Scripting human animations in a virtual environment

The current deficiencies of virtual environment (VE) are well known: annoying lag time in drawing the current view, drastically simplified environments to reduce that time lag, low resolution and narrow field of view. Animation scripting is an application of VE technology which can be carried out successfully despite these deficiencies. The final product is a smoothly moving high resolution animation displaying detailed models. In this system, the user is represented by a human computer model with the same body proportions. Using magnetic tracking, the motions of the model's upper torso, head and arms are controlled by the user's movements (18 degrees of freedom). The model's lower torso and global position and orientation are controlled by a spaceball and keypad (12 degrees of freedom). Using this system human motion scripts can be extracted from the user's movements while immersed in a simplified virtual environment. Recorded data is used to define key frames; motion is interpolated between them and post processing adds a more detailed environment. The result is a considerable savings in time and a much more natural-looking movement of a human figure in a smooth and seamless animation

Correlation and prediction of dynamic human isolated joint strength from lean body mass

A relationship between a person's lean body mass and the amount of maximum torque that can be produced with each isolated joint of the upper extremity was investigated. The maximum dynamic isolated joint torque (upper extremity) on 14 subjects was collected using a dynamometer multi-joint testing unit. These data were reduced to a table of coefficients of second degree polynomials, computed using a least squares regression method. All the coefficients were then organized into look-up tables, a compact and convenient storage/retrieval mechanism for the data set. Data from each joint, direction and velocity, were normalized with respect to that joint's average and merged into files (one for each curve for a particular joint). Regression was performed on each one of these files to derive a table of normalized population curve coefficients for each joint axis, direction, and velocity. In addition, a regression table which included all upper extremity joints was built which related average torque to lean body mass for an individual. These two tables are the basis of the regression model which allows the prediction of dynamic isolated joint torques from an individual's lean body mass

Distance‐based time series classification approach for task recognition with application in surgical robot autonomy

BackgroundRobotic‐assisted surgery allows surgeons to perform many types of complex operations with greater precision than is possible with conventional surgery. Despite these advantages, in current systems, a surgeon should communicate with the device directly and manually. To allow the robot to adjust parameters such as camera position, the system needs to know automatically what task the surgeon is performing.MethodsA distance‐based time series classification framework has been developed which measures dynamic time warping distance between temporal trajectory data of robot arms and classifies surgical tasks and gestures using a k‐nearest neighbor algorithm.ResultsResults on real robotic surgery data show that the proposed framework outperformed state‐of‐the‐art methods by up to 9% across three tasks and by 8% across gestures.ConclusionThe proposed framework is robust and accurate. Therefore, it can be used to develop adaptive control systems that will be more responsive to surgeons’ needs by identifying next movements of the surgeon. Copyright © 2016 John Wiley & Sons, Ltd.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138333/1/rcs1766.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138333/2/rcs1766_am.pd

Comparison of Extravehicular Mobility Unit (EMU) suited and unsuited isolated joint strength measurements

In this study the strength of subjects suited in extravehicular mobility units (EMU's) - or Space Shuttle suits - was compared to the strength of unsuited subjects. The authors devised a systematic and complete data set that characterizes isolated joint torques for all major joints of EMU-suited subjects. Six joint motions were included in the data set. The joint conditions of six subjects were compared to increase our understanding of the strength capabilities of suited subjects. Data were gathered on suited and unsuited subjects. Suited subjects wore Class 3 or Class 1 suits, with and without thermal micrometeoroid garments (TMG's). Suited and unsuited conditions for each joint motion were compared. From this the authors found, for example, that shoulder abduction suited conditions differ from each other and from the unsuited condition. A second-order polynomial regression model was also provided. This model, which allows the prediction of suited strength when given unsuited strength information, relates the torques of unsuited conditions to the torques of all suited conditions. Data obtained will enable computer modeling of EMU strength, conversion from unsuited to suited data, and isolated joint strength comparisons between suited and unsuited conditions at any measured angle. From these data mission planners and human factors engineers may gain a better understanding of crew posture, and mobility and strength capabilities. This study also may help suit designers optimize suit strength, and provide a foundation for EMU strength modeling systems

