Spatiotemporal modeling of grip forces captures proficiency in manual robot control
This work addresses the need for real-time grip force monitoring to track skill evolution in trainees or identify proficiency in human-robot interaction, particularly in high-uncertainty environments like surgery, though it is incremental as it builds on previous research.
The paper tackled the problem of predicting individual grip force variability in manual robot control by analyzing sensor data from both hands of a novice and an expert, revealing that a brain-inspired neural network model reliably captured differences in performance, with finger-specific synergies reflecting skill levels.
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).