Electromyographic response is altered during robotic surgical training with augmented feedback

• Published in: Journal of biomechanics Vol. 42; no. 1; pp. 71 - 76
• Main Authors: Judkins, Timothy N., Oleynikov, Dmitry, Stergiou, Nick
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 05.01.2009; Elsevier Limited
• Subjects: Adult; Augmented feedback; Biofeedback, Psychology; Electromyography; Feedback; Humans; Medical research; Muscular system; Physical Medicine and Rehabilitation; Physicians; Robotic surgery; Robotics; Robotics - education; Robotics - methods; Spectrum allocation; Studies; Surgery; Surgical Procedures, Operative - education; Surgical Procedures, Operative - methods; Training; Robotic surgery; Training; Augmented feedback; Electromyography
• ISSN: 0021-9290; 1873-2380
• DOI: 10.1016/j.jbiomech.2008.09.039

There is a growing prevalence of robotic systems for surgical laparoscopy. We previously developed quantitative measures to assess robotic surgical proficiency, and used augmented feedback to enhance training to reduce applied grip force and increase speed. However, there is also a need to understand the physiological demands of the surgeon during robotic surgery, and if training can reduce these demands. Therefore, the goal of this study was to use clinical biomechanical techniques via electromyography (EMG) to investigate the effects of real-time augmented visual feedback during short-term training on muscular activation and fatigue. Twenty novices were trained in three inanimate surgical tasks with the da Vinci Surgical System. Subjects were divided into five feedback groups (speed, relative phase, grip force, video, and control). Time- and frequency-domain EMG measures were obtained before and after training. Surgical training decreased muscle work as found from mean EMG and EMG envelopes. Grip force feedback further reduced average and total muscle work, while speed feedback increased average muscle work and decreased total muscle work. Training also increased the median frequency response as a result of increased speed and/or reduced fatigue during each task. More diverse motor units were recruited as revealed by increases in the frequency bandwidth post-training. We demonstrated that clinical biomechanics using EMG analysis can help to better understand the effects of training for robotic surgery. Real-time augmented feedback during training can further reduce physiological demands. Future studies will investigate other means of feedback such as biofeedback of EMG during robotic surgery training.