Automated robot‐assisted surgical skill evaluation: Predictive analytics approach

BackgroundSurgical skill assessment has predominantly been a subjective task. Recently, technological advances such as robot‐assisted surgery have created great opportunities for objective surgical evaluation. In this paper, we introduce a predictive framework for objective skill assessment based on movement trajectory data. Our aim is to build a classification framework to automatically evaluate the performance of surgeons with different levels of expertise.MethodsEight global movement features are extracted from movement trajectory data captured by a da Vinci robot for surgeons with two levels of expertise – novice and expert. Three classification methods – k‐nearest neighbours, logistic regression and support vector machines – are applied.ResultsThe result shows that the proposed framework can classify surgeons’ expertise as novice or expert with an accuracy of 82.3% for knot tying and 89.9% for a suturing task.ConclusionThis study demonstrates and evaluates the ability of machine learning methods to automatically classify expert and novice surgeons using global movement features.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141457/1/rcs1850.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141457/2/rcs1850_am.pd

Raman spectral signatures of mouse mammary tissue and associated lymph nodes: normal, tumor and mastitis

Raman spectroscopy involves the interaction of light with the molecular vibrations and therefore can provide information about molecular structure, tissue composition and changes in its environment. We explored whether Raman spectroscopy can reliably distinguish mammary tumors from normal mammary tissues and other pathological states in mice. We analyzed a large number of Raman spectra from the tumor and normal mammary glands of mice injected with 4T1 tumor cells, which were collected using a high-resolution (less than 4 cm −1 ) Raman spectrometer at a fixed (785 nm) laser excitation wavelength and with 60 mW of laser power. The spectra of normal and tumor mammary glands showed consistent differences in the intensity of certain Raman bands and loss of some bands in the tumor spectra. Multivariate statistical methods—principal component analysis (PCA) and discriminant functional analysis (DFA)—were used to separate the data into different groups of mammary tumors, mastitis, lymph nodes contralateral and tumor-cell-injected sides, and normal contralateral and tumor-cell-injected sides. We demonstrate that this spectroscopic technique has the feasibility of discriminating tumor and mastitis from normal tissues and other pathological states in a short period of time and may detect tumor transformation earlier than the standard histological examination stage. Copyright © 2006 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/55947/1/1565_ftp.pd

Physicochemical characterization, toxicity and in vivo biodistribution studies of a discoidal, lipid-based drug delivery vehicle: Lipodisq nanoparticles containing doxorubicin

Many promising pharmaceutically active compounds have low solubility in aqueous environments and their encapsulation into efficient drug delivery vehicles is crucial to increase their bioavailability. Lipodisq nanoparticles are approximately 10 nm in diameter and consist of a circular phospholipid bilayer, stabilized by an annulus of SMA (a hydrolysed copolymer of styrene and maleic anhydride). SMA is used extensively in structural biology to extract and stabilize integral membrane proteins for biophysical studies. Here, we assess the potential of these nanoparticles as drug delivery vehicles, determining their cytotoxicity and the in vivo excretion pathways of their polymer and lipid components. Doxorubicin-loaded Lipodisqs were cytotoxic across a panel of cancer cell lines, whereas nanoparticles without the drug had no effect on cell proliferation. Intracellular doxorubicin release from Lipodisqs in HeLa cells occurred in the low-pH environment of the endolysosomal system, consistent with the breakdown of the discoidal structure as the carboxylate groups of the SMA polymer become protonated. Biodistribution studies in mice showed that, unlike other nanoparticles injected intravenously, most of the Lipodisq components were recovered in the colon, consistent with rapid uptake by hepatocytes and excretion into bile. These data suggest that Lipodisqs have the potential to act as delivery vehicles for drugs and contrast agents