nils-herrmann/clean_scirepeval huggingface dataset:
Nonlinear modeling of FES-supported standing-up in paraplegia for selection of feedback sensors

This paper presents analysis of the standing-up manoeuvre in paraplegia considering the body supportive forces as a potential feedback source in functional electrical stimulation (FES)-assisted standing-up. The analysis investigates the significance of arm, feet, and seat reaction signals to the human body center-of-mass (COM) trajectory reconstruction. The standing-up behavior of eight paraplegic subjects was analyzed, measuring the motion kinematics and reaction forces to provide the data for modeling. Two nonlinear empirical modeling methods are implemented-Gaussian process (GP) priors and multilayer perceptron artificial neural networks (ANN)-and their performance in vertical and horizontal COM component reconstruction is compared. As the input, ten sensory configurations that incorporated different number of sensors were evaluated trading off the modeling performance for variables chosen and ease-of-use in everyday application. For the purpose of evaluation, the root-mean-square difference was calculated between the model output and the kinematics-based COM trajectory. Results show that the force feedback in COM assessment in FES assisted standing-up is comparable alternative to the kinematics measurement systems. It was demonstrated that the GP provided better modeling performance, at higher computational cost. Moreover, on the basis of averaged results, the use of a sensory system incorporating a six-dimensional handle force sensor and an instrumented foot insole is recommended. The configuration is practical for realization and with the GP model achieves an average accuracy of COM estimation 16 /spl plusmn/ 1.8 mm in horizontal and 39 /spl plusmn/ 3.7 mm in vertical direction. Some other configurations analyzed in the study exhibit better modeling accuracy, but are less practical for everyday usage.
Switching and Learning in Feedback Systems, European Summer School on Multi-Agent Control, Maynooth, Ireland, September 8-10, 2003, Revised Lectures and Selected Papers

When there are many people who don't need to expect something more than the benefits to take, we will suggest you to have willing to reach all benefits. Be sure and surely do to take this switching and learning in feedback systems european summer school on multi agent control maynooth ireland september 8 1
Tilt-Based Automatic Zooming and Scaling in Mobile Devices - A State-Space Implementation

We provide a dynamic systems interpretation of the coupling of internal states involved in speed-dependent automatic zooming, and test our implementation on a text browser on a Pocket PC instrumented with an accelerometer. The dynamic systems approach to the design of such continuous interaction interfaces allows the incorporation of analytical tools and constructive techniques from manual and automatic control theory. We illustrate experimental results of the use of the proposed coupled navigation and zooming interface with classical scroll and zoom alternatives.
Girard, A. and Murray-Smith, R. (2005) Gaussian processes: prediction at a noisy input and application to iterative multiple-step ahead forecasting

With the Gaussian Process model, the predictive distribution of the output corresponding to a new given input is Gaussian. But if this input is uncertain or noisy, the predictive distribution becomes non-Gaussian. We present an analytical approach that consists of computing only the mean and variance of this new distribution (Gaussian approximation). We show how, depending on the form of the covariance function of the process, we can evaluate these moments exactly or approximately (within a Taylor approximation of the covariance function). We apply our results to the iterative multiple-step ahead prediction of non-linear dynamic systems with propagation of the uncertainty as we predict ahead in time. Finally, using numerical examples, we compare the Gaussian approximation to the numerical approximation of the true predictive distribution by simple Monte-Carlo.
Variability in Wrist-Tilt Accelerometer Based Gesture Interfaces

In this paper we describe a study that examines human performance in a tilt control targeting task on a PDA. A three-degree of freedom accelerometer attached to the base of the PDA allows users to navigate to the targets by tilting their wrist in different directions. Post hoc analysis of performance data has been used to classify the ease of targeting and variability of movement in the different directions. The results show that there is an increase in variability of motions upwards from the centre, compared to downwards motions. Also the variability in the x axis component of the motion was greater than that in the y axis. This information can be used to guide designers as to the ease of various relative motions, and can be used to reshape the dynamics of the interaction to make each direction equally easy to achieve.
Model-based Target Sonification on Mobile Devices

We investigate the use of audio and haptic feedback to augment the display of a mobile device controlled by tilt input. We provide an example of this based on Doppler effects, which highlight the user's approach to a target, or a target's movement from the current state, in the same way we hear the pitch of a siren change as it passes us. Twelve participants practiced navigation/browsing a state-space that was displayed via audio and vibrotactile modalities. We implemented the experiment on a Pocket PC, with an accelerometer attached to the serial port and a headset attached to audio port. Users navigated through the environment by tilting the device. Feedback was provided by audio displayed via a headset, and by vibrotactile information displayed via a vibrotactile unit in the Pocket PC. Users selected targets placed randomly in the state-space, supported by combinations of audio, visual and vibrotactile cues. The speed of target acquisition and error rate were measured, and summary statistics on the acquisition trajectories were calculated. These data were used to compare different display combinations and configurations. The results in the paper quantified the changes brought by predictive or 'quickened' sonified displays in mobile, gestural interaction.
Pointing without a pointer

We present a method for performing selection tasks based on continuous control of multiple, competing agents who try to determine the user's intentions from their control behaviour without requiring an explicit pointer. The entropy in the selection process decreases in a continuous fashion -- we provide experimental evidence of selection from 500 initial targets. The approach allows adaptation over time to best make use of the multimodal communication channel between the human and the system. This general approach is well suited to mobile and wearable applications, shared displays and security conscious settings.
Muscle tremor as an input mechanism

