This paper presents the experimental position and force testing of a 3-armed 6-DOF Parallel Robot, Robossis, that is specifically designed for the application of long-bone femur fracture surgery. Current surgical techniques require a significant amount of time and effort to restore the fractured femur fragments’ length, alignment and rotation. To address these issues, the Robossis system will facilitate the femur fracture surgical procedure and oppose the large traction forces/torques of the muscle groups surrounding the femur. As such, Robossis would subsequently improve patient outcomes by eliminating intraoperative injuries, reducing radiation exposure from X-rays during surgery and decreasing the likelihood of follow-up operations. Specifically, in this paper, we study the accuracy of the Robossis system while moving in the operational workspace under free and simulated traction loads of ([Formula: see text]–1100[Formula: see text]N). Experimental testing in this study demonstrates that Robossis can reach the most extreme points in the workspace, as defined by the theoretical workspace, while maintaining minimal deviation from those points with an average deviation of 0.324[Formula: see text]mm. Furthermore, the force testing experiment shows that Robossis can counteract loads that are clinically relevant to restoring the fractured femur fragments’ length, alignment and rotation. In addition, we study the accuracy of Robossis motion while coupled with the master controller Sigma 7. The results show that Robossis can follow the desired trajectory in real-time with an average error of less than 1[Formula: see text]mm. To conclude, these results further establish the ability of the Robossis system to facilitate the femur fracture surgical procedure and eliminate limitations faced with the current surgical techniques.
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Determining the Significant Kinematic Features for Characterizing Stress during Surgical Tasks Using Spatial Attention
It has been shown that intraoperative stress can have a negative effect on surgeon surgical skills during laparoscopic procedures. For novice surgeons, stressful conditions can lead to significantly higher velocity, acceleration, and jerk of the surgical instrument tips, resulting in faster but less smooth movements. However, it is still not clear which of these kinematic features (velocity, acceleration, or jerk) is the best marker for identifying the normal and stressed conditions. Therefore, in order to find the most significant kinematic feature that is affected by intraoperative stress, we implemented a spatial attention-based Long Short-Term Memory (LSTM) classifier. In a prior IRB approved experiment, we collected data from medical students performing an extended peg transfer task who were randomized into a control group and a group performing the task under external psychological stresses. In our prior work, we obtained “representative” normal or stressed movements from this dataset using kinematic data as the input. In this study, a spatial attention mechanism is used to describe the contribution of each kinematic feature to the classification of normal/stressed movements. We tested our classifier under Leave-One-User-Out (LOUO) cross-validation, and the classifier reached an overall accuracy of 77.11% for classifying “representative” normal and stressed movements using kinematic features as the input. More importantly, we also studied the spatial attention extracted from the proposed classifier. Velocity and acceleration on both sides had significantly higher attention for classifying a normal movement ([Formula: see text]); Velocity ([Formula: see text]) and jerk ([Formula: see text]) on nondominant hand had significant higher attention for classifying a stressed movement, and it is worthy noting that the attention of jerk on nondominant hand side had the largest increment when moving from describing normal movements to stressed movements ([Formula: see text]). In general, we found that the jerk on nondominant hand side can be used for characterizing the stressed movements for novice surgeons more effectively.
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- Award ID(s):
- 2109635
- NSF-PAR ID:
- 10380014
- Date Published:
- Journal Name:
- Journal of Medical Robotics Research
- Volume:
- 07
- Issue:
- 02n03
- ISSN:
- 2424-905X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1142/S2424905X22410069
