Shahin Sefati

Feedback Control as a Framework for Understanding Tradeoffs in Biology

Control theory arose from a need to control synthetic systems. From regulating steam engines to tuning radios to devices capable of autonomous movement, it provided a formal mathematical basis for understanding the role of feedback in the stability (or change) of dynamical systems. It provides a framework for understanding any system with regulation via feedback, including biological ones such as regulatory gene networks, cellular metabolic systems, sensorimotor dynamics of moving animals, and even ecological or evolutionary dynamics of organisms and populations. Here, we focus on four case studies of the sensorimotor dynamics of animals, each of which involves the application of principles from control theory to probe stability and feedback in an organisms response to perturbations. We use examples from aquatic (two behaviors performed by electric fish), terrestrial (following of walls by cockroaches), and aerial environments (flight control by moths) to highlight how one can use control theory to understand the way feedback mechanisms interact with the physical dynamics of animals to determine their stability and response to sensory inputs and perturbations. Each case study is cast as a control problem with sensory input, neural processing, and motor dynamics, the output of which feeds back to the sensory inputs. Collectively, the interaction of these systems in a closed loop determines the behavior of the entire system.

A Dataset and Benchmarks for Segmentation and Recognition of Gestures in Robotic Surgery

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: First, lack of uniform-shared datasets and benchmarks, and second, lack of consistent validation processes. We address the former by presenting the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), a public dataset that 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 hidden Markov model (HMM), Markov semi-Markov conditional random field, and skip-chain conditional random field; 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.

Linear systems with sparse inputs: Observability and input recovery

In this work, we introduce a new class of linear time-invariant systems for which, at each time instant, the input is sparse with respect to an overcomplete dictionary of inputs. Such systems may be appropriate for modeling a system which exhibits multiple discrete behaviors orchestrated by the sparse input. Although the input is assumed to be unknown, we show that the additional structure imposed on the input allows us to recover both the initial state and the sparse, but unknown, input from output measurements alone. For this purpose, we derive sufficient observability and sparse recovery conditions that integrate classical observability conditions for linear systems with incoherence conditions for sparse recovery. We also propose a convex optimization algorithm for jointly estimating the initial condition and recovering the sparse input.

Snake robot uncovers secrets to sidewinders’ maneuverability

A hallmark of animal life is the ability to move through the environment to catch prey, avoid predators, or find mates. Animals achieve this using a staggering diversity of locomotor strategies despite having similar body shape and being subjected to similar physics—e.g., gazelles pronk and cheetahs gallop. These differences in strategy may allow animals to fill different ecological niches by affording more (or less) stability, maneuverability, speed, efficiency, and stealth. Many animals rely on specialized appendages—limbs, fins, and wings—that reciprocate to produce forward motion. However, some organisms move using a completely different strategy that involves the generation of undulatory traveling waves that propagate along the body or a specialized elongated fin. Most studies of such undulatory locomotion have focused on the role of a single, in-plane wave that travels from head-to-tail to produce forward thrust, as seen for example in aquatic animals such as eels, lampreys, and leeches (1, 2). In PNAS, Astley et al. (3) present behavioral data that suggest a role for multiplane body undulations in sidewinding snakes to achieve turning maneuvers. Specifically, they observe that rattlesnakes adjust the relative amplitude and timing of the horizontal and vertical waves and that these changes are, in turn, correlated with shallow and sharp turning. Of course, correlations do not prove a mechanistic relationship, so the investigators looked for a complementary approach to determine whether these shifts in the traveling waves are indeed responsible for the animals extraordinary maneuverability. A natural approach for understanding such biomechanical mechanisms is the use of models—either computational (simulations) or physical (robots). Complex … [↵][1]2To whom correspondence should be addressed. Email: sstamper{at}jhu.edu. [1]: #xref-corresp-1-1

Walking dynamics are symmetric (enough)

Many biological phenomena such as locomotion, circadian cycles and breathing are rhythmic in nature and can be modelled as rhythmic dynamical systems. Dynamical systems modelling often involves neglecting certain characteristics of a physical system as a modelling convenience. For example, human locomotion is frequently treated as symmetric about the sagittal plane. In this work, we test this assumption by examining human walking dynamics around the steady state (limit-cycle). Here, we adapt statistical cross-validation in order to examine whether there are statistically significant asymmetries and, even if so, test the consequences of assuming bilateral symmetry anyway. Indeed, we identify significant asymmetries in the dynamics of human walking, but nevertheless show that ignoring these asymmetries results in a more consistent and predictive model. In general, neglecting evident characteristics of a system can be more than a modelling convenience—it can produce a better model.

Assessing the quality of force feedback in soft tissue simulation

Many types of deformable models have been proposed for simulation of soft tissue in surgical simulators, but their realism in comparison to actual tissue is rarely assessed. In this paper, a nonlinear mass-spring model is used for realtime simulation of deformable soft tissues and providing force feedback to a human operator. Force-deformation curves of real soft tissue samples were obtained experimentally, and the model was tuned accordingly. To test the realism of the model, we conducted two human-user experiments involving palpation with a rigid probe. First, in a discrimination test, users identified the correct category of real and virtual tissue better than chance, and tended to identify the tissues as real more often than virtual. Second, users identified real and virtual tissues by name, after training on only real tissues. The sorting accuracy was the same for both real and virtual tissues. These results indicate that, despite model limitations, the simulation could convey the feel of touching real tissues. This evaluation approach could be used to compare and validate various soft-tissue simulators.

Ultrasound elastography using regularized phase-zero cost function initialized with dynamic programming

Elastography, computation of elasticity modulus of tissue is one of medical imaging methods with applications such as tumor detection and ablation therapy. Phase-based time delay estimation methods exploit the frequency information of the RF data to obtain strain estimates [1]. Although iterative Phase zero estimation is more computationally efficient in comparison to methods that seek for the absolute maximum cross-correlation between precompression and postcompression echo signals, it is quite sensitive to noise. The reason for this sensitivity is that for this iterative method an initial guess for the time shift is needed for each pixel. To estimate time shifts for the sample k, the time shift resulted from iterative phase zero method applied on sample k-1 is used as an initial value. This makes the method sensitive to noise because the error is propagating sample by sample and if the method gets unstable for any pixel, it will give unstable result for the following pixels in image line. Proposed strategy in this work to overcome this problem is to first estimate the displacement using Dynamic Programming [2] and use the results from DP as an initial guess of displacement for each pixel in iterative Phase zero method. Recently, regularized methods that incorporate the prior of tissue continuity in time delay estimation have been shown to produce low-noise and high contrast strain images [3,5]. In this work, we also incorporate the prior of tissue motion continuity in the phase zero method to make the zero-phase method more robust to signal decorrelation.