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With increased rates of smartphone theft over the past decade, mobile authentication systems that operate on a continual basis are a necessity to meet increasing demands for user privacy, device usage, and authentication accuracy. Rather than forcing end users to continually self-authenticate via password pins or through other means on a time-interval basis [2–7], a system that continuously authenticates users provides a more frictionless relationship between a user's device and its physical security. Such a system, if effectively operating on a low-powered, unobtrusive, and secure basis, would make it viable for most consumer mobile devices. In this paper, we build upon our work in [1] to provide a novel authentication scheme that meets these requirements for a commonly adopted system. Our system, iSentinel, hopes to provide an unobtrusive, low-powered solution for detecting and responding to common theft scenarios by continuously authenticating mobile devices in use cases such as walking, texting, and driving.

We have developed a set of tools based on the OpenSim simulation framework that allows for input of anthropometry, muscle geometry, and measured strength capability to generate demographically tuned models from a core generic musculoskeletal model. The strength tuning capability exploits an algorithm that adaptively tunes muscle parameters in generic Hill-type muscle models to generate performance data consistent with ergonomic subject studies of specific demographic populations (e.g. elderly populations). Following the tuning of the generic model to generate a demographic-specific model, human performance in a variety of scenarios can then be analyzed. Currently, the model is in a prototype phase and has been applied to scenarios modeling elderly passengers interacting with airplane interior features including overhead bins and lavatories. The next phase of development will include manufacturing scenarios with input based on motion capture and worker demographics, including strength measurements.

Holonomic constraints are ubiquitous in multibody systems. We present an approach to effectively address the control of holonomically constrained systems using a novel decomposition of task, constraint, and posture space. In addition to providing a natural approach for motion control in the presence of constraints, this scheme also allows for concurrent specification of desired constraint forces, given sufficient actuation. It does this by exposing both motion coordinates and constraint forces within the control formalism, allowing for substantial flexibility in control synthesis. Implementations are presented based on a partitioning of the constraint forces into controlled and uncontrolled subsets, as well as a specification of implicit conditions on the constraint forces. A number of examples demonstrate the practical efficacy of the approach. Finally, a system-level methodology for constraint management during robot interactions with the environment is presented.

In the previous two chapters we addressed zeroth- and first-order variational principles. In this chapter we will focus on the second-order variation of displacement. We will begin with Gauss's Principle (Gauss 1829). The principle is based on the notion of virtual acceleration. The derivation of the equations of motion for particles and rigid bodies using Gauss's Principle closely parallels the derivation of the equations of motion using d'Alembert's Principle and Jourdain's Principle in the previous chapters. As with d'Alembert's and Jourdain's Principles, Gauss's Principle can be seen as an independent principle of analytical dynamics. Therefore, as with the previous chapters, this chapter is presented in a stand-alone manner. Virtual Accelerations Virtual accelerations refer to all accelerations of a system that satisfy the scleronomic constraints. In the case of virtual accelerations, time, position, and velocity are frozen or stationary. Gauss's Principle PRINCIPLE 7.1 The virtual acceleration work of a system is stationary. That is, δA = 0. Additionally, the constraints of the system generate no virtual acceleration work, δA c = 0. This is known as Gauss's Principle . As with d'Alembert's Principle and Jourdain's Principle, it is noted that while Gauss's Principle can be seen as providing an alternate statement of Newton's second law, for interacting bodies, a law of action and reaction (Newton's third law) is still needed. Therefore, when we use Gauss's Principle to derive the equations of motion for systems of particles/bodies, we will invoke the law of action and reaction. A Single Particle Gauss's Principle for a single point mass with a discrete set of n f external forces, ﹛ f 1 , …, f nf ﹜, acting on it is expressed as where δ a represents the acceleration variations. During these variations, time, position, and velocity are stationary. That is, δt = 0, δ r = 0, and δ v = 0.

