Objective State-of-the-art techniques for surgical data analysis report promising results for automated skill assessment and action recognition. The contributions of many of these techniques, however, are limited to study-specific data and validation metrics, making assessment of progress across the field extremely challenging. Methods In this paper, we address two major problems for surgical data analysis: (1) lack of uniform shared datasets and benchmarks and (2) lack of consistent validation processes. We address the former by presenting the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), a public dataset we have created to support comparative research benchmarking. JIGSAWS contains synchronized video and kinematic data from multiple performances of robotic surgical tasks by operators of varying skill. We address the latter by presenting a well-documented evaluation methodology and reporting results for six techniques for automated segmentation and classification of time-series data on JIGSAWS. These techniques comprise four temporal approaches for joint segmentation and classification: Hidden Markov Model, Sparse HMM, Markov semi-Markov Conditional Random Field, and Skip-Chain CRF; and two feature-based ones that aim to classify fixed segments: Bag of spatiotemporal Features and Linear Dynamical Systems. Results Most methods recognize gesture activities with approximately 80% overall accuracy under both leave-one-super-trial-out and leave-one-user-out cross-validation settings. Conclusion Current methods show promising results on this shared dataset, but room for significant progress remains, particularly for consistent prediction of gesture activities across different surgeons. Significance The results reported in this paper provide the first systematic and uniform evaluation of surgical activity recognition techniques on the benchmark database.
Persistent spin texture (PST) is the property of some materials to maintain a uniform spin configuration in the momentum space. This property has been predicted to support an extraordinarily long spin lifetime of carriers promising for spintronics applications. Here, we predict that there exists a class of noncentrosymmetric bulk materials, where the PST is enforced by the nonsymmorphic space group symmetry of the crystal. Around certain high symmetry points in the Brillouin zone, the sublattice degrees of freedom impose a constraint on the effective spin–orbit field, which orientation remains independent of the momentum and thus maintains the PST. We illustrate this behavior using density-functional theory calculations for a handful of promising candidates accessible experimentally. Among them is the ferroelectric oxide BiInO3—a wide band gap semiconductor which sustains a PST around the conduction band minimum. Our results broaden the range of materials that can be employed in spintronics.
Abstract. We consider the problem of classifying surgical gestures and skill level in robotic surgical tasks. Prior work in this area models gestures as states of a hidden Markov model (HMM) whose observations are discrete, Gaussian or factor analyzed. While successful, these approaches are limited in expressive power due to the use of discrete or Gaussian observations. In this paper, we propose a new model called sparse HMMs whose observations are sparse linear combinations of elements from a dictionary of basic surgical motions. Given motion data from many surgeons with different skill levels, we propose an algorithm for learning a dictionary for each gesture together with an HMM grammar describing the transitions among different gestures. We then use these dictionaries and the grammar to represent and classify new motion data. Experiments on a database of surgical motions acquired with the da Vinci system show that our method performs on par with or better than state-of-the-art methods.This suggests that learning a grammar based on sparse motion dictionaries is important in gesture and skill classification.
Abstract. Automatic surgical gesture segmentation and recognition can provide useful feedback for surgical training in robotic surgery. Most prior work in this field relies on the robot's kinematic data. Although recent work [1,2] shows that the robot's video data can be equally effective for surgical gesture recognition, the segmentation of the video into gestures is assumed to be known. In this paper, we propose a framework for joint segmentation and recognition of surgical gestures from kinematic and video data. Unlike prior work that relies on either frame-level kinematic cues, or segment-level kinematic or video cues, our approach exploits both cues by using a combined Markov/semi-Markov conditional random field (MsM-CRF) model. Our experiments show that the proposed model improves over a Markov or semi-Markov CRF when using video data alone, gives results that are comparable to state-of-the-art methods on kinematic data alone, and improves over state-of-the-art methods when combining kinematic and video data.
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