Sensors for expert grip force profiling: towards benchmarking manual control of a robotic device for surgical tool movements
This work addresses the need for benchmarking manual control in robotic surgical systems to improve surgeon training, though it is incremental as it builds on existing sensor technology for a specific domain.
The study tackled the problem of objectively assessing expertise in robotic surgery by analyzing grip force profiles of expert and novice surgeons using sensor-equipped gloves during a simulated surgical task, revealing specific differences in force application on anatomically relevant hand parts.
STRAS (Single access Transluminal Robotic Assistant for Surgeons) is a new robotic system for application to intraluminal surgical procedures. Preclinical testing of STRAS has recently permitted to demonstrate major advantages of the system in comparison with classic procedures. Benchmark methods permitting to establish objective criteria for expertise need to be worked out now to effectively train surgeons on this new system in the near future. STRAS consists of three cable driven subsystems, one endoscope serving as guide, and two flexible instruments. The flexible instruments have three degrees of freedom and can be teleoperated by a single user via two specially designed master interfaces. In this study here, small force sensors sewn into a wearable glove to ergonomically fit the master handles of the robotic system were employed for monitoring the forces applied by an expert and a trainee who was a complete novice during all the steps of surgical task execution in a simulator task, a four step pick and drop. Analysis of gripforce profiles is performed sensor by sensor to bring to the fore specific differences in handgrip force profiles in specific sensor locations on anatomically relevant parts of the fingers and hand controlling the master slave system.