In the previous chapter we addressed zeroth-order variational principles rooted in the fundamental principle of d'Alembert. In this chapter we will focus on the first-order variation of displacement. We will begin with Jourdain's Principle of Virtual Power (Jourdain 1909). The principle is based on the notion of virtual velocity. The derivation of the equations of motion for particles and rigid bodies using Jourdain's Principle closely parallels the derivation of the equations of motion using d'Alembert's Principle in the previous chapter. While Jourdain's Principle is analogous to d'Alembert's Principle, it can be seen as an independent principle of analytical dynamics. Therefore, the material in the previous chapter is not a prerequisite to this chapter, and this chapter will be treated in a stand-alone manner. Virtual Velocities Virtual velocities refer to all velocities of a system that satisfy the scleronomic constraints of the system. In the case of virtual velocities, time and position are frozen or stationary. Jourdain's Principle of Virtual Power PRINCIPLE 6.1 The virtual power of a system is stationary. That is, δP = 0. Additionally, the constraints of the system generate no virtual power, δP c = 0. This is known as Jourdain's Principle . As with d'Alembert's Principle, it is noted that while Jourdain's Principle can be seen as providing an alternate statement of Newton's second law, for interacting bodies, a law of action and reaction (Newton's third law) is still needed. Therefore, when we use Jourdain's Principle to derive the equations of motion for systems of particles/bodies, we will invoke the law of action and reaction. A Single Particle Jourdain's Principle for a single point mass with a discrete set of n f external forces, ﹛ f 1 , …, f nf ﹜, acting on it is expressed as where δ v represents the velocity variations. During these variations, time and position are stationary. That is, δt = 0 and δ r = 0.

Providing a unique bridge between the foundations of analytical mechanics and application to multi-body dynamical systems, this textbook is particularly well suited for graduate students seeking an understanding of the theoretical underpinnings of analytical mechanics, as well as modern task space approaches for representing the resulting dynamics that can be exploited for real-world problems in areas such as biomechanics and robotics. Established principles in mechanics are presented in a thorough and modern way. The chapters build up from general mathematical foundations, an extensive treatment of kinematics, and then to a rigorous treatment of conservation and variational principles in mechanics. Parallels are drawn between the different approaches, providing the reader with insights that unify his or her understanding of analytical dynamics. Additionally, a unique treatment is presented on task space dynamical formulations that map traditional configuration space representations into more intuitive geometric spaces.

Finding the balance between security, privacy, and usability for mobile authentication has been an active area of research for the past several years. Many researchers have taken advantage of the availability of multiple sensors on mobile devices and have used these data to train classifiers to authenticate users. For example, implicit authentication algorithms have been developed based on behavior patterns identified from a combination of sensors including location, co-location, application usage, biometric measurements, continuity of interaction between the user and the phone, and possession of the phone [1,2,3,4,5]. Furthermore, the onboard sensors of mobile devices have previously been used to identify users based on touch [6] and fusions of touch and speech inputs [7]. However, a system utilizing low-power onboard electronics for anomaly detection and user classification is lacking. Here, we report on the performance of two subsystems tested in a controlled use scenario to classify and authenticate users of a mobile device. The overall system utilizes two subsystems for anomaly detection and user classification: (1) a neuromorphic chip for continuous, low-power, online monitoring and classification, and (2) an early warning system (EWS) algorithm for longer duration time-series behavioral and biometric classification.

International audience; Least action principles provide an insightful starting point from which problems involving constraints and task-level objectives can be addressed. In this paper, the principle of least action is first treated with regard to holonomic constraints in multibody systems. A variant of this, the principle of least curvature or straightest path, is then investigated in the context of geodesic paths on constrained motion manifolds. Subsequently, task space descriptions are addressed and the operational space approach is interpreted in terms of least action. Task-level control is then applied to the problem of cost minimization. Finally, task-level optimization is formulated with respect to extremizing an objective criterion, where the criterion is interpreted as the action of the system. Examples are presented which illustrate these approaches.

This paper presents a task-level control methodology for the general class of holonomically constrained multibody systems. As a point of departure, the general formulation of constrained dynamical systems is reviewed with respect to multiplier and minimization approaches. Subsequently, the operational space framework is considered and the underlying symmetry between constrained dynamics and operational space control is discussed. Motivated by this symmetry, approaches for constrained task-level control are presented which cast the general formulation of constrained multibody systems into a task space setting using the operational space framework. This provides a means of exploiting task-level control structures, native to operational space control, within the context of constrained systems. This allows us to naturally synthesize dynamic compensation for a multibody system, that properly accounts for the system constraints while performing a control task. A set of examples illustrate this control implementation. Additionally, the inclusion of flexible bodies in this approach is addressed.

We present a new approach for identifying features of ligand-protein binding interfaces that predict binding selectivity and demonstrate its effectiveness for predicting kinase inhibitor specificity. We analyzed a large set of human kinases and kinase inhibitors using clustering of experimentally determined inhibition constants (to define specificity classes of kinases and inhibitors) and virtual ligand docking (to extract structural and chemical features of the ligand-protein binding interfaces). We then used statistical methods to identify features characteristic of each class. Machine learning was employed to determine which combinations of characteristic features were predictive of class membership and to predict binding specificities and affinities of new compounds. Experiments showed predictions were 70% accurate. These results show that our method can automatically pinpoint on the three-dimensional binding interfaces pharmacophore-like features that act as "selectivity filters". The method is not restricted to kinases, requires no prior hypotheses about specific interactions, and can be applied to any protein families for which sets of structures and ligand binding data are available.

