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  1. Virdi, Amarjit Singh (Ed.)
    Adult acquired flatfoot deformity becomes permanent with stage III posterior tibialis tendon dysfunction and results in foot pain and difficulty walking and balancing. To prevent progression to stage III posterior tibialis tendon dysfunction when conservative treatment fails, a flexor digitorum longus to posterior tibialis tendon transfer is often conducted. However, since the flexor digitorum longus only has one-third the force-capability of the posterior tibialis, an osteotomy is typically also required. We propose the use of a novel implantable mechanism to replace the direct attachment of the tendon transfer with a sliding pulley to amplify the force transferred from the donor flexor digitorum longus to the foot arch. In this work, we created four OpenSim models of an arched foot, a flatfoot, a flatfoot with traditional tendon transfer, and a flatfoot with implant-modified tendon transfer. Paired with these models, we developed a forward dynamic simulation of the stance phase of gait that reproduces the medial/lateral distribution of vertical ground reaction forces. The simulation couples the use of a fixed tibia, moving ground plane methodology with simultaneous activation of nine extrinsic lower limb muscles. The arched foot and flatfoot models produced vertical ground reaction forces with the characteristic double-peak profile of gait,more »and the medial/lateral distribution of these forces compared well with the literature. The flatfoot model with implant-modified tendon transfer produced a 94.2% restoration of the medial/lateral distribution of vertical ground reaction forces generated by our arched foot model, which also represents a 2.1X improvement upon our tendon transfer model. This result demonstrates the feasibility of a pulley-like implant to improve functional outcomes for surgical treatment of adult acquired flatfoot deformity with ideal biomechanics in simulation. The real-world efficacy and feasibility of such a device will require further exploration of factors such as surgical variability, soft tissue interactions and healing response.« less
    Free, publicly-accessible full text available September 27, 2023
  2. Dynamic loading is a shared feature of tendon tissue homeostasis and pathology. Tendon cells have the inherent ability to sense mechanical loads that initiate molecular-level mechanotransduction pathways. While mature tendons require physiological mechanical loading in order to maintain and fine tune their extracellular matrix architecture, pathological loading initiates an inflammatory-mediated tissue repair pathway that may ultimately result in extracellular matrix dysregulation and tendon degeneration. The exact loading and inflammatory mechanisms involved in tendon healing and pathology is unclear although a precise understanding is imperative to improving therapeutic outcomes of tendon pathologies. Thus, various model systems have been designed to help elucidate the underlying mechanisms of tendon mechanobiology via mimicry of the in vivo tendon architecture and biomechanics. Recent development of model systems has focused on identifying mechanoresponses to various mechanical loading platforms. Less effort has been placed on identifying inflammatory pathways involved in tendon pathology etiology, though inflammation has been implicated in the onset of such chronic injuries. The focus of this work is to highlight the latest discoveries in tendon mechanobiology platforms and specifically identify the gaps for future work. An interdisciplinary approach is necessary to reveal the complex molecular interplay that leads to tendon pathologies and will ultimatelymore »identify potential regenerative therapeutic targets.« less
    Free, publicly-accessible full text available July 15, 2023
  3. Free, publicly-accessible full text available January 1, 2023
  4. We present a method for classifying the quality of near-contact grasps using spatial metrics that are recoverable from sensor data. Current methods often rely on calculating precise contact points, which are difficult to calculate in real life, or on tactile sensors or image data, which may be unavailable for some applications. Our method, in contrast, uses a mix of spatial metrics that do not depend on the fingers being in contact with the object, such as the object's approximate size and location. The grasp quality can be calculated {\em before} the fingers actually contact the object, enabling near-grasp quality prediction. Using a random forest classifier, the resulting system is able to predict grasp quality with 96\% accuracy using spatial metrics based on the locations of the robot palm, fingers and object. Furthermore, it can maintain an accuracy of 90\% when exposed to 10\% noise across all its inputs.
  5. Grasping a simple object from the side is easy-unless the object is almost as big as the hand or space constraints require positioning the robot hand awkwardly with respect to the object. We show that humans-when faced with this challenge-adopt coordinated finger movements which enable them to successfully grasp objects even from these awkward poses. We also show that it is relatively straight forward to implement these strategies autonomously. Our human-studies approach asks participants to perform grasping task by either "puppetteering" a robotic manipulator that is identical (geometrically and kinematically) to a popular underactuated robotic manipulator (the Barrett hand), or using sliders to control the original Barrett hand. Unlike previous studies, this enables us to directly capture and compare human manipulation strategies with robotic ones. Our observation is that, while humans employ underactuation, how they use it is fundamentally different (and more effective) than that found in existing hardware.
  6. Grasping a simple object from the side is easy --- unless the object is almost as big as the hand or space constraints require positioning the robot hand awkwardly with respect to the object. We show that humans --- when faced with this challenge --- adopt coordinated finger movements which enable them to successfully grasp objects even from these awkward poses. We also show that it is relatively straight forward to implement these strategies autonomously. Our human-studies approach asks participants to perform grasping task by either ``puppetteering'' a robotic manipulator that is identical~(geometrically and kinematically) to a popular underactuated robotic manipulator~(the Barrett hand), or using sliders to control the original Barrett hand. Unlike previous studies, this enables us to directly capture and compare human manipulation strategies with robotic ones. Our observation is that, while humans employ underactuation, how they use it is fundamentally different (and more effective) than that found in existing hardware.
  7. In this paper we define two feature representations for grasping. These representations capture hand-object geometric relationships at the near-contact stage - before the fingers close around the object. Their benefits are: 1) They are stable under noise in both joint and pose variation. 2) They are largely hand and object agnostic, enabling direct comparison across different hand morphologies. 3) Their format makes them suitable for direct application of machine learning techniques developed for images. We validate the representations by: 1) Demonstrating that they can accurately predict the distribution of ε-metric values generated by kinematic noise. I.e., they capture much of the information inherent in contact points and force vectors without the corresponding instabilities. 2) Training a binary grasp success classifier on a real-world data set consisting of 588 grasps.
  8. This paper presents an online data collection method that captures human intuition about what grasp types are preferred for different fundamental object shapes and sizes. Survey questions are based on an adopted taxonomy that combines grasp pre-shape, approach, wrist orientation, object shape, orientation and size which covers a large swathe of common grasps. For example, the survey identifies at what object height or width dimension (normalized by robot hand size) the human prefers to use a two finger precision grasp versus a three-finger power grasp. This information is represented as a confidence-interval based polytope in the object shape space. The result is a database that can be used to quickly find potential pre-grasps that are likely to work, given an estimate of the object shape and size.