A modular extreme learning machine with linguistic interpreter and accelerated chaotic distributor for evaluating the safety of robot maneuvers in laparoscopic surgery

• Proposing a systematic sequential intelligent framework for evaluating the safety of robot maneuvers over laparoscopic surgery.
• Demonstrating the high potentials of extreme learning machine to be used as the component of a modular soft system.
• Elaborating on the hybridization power of extreme learning machine with fuzzy inference system for uncertain real-life applications.
• Extending the realms of applications of accelerated chaotic particle swarm optimization for clustering the database of laparoscopic surgery.

In this investigation, a systematic sequential intelligent system is proposed to provide the surgeon with an estimation of the state of the tool-tissue interaction force in laparoscopic surgery. To train the proposed intelligent system, a 3D model of an in vivo porcine liver was built for different probing tasks. To capture the required knowledge, three different geometric features, i.e. Y displacement of the nodes on the upper surface and slopes on the closest node to the deforming area of the upper edge in both X–Y and Z–Y planes, were extracted experimentally. The numerical simulations are conducted in three independent successive stages. At the first step, a well-known partition-based clustering technique called accelerated chaotic particle swarm optimization (ACPSO) is used to cluster the information of database into a number of partitions. Thereafter, a modular extreme learning machine (M-ELM) is used to model the characteristics of each cluster. Finally, the output of M-ELM is fed to a Mamdani fuzzy inference system (MFIS) to interpret the safety of robot maneuvers in laparoscopic surgery. The proposed intelligent framework is used for real-time applications so that the surgeon can adjust the movements of the robot to avoid operational hazards. Based on a rigor comparative study, it is indicated that not only the proposed intelligent technique can effectively handle the considered problem but also is a reliable alternative to physical sensors and measurement tools.