The behavioral richness exhibited in natural humanmotion results from the complex interplay of biomechanical and neurological factors. The biomechanical factors involve the kinematics and dynamics of the musculoskeletal system while the neurological factors involve the sensorimotor integration performed by the central nervous system (CNS). An adequate understanding of these factors is a prerequisite to understanding the overall effect on human motion as well as providing a means for synthesizing human motion. The fields of neuroscience, biomechanics, robotics, and computer graphics provide motivation, as well as tools, for understanding human motion. In neuroscience, fundamental scientific understanding drives the motivation to understand human motion, whereas, in biomechanics, clinical applications often form the driving motivation. These clinical applications involve the use of movement analysis and simulation tools to help direct patient rehabilitation as well as predict the effects of surgery on movement. In addition to the clinical desire to analyze movement there has been an emerging desire in recent years to synthetically generate human-like motion in both simulated and physical settings. In computer graphics this desire is directed toward autonomously generating realistic motion for virtual actors. The intent is to direct these virtual actors using high-level goal directed commands for which low-level motion control is automatically generated. Motivated by similar desires, the robotics community seeks a high-level control framework for robotic systems. With the recent advent of complex humanoid robots this challenge has grownmore demanding. Consistent with their anthropomorphic design, humanoid robots are intended to operate in a human-like manner within man-made environments and to promote interaction with their biological counterparts. To achieve this, common control strategies have involved generating joint space trajectories or learning specific motions, but these approaches require extensive motion planning computations and do not generalize well to related tasks.

Sandia's scientific and engineering expertise in the fields of computational biology, high-performance prosthetic limbs, biodetection, and bioinformatics has been applied to specific problems at the forefront of cancer research. Molecular modeling was employed to design stable mutations of the enzyme L-asparaginase with improved selectivity for asparagine over other amino acids with the potential for improved cancer chemotherapy. New electrospun polymer composites with improved electrical conductivity and mechanical compliance have been demonstrated with the promise of direct interfacing between the peripheral nervous system and the control electronics of advanced prosthetics. The capture of rare circulating tumor cells has been demonstrated on a microfluidic chip produced with a versatile fabrication processes capable of integration with existing lab-on-a-chip and biosensor technology. And software tools have been developed to increase the calculation speed of clustered heat maps for the display of relationships in large arrays of protein data. All these projects were carried out in collaboration with researchers at the University of Texas M. D. Anderson Cancer Center in Houston, TX.

The analysis of spacecraft kinematics and dynamics requires an efficient scheme for spatial representation. While the representation of displacement in three dimensional Euclidean space is straightforward, orientation in three dimensions poses particular challenges. The unit quaternion provides an approach that mitigates many of the problems intrinsic in other representation approaches, including the ill-conditioning that arises from computing many successive rotations. This report focuses on the computational utility of unit quaternions and their application to the reconstruction of re-entry vehicle (RV) motion history from sensor data. To this end they will be used in conjunction with other kinematic and data processing techniques. We will present a numerical implementation for the reconstruction of RV motion solely from gyroscope and accelerometer data. This will make use of unit quaternions due to their numerical efficacy in dealing with the composition of many incremental rotations over a time series. In addition to signal processing and data conditioning procedures, algorithms for numerical quaternion-based integration of gyroscope data will be addressed, as well as accelerometer triangulation and integration to yield RV trajectory. Actual processed flight data will be presented to demonstrate the implementation of these methods.

This report summarizes the current statistical analysis capability of OVIS and how it works in conjunction with the OVIS data readers and interpolators. It also documents how to extend these capabilities. OVIS is a tool for parallel statistical analysis of sensor data to improve system reliability. Parallelism is achieved using a distributed data model: many sensors on similar components (metaphorically sheep) insert measurements into a series of databases on computers reserved for analyzing the measurements (metaphorically shepherds). Each shepherd node then processes the sheep data stored locally and the results are aggregated across all shepherds. OVIS uses the Visualization Tool Kit (VTK) statistics algorithm class hierarchy to perform analysis of each process's data but avoids VTK's model aggregation stage which uses the Message Passing Interface (MPI); this is because if a single process in an MPI job fails, the entire job will fail. Instead, OVIS uses asynchronous database replication to aggregate statistical models. OVIS has several additional features beyond those present in VTK that, first, accommodate its particular data format and, second, improve the memory and speed of the statistical analyses. First, because many statistical algorithms are multivariate in nature and sensor data is typically univariate, interpolation of data is more » required to provide simultaneous observations of metrics. Note that in this report, we will refer to a single value obtained from a sensor as a measurement while a collection of multiple sensor values simultaneously present in the system is an observation. A base class for interpolation is provided that abstracts the operation of converting multiple sensor measurements into simultaneous observations. A concrete implementation is provided that performs piecewise constant temporal interpolation of multiple metrics across a single component. Secondly, because calculations may summarize data too large to fit in memory OVIS analyses batches of observations at a time and aggregates these intermediate intra-process models as it goes before storing the final model for inter-process aggregation via database replication. This reduces the memory footprint of the analysis, interpolation, and the database client and server query processing. This also interleaves processing with the disk I/O required to fetch data from the database - also improving speed. This report documents how OVIS performs analyses and how to create additional analysis components that fetch measurements from the database, perform interpolation, or perform operations on streamed observations (such as model updates or assessments). The rest of this section outlines the OVIS analysis algorithm and is followed by sections specific to each subtask. Note that we are limiting our discussion for now to the creation of a model from a set of measurements, and not including the assessment of observations using a model. The same framework can be used for assessment but that use case is not detailed in this report. « less

