Biosymbiotic haptic feedback - Sustained long term human machine interfaces

Haptic technology permeates diverse fields and is receiving renewed attention for VR and AR applications. Advances in flexible electronics, facilitate the integration of haptic technologies into soft wearable systems, however, because of small footprint requirements face challenges of operational time requiring either large batteries, wired connections or frequent recharge, restricting the utility of haptic devices to short-duration tasks or low duty cycles, prohibiting continuously assisting applications. Currently many chronic applications are not investigated because of this technological gap. Here, we address wireless power and operation challenges with a biosymbiotic approach enabling continuous operation without user intervention, facilitated by wireless power transfer, eliminating the need for large batteries, and offering long-term haptic feedback without adhesive attachment to the body. These capabilities enable haptic feedback for robotic surgery training and posture correction over weeks of use with neural net computation. The demonstrations showcase that this device class expands use beyond conventional brick and strap or epidermally attached devices enabling new fields of use for imperceptible therapeutic and assistive haptic technologies supporting care and disease management.

Validation of a Simulation Model for Robotic Myomectomy

STUDY OBJECTIVE

Several simulation models have been evaluated for gynecologic procedures such as hysterectomy, but there are limited published data for myomectomy. This study aimed to assess the validity of a low-cost robotic myomectomy model for surgical simulation training.

DESIGN

Prospective cohort simulation study.

SETTING

Surgical simulation laboratory.

PARTICIPANTS

Twelve obstetrics and gynecology residents and 4 fellowship-trained minimally invasive gynecologic surgeons were recruited for a 3:1 novice-to-expert ratio.

INTERVENTIONS

A robotic myomectomy simulation model was constructed using <$5 worth of materials: a foam cylinder, felt, a stress ball, bandage wrap, and multipurpose sealing wrap. Participants performed a simulation task involving 2 steps: fibroid enucleation and hysterotomy repair. Video-recorded performances were timed and scored by 2 blinded reviewers using the validated Global Evaluative Assessment of Robotic Skills (GEARS) scale (5-25 points) and a modified GEARS scale (5-40 points), which adds 3 novel domains specific to robotic myomectomy. Performance was also scored using predefined task errors. Participants completed a post-task questionnaire assessing the model's realism and utility.

MEASUREMENTS AND MAIN RESULTS

Median task completion time was shorter for experts than novices (9.7 vs 24.6 min, p = .001). Experts scored higher than novices on both the GEARS scale (median 23 vs 12, p = .004) and modified GEARS scale (36 vs 20, p = .004). Experts made fewer task errors than novices (median 15.5 vs 37.5, p = .034). For interrater reliability of scoring, the intraclass correlation coefficient was calculated to be 0.91 for the GEARS assessment, 0.93 for the modified GEARS assessment, and 0.60 for task errors. Using the contrasting groups method, the passing mark for the simulation task was set to a minimum modified GEARS score of 28 and a maximum of 28 errors. Most participants agreed that the model was realistic (62.5%) and useful for training (93.8%).

CONCLUSION

We have demonstrated evidence supporting the validity of a low-cost robotic myomectomy model. This simulation model and the performance assessments developed in this study provide further educational tools for robotic myomectomy training.

Identifying Kinematic Markers Associated with Intraoperative Stress during Surgical Training Tasks

Increased levels of stress can impair surgeon performance and patient safety during surgery. The aim of this study is to investigate the effect of short term stressors on laparoscopic performance through analysis of kinematic data. Thirty subjects were randomly assigned into two groups in this IRB-approved study. The control group was required to finish an extended-duration peg transfer task (6 minutes) using the FLS trainer while listening to normal simulated vital signs and while being observed by a silent moderator. The stressed group finished the same task but listened to a period of progressively deteriorating simulated patient vitals, as well as critical verbal feedback from the moderator, which culminated in 30 seconds of cardiac arrest and expiration of the simulated patient. For all subjects, video and position data using electromagnetic trackers mounted on the handles of the laparoscopic instruments were recorded. A statistical analysis comparing time-series velocity, acceleration, and jerk data, as well as path length and economy of volume was conducted. Clinical stressors lead to significantly higher velocity, acceleration, jerk, and path length as well as lower economy of volume. An objective evaluation score using a modified OSATS technique was also significantly worse for the stressed group than the control group. This study shows the potential feasibility and advantages of using the time-series kinematic data to identify the stressful conditions during laparoscopic surgery in near-real-time. This data could be useful in the design of future robot-assisted algorithms to reduce the unwanted effects of stress on surgical performance.

Rethinking Autonomous Surgery: Focusing on Enhancement over Autonomy

CONTEXT

As robot-assisted surgery is increasingly used in surgical care, the engineering research effort towards surgical automation has also increased significantly. Automation promises to enhance surgical outcomes, offload mundane or repetitive tasks, and improve workflow. However, we must ask an important question: should autonomous surgery be our long-term goal?

OBJECTIVE

To provide an overview of the engineering requirements for automating control systems, summarize technical challenges in automated robotic surgery, and review sensing and modeling techniques to capture real-time human behaviors for integration into the robotic control loop for enhanced shared or collaborative control.

EVIDENCE ACQUISITION

We performed a nonsystematic search of the English language literature up to March 25, 2021. We included original studies related to automation in robot-assisted laparoscopic surgery and human-centered sensing and modeling.

EVIDENCE SYNTHESIS

We identified four comprehensive review papers that present techniques for automating portions of surgical tasks. Sixteen studies relate to human-centered sensing technologies and 23 to computer vision and/or advanced artificial intelligence or machine learning methods for skill assessment. Twenty-two studies evaluate or review the role of haptic or adaptive guidance during some learning task, with only a few applied to robotic surgery. Finally, only three studies discuss the role of some form of training in patient outcomes and none evaluated the effects of full or semi-autonomy on patient outcomes.

CONCLUSIONS

Rather than focusing on autonomy, which eliminates the surgeon from the loop, research centered on more fully understanding the surgeon's behaviors, goals, and limitations could facilitate a superior class of collaborative surgical robots that could be more effective and intelligent than automation alone.

PATIENT SUMMARY

We reviewed the literature for studies on automation in surgical robotics and on modeling of human behavior in human-machine interaction. The main application is to enhance the ability of surgical robotic systems to collaborate more effectively and intelligently with human surgeon operators.