Harold Jay Bolingot1, Kevin Kiel Apeles, MD2, Bernalyn Eris Cansana, MD2, Karen Faye Serrano2, Nicole Robyn Bangayan2, Deogracias Alberto Reyes, MD, MBA, MMAS2, Nathaniel Joseph Libatique, PhD3, Gregory Tangonan, PhD3, Tomohiro Shibata, PhD1. 1Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2Center for Advanced Skills, Simulation, and Training Innovation, The Medical City, 3Ateneo Innovation Center, Ateneo de Manila University
While laparoscopic surgery cases have continued to increase, many in the profession report experiences of fatigue and injury during such procedures of long durations, which indicates the need for ergonomically designed tools as well as effective training systems that train the muscle groups required for the procedure. Advances in non-invasive wearable sensing and biofeedback signal processing have paved way for robust methods in executing computational and data-driven analysis of anatomical states of the human body, such as in physical therapy and rehabilitation, as they have allowed muscle fatigue to be identified by recording and analyzing the EMG in certain muscle groups. These advancements have also vastly improved human-computer interfaces in gaming environments as well as in recent prototypes of prosthetics.
We have previously devised a web-based telemetry and visualization system that explores key muscle group actuation via a now-commonly available non-invasive, surface-based EMG (sEMG) sensing interface, the Myo™ Gesture Control Armband. This sEMG device enables the recording of transient signals about the muscle activity, and can be used to observe the development of neuromuscular fatigue in the forearms. We explore the feasibility of this new paradigm in immediate and data-driven analysis of psychomotor performance and acquisition of fundamental skills during the course of laparoscopic surgical training, utilizing the Myo™ Gesture Control armband device on the forearms of a trainee, while performing the different training tasks in various training environments.
As part of our ongoing research effort to analyze these sEMG and motion variations, we present our growing database of biofeedback that reflects the changes of the psychomotor abilities of trainees as they gain the fundamental skills in laparoscopic surgery, within the FLS framework. Our dataset includes FLS performances across skill levels, from novice, intermediate, and expert participants as described by their experience of laparoscopic surgery in the operating room. We present early results from the signal processing techniques used in analyzing trainee performance under a variety of fatigue levels, as we measure the development and experience of neuromuscular fatigue and its effect in the use of various laparoscopic instruments and training environments.
We also present the wireless telemetry and data recording system used in the study, that allowed us to record sensor data from participants without obstructions and transmit these data seamlessly to a local, private, and secure on-premise data kiosk. In addition to the biofeedback signals, we also record the videos of the surgical activities performed during training, as well as the posture and hand movements of the trainee from cameras filming the trainee during training sessions. We present a prototype of a web-based performance analytics dashboard that can be that displays all these dataset and analysis on both PCs and mobile devices, such as tablets, as part of our vision to integrate this sensor system and analytics in the training procedure and in training facilities.
Presented at the SAGES 2017 Annual Meeting in Houston, TX.
Abstract ID: 91208
Program Number: ETP870
Presentation Session: Emerging Technology iPoster Session (Non CME)
Presentation Type: Poster