Bioengineering, 2023; 10(1):59
This paper builds on our previous work by exploiting Artificial Intelligence
to predict individual grip force variability in manual robot control. Grip
forces were recorded from various loci in the dominant and non dominant hands
of individuals by means of 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 Map 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 t and reliably captures the differences between novice
and expert performance in terms of grip force 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 to permit tracking task skill evolution in
trainees, or identify individual proficiency levels in human robot interaction
in environmental contexts of high sensory uncertainty. Parsimonious Artificial
Intelligence (AI) assistance will contribute to the outcome of new types of
surgery, in particular single-port approaches such as NOTES (Natural Orifice
Transluminal Endoscopic Surgery) and SILS (Single Incision Laparoscopic
Surgery).
Rongrong Liu, John M. Wandeto, Florent Nageotte, Philippe Zanne, Michel de Mathelin, Birgitta Dresp-Langley
2023-03-03
