Abstract:
Abstract: New technologies for monitoring grip forces during hand and finger movements in nonstandard
task contexts have provided unprecedented functional insights into somatosensory cognition.
Somatosensory cognition is the basis of our ability to manipulate and transform objects
of the physical world and to grasp them with the right amount of force. In previous work, the
wireless tracking of grip-force signals recorded from biosensors in the palm of the human hand has
permitted us to unravel some of the functional synergies that underlie perceptual and motor learning
under conditions of non-standard and essentially unreliable sensory input. This paper builds on
this previous work and discusses further, functionally motivated, analyses of individual grip-force
data in manual robot control. Grip forces were recorded from various loci in the dominant and
non-dominant hands of individuals with wearable wireless sensor technology. Statistical analyses
bring to the fore skill-specific temporal variations in thousands of grip forces of a complete novice
and a highly proficient expert in manual robot control. A brain-inspired neural network model that
uses the output metric of a self-organizing pap with unsupervised winner-take-all learning was
run on the sensor output from both hands of each user. The neural network metric expresses the
difference between an input representation and its model representation at any given moment in
time and reliably captures the differences between novice and expert performance in terms of gripforce
variability.Functionally motivated spatiotemporal analysis of individual average grip forces,
computed for time windows of constant size in the output of a restricted amount of task-relevant
sensors in the dominant (preferred) hand, reveal finger-specific synergies reflecting robotic task skill.
The analyses lead the way towards grip-force monitoring in real time. This will permit tracking
task skill evolution in trainees, or identify individual proficiency levels in human robot-interaction,
which represents unprecedented challenges for perceptual and motor adaptation in environmental
contexts of high sensory uncertainty. Cross-disciplinary insights from systems neuroscience and
cognitive behavioral science, and the predictive modeling of operator skills using parsimonious
Artificial Intelligence (AI), will contribute towards improving the outcome of new types of surgery,
in particular the single-port approaches such as NOTES (Natural Orifice Transluminal Endoscopic
Surgery) and SILS (Single-Incision Laparoscopic Surgery).