Robotic tool change mechanism

An assembly of three major components is disclosed which included a wrist interface plate which is secured to the wrist joint of a robotic arm, a tool interface plate which is secured to each tool intended for use by the robotic arm, and a tool holster for each tool attached to the interface plate. The wrist interface plate and a selected tool interface plate are mutually connectable together through an opening or recess in the upper face of the interface plate by means of a notched tongue protruding from the front face of the wrist interface plate which engages a pair of spring-biased rotatable notched wheels located within the body of the tool interface plate. The tool holster captures and locks onto the tool interface plate by means of a pair of actuation claws including a locking tab and an unlocking wedge which operate respective actuation bosses on each of the notched wheels in response to a forward and backward motion of the tool interface plate as a result of motion of the robotic arm to either park the tool or use the tool

Design and Validation of a MR-compatible Pneumatic Manipulandum

The combination of functional MR imaging and novel robotic tools may provide unique opportunities to probe the neural systems underlying motor control and learning. Here, we describe the design and validation of a MR-compatible, 1 degree-of-freedom pneumatic manipulandum along with experiments demonstrating its safety and efficacy. We first validated the robot\u27s ability to apply computer-controlled loads about the wrist, demonstrating that it possesses sufficient bandwidth to simulate torsional spring-like loads during point-to-point flexion movements. Next, we verified the MR-compatibility of the device by imaging a head phantom during robot operation. We observed no systematic differences in two measures of MRI signal quality (signal/noise and field homogeneity) when the robot was introduced into the scanner environment. Likewise, measurements of joint angle and actuator pressure were not adversely affected by scanning. Finally, we verified device efficacy by scanning 20 healthy human subjects performing rapid wrist flexions against a wide range of spring-like loads. We observed a linear relationship between joint torque at peak movement extent and perturbation magnitude, thus demonstrating the robot\u27s ability to simulate spring-like loads in situ. fMRI revealed task-related activation in regions known to contribute to the control of movement including the left primary sensorimotor cortex and right cerebellum

Moregrasp: Restoration of Upper Limb Function in Individuals with High Spinal Cord Injury by Multimodal Neuroprostheses for Interaction in Daily Activities

The aim of the MoreGrasp project is to develop a noninvasive, multimodal user interface including a brain-computer interface (BCI) for intuitive control of a grasp neuroprosthesis to support individuals with high spinal cord injury (SCI) in everyday activities. We describe the current state of the project, including the EEG system, preliminary results of natural movements decoding in people with SCI, the new electrode concept for the grasp neuroprosthesis, the shared control architecture behind the system and the implementation of a user-centered design

Automatic generation of statistical pose and shape models for articulated joints

Statistical analysis of motion patterns of body joints is potentially useful for detecting and quantifying pathologies. However, building a statistical motion model across different subjects remains a challenging task, especially for a complex joint like the wrist. We present a novel framework for simultaneous registration and segmentation of multiple 3-D (CT or MR) volumes of different subjects at various articulated positions. The framework starts with a pose model generated from 3-D volumes captured at different articulated positions of a single subject (template). This initial pose model is used to register the template volume to image volumes from new subjects. During this process, the Grow-Cut algorithm is used in an iterative refinement of the segmentation of the bone along with the pose parameters. As each new subject is registered and segmented, the pose model is updated, improving the accuracy of successive registrations. We applied the algorithm to CT images of the wrist from 25 subjects, each at five different wrist positions and demonstrated that it performed robustly and accurately. More importantly, the resulting segmentations allowed a statistical pose model of the carpal bones to be generated automatically without interaction. The evaluation results show that our proposed framework achieved accurate registration with an average mean target registration error of mm. The automatic segmentation results also show high consistency with the ground truth obtained semi-automatically. Furthermore, we demonstrated the capability of the resulting statistical pose and shape models by using them to generate a measurement tool for scaphoid-lunate dissociation diagnosis, which achieved 90% sensitivity and specificity

A multi-projector CAVE system with commodity hardware and gesture-based interaction

