Electromyographic response is altered during robotic surgical training with augmented feedback

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

Robotic Surgery and Training: Electromyographic correlates of robotic laparoscopic training

Background: Robotic laparoscopic surgery has been shown to decrease task completion time, reduce errors, and decrease training time when compared to manual laparoscopic surgery. However, current literature has not addressed physiological effects, in particular muscle responses, to training with a robotic surgical system. We seek to determine the frequency response of electromyographic (EMG) signals of specific arm and hand muscles with training using the da Vinci Surgical System (dVSS). Methods: Seven right-handed medical students were trained in three tasks with da Vinci Surgical System over four weeks. These subjects, along with eight controls, were tested before and after training. Electromyographic (EMG) signals were collected from four arm and hand muscles during the testing sessions and the median EMG frequency and bandwidth were computed. Results: The median frequency and frequency bandwidth both increased after training for two of the three tasks. Conclusion: The results suggested that training reduces muscle fatigue as a result of faster and more deliberate movements. These changes occurred predominantly in muscles that were the dominant muscles for each task, whereas the more demanding task recruited more diverse motor units. An evaluation of the physiological demands of robotic laparoscopic surgery using electromyography can provide us with a meaningful quantitative way to examine performance and skill acquisition

Objective evaluation of expert and novice performance during robotic surgical training tasks

Background - Robotic laparoscopic surgery has revolutionized minimally invasive surgery for the treatment of abdominal pathologies. However, current training techniques rely on subjective evaluation. The authors sought to identify objective measures of robotic surgical performance by comparing novices and experts during three training tasks. Methods - Five novices (medical students) were trained in three tasks with the da Vinci Surgical System. Five experts trained in advanced laparoscopy also performed the three tasks. Time to task completion (TTC), total distance traveled (D), speed (S), curvature (Ƙ), and relative phase (Φ) were measured. Results - Before training, TTC, D, and Ƙ were significantly smaller for experts than for novices (p \u3c 0.05), whereas S was significantly larger for experts than for novices before training (p \u3c 0.05). Novices performed significantly better after training, as shown by smaller TTC, D, and Ƙ, and larger S. Novice performance after training approached expert performance. Conclusion - This study clearly demonstrated the ability of objective kinematic measures to distinguish between novice and expert performance and training effects in the performance of robotic surgical training tasks

Enhanced Robotic Surgical Training Using Augmented Visual Feedback

The goal of this study was to enhance robotic surgical training via real-time augmented visual feedback. Thirty novices (medical students) were divided into 5 feedback groups (speed, relative phase, grip force, video, and control) and trained during 1 session in 3 inanimate surgical tasks with the da Vinci Surgical System. Task completion time, distance traveled, speed, curvature, relative phase, and grip force were measured immediately before and after training and during a retention test 2 weeks after training. All performance measures except relative phase improved after training and were retained after 2 weeks. Feedback-specific effects showed that the speed group was faster than other groups after training, and the grip force group applied less grip force. This study showed that the real-time augmented feedback during training can enhance the surgical performance and can potentially be beneficial for both training and surgery

Electromyographic response is altered during robotic surgical training with augmented feedback

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

Reduced vertical displacement of the center of mass is not accompanied by reduced oxygen uptake during walking

Abstract The six determinants of gait proposed that the goal of gait is to minimize vertical displacement of the body’s center of mass (CoM) with the objective to optimize energy expenditure. On the contrary, recent investigations suggest that reduced vertical displacement leads to an increase in energy expenditure. However, these investigations had the included subjects deliberately changing their gait, which could bias the endpoint measures. The present study investigated the effect of reduced vertical displacement of the CoM on oxygen uptake and walking economy without imposing altered gait patterns. This was accomplished by having subjects walk on a curved treadmill and on a flat treadmill. Vertical displacement of the CoM (sacrum marker displacement), oxygen uptake, walking economy, stride characteristics and lower limb joint angles were measured. There were significant differences in stride characteristics and phase dependent differences in lower limb movement pattern between the two conditions which in size were comparable to the changes observed between different speeds. The vertical displacement of the CoM was significantly reduced on the curved treadmill. This was accompanied by an increase in oxygen uptake and walking economy. These results support recent assertions that the six determinants of gait do not serve to improve walking economy

\u3ci\u3eMedicine Meets Virtual Reality 14\u3c/i\u3e

Chapter, Real-Time Augmented Feedback Benefits Robotic Laparoscopic Training, co-authored by Nicholas Steriou, UNO faculty member. Machine intelligence will eclipse human intelligence within the next few decades - extrapolating from Moore’s Law - and our world will enjoy limitless computational power and ubiquitous data networks. Today’s iPod® devices portend an era when biology and information technology will fuse to create a human experience radically different from our own. Already, our healthcare system now appears on the verge of crisis; accelerating change is part of the problem. Each technological upgrade demands an investment of education and money, and a costly infrastructure more quickly becomes obsolete. Practitioners can be overloaded with complexity: therapeutic options, outcomes data, procedural coding, drug names etc. Furthermore, an aging global population with a growing sense of entitlement demands that each medical breakthrough be immediately available for its benefit: what appears in the morning paper is expected simultaneously in the doctor’s office. Meanwhile, a third-party payer system generates conflicting priorities for patient care and stockholder returns. The result is a healthcare system stressed by scientific promise, public expectation, economic and regulatory constraints and human limitations. Change is also proving beneficial, of course. Practitioners are empowered by better imaging methods, more precise robotic tools, greater realism in training simulators, and more powerful intelligence networks. The remarkable accomplishments of the IT industry and the Internet are trickling steadily into healthcare. The Medicine Meets Virtual Reality series can readily see the progress of the past fourteen years: more effective healthcare at a lower overall cost, driven by cheaper and better computers.https://digitalcommons.unomaha.edu/facultybooks/1236/thumbnail.jp

Lower limb joint angle variability and dimensionality are different in stairmill climbing and treadmill walking

The present study tested if the quadratic relationship which exists between stepping frequency and gait dynamics in walking can be generalized to stairmill climbing. To accomplish this, we investigated the joint angle dynamics and variability during continuous stairmill climbing at stepping frequencies both above and below the preferred stepping frequency (PSF). Nine subjects performed stairmill climbing at 80, 90, 100, 110 and 120% PSF and treadmill walking at preferred walking speed during which sagittal hip, knee and ankle angles were extracted. Joint angle dynamics were quantified by the largest Lyapunov exponent (LyE) and correlation dimension (CoD). Joint angle variability was estimated by the mean ensemble standard deviation (meanSD). MeanSD and CoD for all joints were significantly higher during stairmill climbing but there were no task differences in LyE. Changes in stepping frequency had only limited effect on joint angle variability and did not affect joint angle dynamics. Thus, we concluded that the quadratic relationship between stepping frequency and gait dynamics observed in walking is not present in stairmill climbing based on the investigated parameters

Compensatory mechanisms in anterior cruciate ligament deficiency

The literature cites numerous studies involving the analysis of movement patterns in anterior cruciate ligament deficient (ACLD) patients. Although several in vivo biomechanical studies have shown that ACLD patients develop protective mechanisms against degenerative diseases, it seems that these adaptations fail to protect the knee from future pathology. Some authors state that ACLD patients adapt to the injury by avoiding quadriceps contraction during gait when the knee is near full extension. However, others have found increased hamstrings and decreased gastrocnemius activity, which normally contribute to the stability of the knee. It seems that further in vivo biomechanical investigation is required to understand the mechanisms of pathological knee joint motions and develop rehabilitation programs, which would delay the progress of developing long-term degenerative diseases