This paper presents an approach to formulating task-level motion-control for holonomically constrained multibody systems based on a mass-weighted orthogonal decomposition. The basis for this approach involves the formation of a recursive null space for constraints and motion-control tasks onto which subsequent motion-control tasks are projected. The recursive null space arises out of the process of orthogonalizing individual task Jacobian matrices. This orthogonalization process is analogous to the Gram–Schmidt process used for orthogonalizing a vector basis. Based on this mass-weighted orthogonal decomposition, recursive algorithms are developed for formulating the overall motion-control equations. The natural symmetry between task-level dynamics and the dynamics of constrained systems is exploited in this approach. An example is presented to illustrate the practical application of this methodology.

Archimedes’ genius was derived in no small part from his ability to effortlessly interpret problems in both geometric and mechanical ways. We explore, in a modern context, the application of mechanical reasoning to geometric problem solving. The general form of this inherently Archimedean approach is described and it’s specific use is demonstrated with regard to the problem of finding the geodesics of a surface. Archimedes’ approach to thinking about problems may be his greatest contribution, and in that spirit we present some work related to teaching Archimedes’ ideas at an elementary level. The aim is to cultivate the same sort of creative problem solving employed by Archimedes, in young students with nascent mechanical reasoning skills.

Understanding the basis of human movement and reproducing it in robotic environments is a compelling challenge that has engaged a multidisciplinary audience. In addressing this challenge, an important initial step involves reconstructing motion from experimental motion capture data. To this end we propose a new algorithm to reconstruct human motion from motion capture data through direct control of captured marker trajectories. This algorithm is based on a task/posture decomposition and prioritized control approach. This approach ensures smooth tracking of desired marker trajectories as well as the extraction of joint angles in real-time without the need for inverse kinematics. It also provides flexibility over traditional inverse kinematic approaches. Our algorithm was validated on a sequence of tai chi motions. The results demonstrate the efficacy of the direct marker control approach for motion reconstruction from experimental marker data.

In today's DP product realization enterprise it is imperative to reduce the design-to-fabrication cycle time and cost while improving the quality of DP parts (reducing defects). Much of this challenge resides in the inherent gap between the product and process worlds. The lack of seamless, bi-directional flow of information prevents true concurrency in the product realization world. This report addresses a framework for product-process data fusion to help achieve next generation product realization. A fundamental objective is to create an open environment for multichannel observation of process date, and subsequent mapping of that data onto product geometry. In addition to the sensor-based observation of manufacturing processes, model-based process data provides an important complement to empirically acquired data. Two basic groups of manufacturing models are process physics, and machine kinematics and dynamics. Process physics addresses analytical models that describe the physical phenomena of the process itself. Machine kinematic and dynamic models address the mechanical behavior of the processing equipment. As a secondary objective, an attempt has been made in this report to address part of the model-based realm through the development of an open object-oriented library and toolkit for machine kinematics and dynamics. Ultimately, it is desirable to integrate design definition, with all types of process data; both sensor-based and model-based. Collectively, the goal is to allow all disciplines within the product realization enterprise to have a centralized medium for the fusion of product and process data.

We present a novel approach for the control of digital humans modelled as musculoskeletal systems in physics-based environments. This approach uses high-level goal-oriented commands as input, from which low-level neuromuscular excitations are produced that generate motion consistent with the high-level command input. Central to our approach is the reformulation of a neuromuscular control algorithm in task space, a space defined by coordinates relevant to the specific task being performed, rather than the full configuration space of the digital human. A control methodology is also detailed for addressing holonomic constraints in the skeletal kinematics. Examples are presented that demonstrate the motion control architecture for an arm-reaching simulation.