This study explores the application of functional data analysis (FDA) to hand roll velocity during radial suturing on the SutureCoach bench simulator for evaluating open suturing performance. By treating temporal sensor data as mathematical functions, FDA provides a holistic view of the dynamic changes in hand roll, offering comprehensive assessments that are easily interpretable and clinically relevant. Cluster analysis was performed on hand roll profiles from 96 subjects, categorized into advanced surgeons, trainee surgeons, and novices. Functional k-means, using dynamic time-warping to align curves, were used to partition the data into two preset numbers of clusters (3 and 6). Both clustering models (3-cluster and 6-cluster) effectively clustered performance into groups with distinct characteristics and levels of skill (evident from visual inspection of cluster centroids). The relationship between cluster membership and suturing skills was corroborated using proxy measures of skill: expert global rating scale ratings, clinical status and expertise, and simulator-derived metrics. The findings of this study offer valuable insight into essential components of suturing skill and can improve the autonomy and efficiency of simulation-based suturing training. The clinical relevance of our results is immediately pertinent to the field of surgical skill assessment, where FDA-based methods could potentially be employed for objective feedback and training.

Compliant mechanisms provide an attractive alternative to conventional rigid mechanisms in the design of ultra low-cost precision positioning systems. The desirable performance characteristics of these mechanisms including freedom from backlash, long life, light weight, and ease of fabrication/assembly make them an ideal solution to the problem of inexpensive precision positioning for microassembly. This paper presents a design for a 2 axis precision positioning system which makes use of large displacement compliant mechanisms, a room temperature and pressure molding fabrication process, commodity hardware, and a piecewise linear interpolation compensation scheme to achieve positioning performance suitable for automated assembly of sub-centimeter robotic and mechatronic devices

This paper discusses the biomimetic design and assembly of a 3g self-contained crawling robot fabricated through the integrated use of various microrobot technologies. The hexapod structure is designed to move in an alternating tripod gait driven by two piezoelectric actuators connected by sliding plates to two sets of three legs. We present results of both the kinematic and static analyses of the driving mechanism that essentially consists of three slider cranks in series. This analysis confirmed the force differential needed to propel the device. We then review various other microrobot technologies that have been developed including actuator design and fabrication, power and control electronics design, programming via a finite state machine, and the development of bioinspired fiber arrays. These technologies were then successfully integrated into the device. The robot is now functioning and we have already fabricated three iterations of the proposed device. We hope with further design iterations to produce a fully operational model in the near future

Gait parameter adaptation on a physical robot is an error-prone, tedious and time-consuming process. In this paper we present a system for gait adaptation in our RHex series of hexapedal robots that renders this arduous process nearly autonomous. The robot adapts its gait parameters by recourse to a modified version of Nelder-Mead descent, while managing its self-experiments and measuring the outcome by visual servoing within a partially engineered environment The resulting performance gains extend considerably beyond what we have managed with hand tuning. For example, the best hand tuned alternating tripod gaits never exceeded 0.8 m/s nor achieved specific resistance below 2.0. In contrast, Nelder-Mead based tuning has yielded alternating tripod gaits at 2.7 m/s (well over 5 body lengths per second) and reduced specific resistance to 0.6 while requiring little human intervention at low and moderate speeds. Comparable gains have been achieved on the much larger ruggedized version of this machine.

The class of piecewise linear homeomorphisms (PLH) provides a convenient functional representation for many applications wherein an approximation to data is required that is invertible in closed form. In this paper we introduce the graph intersection (GI) algorithm for "learning" piecewise linear scalar functions in two settings: "approximation", where an "oracle" outputs accurate functional values in response to input queries; and "estimation", where only a fixed discrete data base of input-output pairs is available. We provide a local convergence result for the approximation version of the GI algorithm as well as a study of its numerical performance in the estimation setting. We conclude that PLH offers accuracy closed to that of a neural net while requiring, via our GI algorithm, far shorter training time and preserving desired invariant properties unlike any other presently popular basis family.