Spatially-immersive systems such as CAVEs provide users with surrounding worlds by projecting 3D models on multiple screens around the viewer. Compared to alternative immersive systems such as HMDs, CAVE systems are a powerful tool for collaborative inspection of virtual environments due to better use of peripheral vision, less sensitivity to tracking errors, and higher communication possibilities among users. Unfortunately, traditional CAVE setups require sophisticated equipment including stereo-ready projectors and tracking systems with high acquisition and maintenance costs. In this paper we present the design and construction of a passive-stereo, four-wall CAVE system based on commodity hardware. Our system works with any mix of a wide range of projector models that can be replaced independently at any time, and achieves high resolution and brightness at a minimum cost. The key ingredients of our CAVE are a self-calibration approach that guarantees continuity across the screen, as well as a gesture-based interaction approach based on a clever combination of skeletal data from multiple Kinect sensors.Preprin

Remembering Forward: Neural Correlates of Memory and Prediction in Human Motor Adaptation

We used functional MR imaging (FMRI), a robotic manipulandum and systems identification techniques to examine neural correlates of predictive compensation for spring-like loads during goal-directed wrist movements in neurologically-intact humans. Although load changed unpredictably from one trial to the next, subjects nevertheless used sensorimotor memories from recent movements to predict and compensate upcoming loads. Prediction enabled subjects to adapt performance so that the task was accomplished with minimum effort. Population analyses of functional images revealed a distributed, bilateral network of cortical and subcortical activity supporting predictive load compensation during visual target capture. Cortical regions – including prefrontal, parietal and hippocampal cortices – exhibited trial-by-trial fluctuations in BOLD signal consistent with the storage and recall of sensorimotor memories or “states” important for spatial working memory. Bilateral activations in associative regions of the striatum demonstrated temporal correlation with the magnitude of kinematic performance error (a signal that could drive reward-optimizing reinforcement learning and the prospective scaling of previously learned motor programs). BOLD signal correlations with load prediction were observed in the cerebellar cortex and red nuclei (consistent with the idea that these structures generate adaptive fusimotor signals facilitating cancelation of expected proprioceptive feedback, as required for conditional feedback adjustments to ongoing motor commands and feedback error learning). Analysis of single subject images revealed that predictive activity was at least as likely to be observed in more than one of these neural systems as in just one. We conclude therefore that motor adaptation is mediated by predictive compensations supported by multiple, distributed, cortical and subcortical structures

Effects of Surgical Repair or Reconstruction on Radiocarpal Mechanics from Wrists with Scapholunate Ligament Injury

Osteoarthritis as a result of injury/trauma is a significant problem, and there is still a need to develop tools for evaluating joint injuries and the effectiveness of surgical treatments. For the wrist in particular, injury to the scapholunate ligament from impact loading, can lead to scapholunate joint instability. Without treatment, this can lead to progressive development of wrist osteoarthritis. Joint contact pressures are important mechanical factors in the etiology of osteoarthritis, and these can be determined non-invasively through computer modeling. Hence, the goal of this work was to investigate the effects of scapholunate ligament injury and surgical repair on radioscapholunate contact mechanics, through surface contact modeling (SCM) and finite element modeling (FEM). The modeling process required geometries, boundary conditions and a contact relationship. Magnetic resonance imaging (MRI) was used to acquire images of the normal, injured and post-operative wrists, while relaxed and during active grasp loading. Surface and volumetric models were generated from the relaxed images, while kinematic boundary conditions were determined from image registration between the relaxed and loaded images. To improve the automatic image registration process, the effects of initial manual registration on the outcome of final registration accuracy, were investigated. Results showed that kinematic accuracy and subsequent contact mechanics were improved by performing a manual registration to align the image volumes as close as possible, before auto-registration. Looking at the effects of scapholunate ligament injury, results showed that contact forces, contact areas, peak and mean contact pressures significantly increased in the radioscaphoid joint. The locations of contact also shifted with injury. This novel data showed that contact mechanics was altered for the worse after injury. Novel contact mechanics data on the effects of surgical repair were also obtained. Results showed that radiolunate peak and mean contact pressures decreased significantly compared to injured, which indicated the possibility of restoring normal mechanics post surgery. SCM results were compared to FEM results to demonstrate the feasibility of the surface contact modeling approach for clinical applications. Contact parameters compared well between the two techniques. This work demonstrated the potential of MRI-based SCM as a tool to evaluate joint injuries and subsequent treatments, for clinical applications