Cai presented her poster "Muscle Activity Correlation with Surgeons' Self-reported Workload and Performance in Robotic Training" at the 2018 SURF Research Symposium on Aug. 2, 2018.

Cai authored the poster with HEAL members Jackie Cha, Hamed Asadi and Dr. Denny Yu of HEAL, and Jay Sulek and Chandru Sundaram of the Department of Urology at the Indiana University School of Medicine. This work was supported in part by the Walther Oncology Physical Sciences and Engineering Research Embedding Program.

The 2018 SURF program culminated with a research symposium in the Neil Armstrong Hall of Engineering. Of the 137 student participants, 50 gave oral presentations and 87 presented posters on engineering, science, and agriculture topics.

ABSTRACT

Studies have shown that muscle activity levels reflect work demands of operators performing physically and mentally tasks. Identifying work demands during the robotic surgery training is essential to ensure usability of teleoperation equipment and prevent surgeon musculoskeletal injuries and fatigue. The purpose of this project is to use physiological muscle activity sensors (electromyography (EMG)) to measure surgeons' work demands during robotic training and to quantify the relationship of these metrics. Eight surface EMG sensors were used to collect upper body muscle activity. Signals from eight participants (all right-hand dominant) during multiple training sessions were collected while performing simulated robotic assisted tasks on the da Vinci Skills Simulator. Subjective workload measurements (i.e. NASA-TLX) and performance scores were also collected. The results showed muscle activity for neck, shoulder, and left forearm are significantly correlated with self-perceived workload and negatively correlated with performance. This may be due to increased muscle fatigue, which may cause higher workload and lower performance score. These results provide insight to surgeons' workload and to help optimize their performance.