Background:
Although experiences in the operating room can help surgeons to learn simple bone-drilling techniques, outside training may be better suited for complex procedures. We adapted a rotary handpiece to evaluate the bone-drilling skills of orthopaedic resident physicians during the 2017 Southwest Orthopaedic Trauma Association (SWOTA) motor skills course.
Methods:
Twenty-five postgraduate year (PGY)-1 orthopaedic residents from 7 institutions were asked to perform a bicortical drilling task 3 times both before and after attending a motor skills course. Kinetic and kinematic data were collected using force, acceleration, and visual sensors.
Results:
Sixteen parameters were measured. The interdependence of these parameters (taken separately for precourse and postcourse performance) is presented. Evidence for motor skill acquisition across a short time scale is elucidated. Noteworthy correlations include overpenetration with force (0.65 mm), palmar-dorsal (P-D) toggle (0.65°), vibration in the P-D direction (0.53 m/s2), time (−0.41 sec), and RPM (revolutions per minute; −0.36); time with both RPM (0.38) and P-D toggle (−0.40°); and force with both RPM (−0.41) and P-D toggle (0.32°). Differences in performance before and after the motor skills course include reduction in overpenetration (28.8 to 18.2 mm), reduction in skiving (22% to 6%), and reduction in preparation time (27.3 to 9.65 sec). Additionally, there were several differences in performance by institution that were significant (overpenetration, toggle in the P-D and radial-ulnar [R-U] directions, and both drilling force and drilling time).
Conclusions:
Understanding how performance and outcome parameters are correlated provides powerful insight into how surgical procedures can be best performed. In particular, we hope that these findings will inform new training paradigms. Variations in resident training from 1 institution to another are evidenced in surgical performance. Similarly, the methods used here to quantify changes in performance across the 3-day SWOTA training course allow a unique vehicle for optimization of these types of training opportunities outside of the operating room.
This paper describes the Sarcos Treadport locomotion interface, a large tilting treadmill whose unique aspect is the application of forces to a user's torso. The application of torso forces is necessary to provide missing inertial forces during acceleration on a treadmill and to stop a user from walking through virtual walls. Torso forces can also substitute for treadmill tilt, as has been validated by psychological, biomechanical and energetic studies. A harness has been designed to transmit forces to the torso with minimum backlash while maintaining comfort. The experience of locomotion is integrated into a virtual environment comprised of a CAVElike visual display and head-tracked stereo sound synthesis. To complete the multi-sensory experience, atmospheric and olfactory displays are under development.
This paper describes a harness design for transmitting both horizontal and vertical forces to the torso of a user on a locomotion interfaces. For horizontal forces, an exoskeleton-like mechanism distributes forces between the shoulders and hips and accommodates to the complicated motions of the back and shoulders relative to hips. For vertical forces, a body weight support harness is integrated into the exoskeleton-like mechanism. A passive elastic element has been devised that ensures consistent strap tightening. Measurements are presented that shows the stiffness of the mechanical coupling of harness to subject for the purposes of faithful force application to the torso.
Patients with damage to the cerebellum make reaching movements that are uncoordinated or "ataxic." One prevailing hypothesis is that the cerebellum functions as an internal model for planning movements, and that damage to the cerebellum results in movements that do not properly account for arm dynamics. An exoskeleton robot was used to record multi-joint reaching movements. Subsequently, joint-torque trajectories were calculated and a gradient descent algorithm found optimal, patient-specific perturbations to actual limb dynamics predicted to reduce directional reaching errors by an average of 41%, elucidating a promising form of robotic intervention and adding support to the internal model hypothesis.
This paper explores the viability of neural-network-based classification of ground surface for vehicles. By classifying road surface in near real-time, improvements in vehicle performance (e.g. braking and cornering) may be possible. Classification performance for many combinations of feature encoding and neural network types are compared. The vehicle used here was an Audi “S3” with a magnetic suspension system on the Sport mode. An NI CompactRIO (or cDAQ) module was used to record from a lowing the cDAQ to communicate with the PCB 352C03 one-axis accelerometer. The accelerometer was firmly attached to the windshield of the car. This work focuses on the classification of four road surfaces (asphalt, dirt, concrete, and sand), though larger target sets were also considered. The most accurate method involved a MATLAB feature extraction package with a back-propagation neural network, yielding an overall accuracy of 97%. Lessons learned from this wide exploration of options may extend to other related classification problems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.