brain networks that govern grip force
control and its optimization with a specific task expertise. Implications for
the real time monitoring of grip forces and performance training in complex
task user systems are brought forward.