We present the use of human physiological tremor, detected by a linear accelerometer, as a novel form of input for mobile devices. This allows the device to be aware if it is being held in a human hand, and in which posture. It also has security applications, as human tremor characteristics are individual. We describe two simple demonstrations of the use of tremor. One demo uses isometric muscle tremor as a proxy for a pressure sensor, and allows a user to ‘pump up’ an onscreen balloon. The second demo simulates a mobile phone application, using muscle tremor to enable the device to be aware if it is currently held by human hand, and to stop ringing when picked up.
Haptic granular synthesis: targeting, visualisation and texturing

This work introduces the idea of haptic rendering using granular synthesis - an established technique for synthesising audio. It describes the technique along with potential application areas, and initial results from an implementation on a PHANToM force feedback device. Three main applications are considered. Firstly, rendering of probabilistic vector fields for presenting ambiguity and context information to the user. Secondly, the possibility of producing textured virtual objects using granular synthesis is discussed. Thirdly, we use the approach to display scatterplot data on haptic devices.
Multimodal feedback for tilt controlled speed dependent automatic zooming

Speed Dependent Automatic Zooming proposed by Igarashi and Hinckley is a powerful tool for document navigation on mobile devices. We show that browsing and targeting can be facilitated by using a model-based sonification approach to generate audio feedback about document structure, in a tilt-controlled SDAZ interface. We implemented this system for a text browser on a Pocket PC instrumented with an accelerometer and headset, and found that audio feedback provided valuable information, supporting intermittent interaction, i.e. allowing movement-based interaction techniques to continue while the user is simultaneously involved with other tasks. This was demonstrated by a blindfolded user successfully locating specified elements in a text file.
Granular Synthesis for Display of Time-Varying Probability Densities

We present a method for displaying time-varying probabilistic information to users using an asynchronous granu- lar synthesis technique. We extend the basic synthesis technique to include distribution over waveform source, spatial position, pitch and time inside waveforms. To enhance the synthesis in interactive contexts, we "quicken" the display by integrating predictions of user behaviour into the sonification. This includes summing the derivatives of the distribution during exploration of static densities, and using Monte-Carlo sampling to predict future user states in nonlinear dynamic systems. These techniques can be used to improve user performance in continuous control systems and in the interactive exploration of high dimensional spaces. This technique provides feedback from users potential goals, and their progress toward achieving them; modulating the feedback with quickening can help shape the users actions toward achieving these goals. We have applied these techniques to a simple nonlinear control problem as well as to the sonification of on-line probabilistic gesture recognition. We are applying these displays to mobile, gestural interfaces, where visual display is often impractical. The granular synthesis approach is theo- retically elegant and easily applied in contexts where dynamic probabilistic displays are required.
Multivariable Generalized Minimum Variance Control Based on Artificial Neural Networks and Gaussian Process Models

The control of an unknown multivariable nonlinear process represents a challenging problem. Model based approaches, like Generalized Minimum Variance, provide a flexible framework for addressing the main issues arising in the control of complex nonlinear systems. However, the final performance will depend heavily on the models representing the system. This work presents a comparative analysis of two modelling approaches for nonlinear systems, namely Artificial Neural Network (ANN) and Gaussian processes. Their advantages and disadvantages as building blocks of a GMV controller are illustrated by simulation.
Dynamic Primitives for Gestural Interaction

We describe the implementation of an interaction technique which allows users to store and retrieve information and computational functionality on different parts of their body. We present a dynamic systems approach to gestural interaction using Dynamic Movement Primitives, which model a gesture as a second order dynamic system followed by a learned nonlinear transformation. We demonstrate that it is possible to learn models, even from single examples, which can simulate and classify the gestures needed for the Body Space project, running on a PocketPC with a 3-degree of freedom linear accelerometer.
Gaussian Processes: Prediction at a Noisy Input and Application to Iterative Multiple-Step Ahead Forecasting of Time-Series

With the Gaussian Process model, the predictive distribution of the output corresponding to a new given input is Gaussian. But if this input is uncertain or noisy, the predictive distribution becomes non-Gaussian. We present an analytical approach that consists of computing only the mean and variance of this new distribution (Gaussian approximation). We show how, depending on the form of the covariance function of the process, we can evaluate these moments exactly or approximately (within a Taylor approximation of the covariance function). We apply our results to the iterative multiple-step ahead prediction of non-linear dynamic systems with propagation of the uncertainty as we predict ahead in time. Finally, using numerical examples, we compare the Gaussian approximation to the numerical approximation of the true predictive distribution by simple Monte-Carlo.
Haptic dancing: human performance at haptic decoding with a vocabulary

The inspiration for this study is the observation that swing dancing involves coordination of actions between two humans that can be accomplished by pure haptic signaling. This study implements a leader-follower dance to be executed between a human and a PHANToM haptic device. The data demonstrates that the participants' understanding of the motion as a random sequence of known moves informs their following, making this vocabulary-based interaction fundamentally different from closed loop pursuit tracking. This robot leader does not respond to the follower's movement other than to display error from a nominal path. This work is the first step in an investigation of the successful haptic coordination between dancers, which will inform a subsequent design of a truly interactive robot leader.
Predictive control with Gaussian process models

This paper describes model-based predictive control based on Gaussian processes. Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of nonlinear dynamic systems. It offers more insight in variance of obtained model response, as well as fewer parameters to determine than other models. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. This property is used in predictive control, where optimisation of control signal takes the variance information into account. The predictive control principle is demonstrated on a simulated example of nonlinear system.
Hex: Dynamics and Probabilistic Text Entry

We present a gestural interface for entering text on a mobile device via continuous movements, with control based on feedback from a probabilistic language model. Text is represented by continuous trajectories over a hexagonal tessellation, and entry becomes a manual control task. The language model is used to infer user intentions and provide predictions about future actions, and the local dynamics adapt to reduce effort in entering probable text. This leads to an interface with a stable layout, aiding user learning, but which appropriately supports the user via the probability model. Experimental results demonstrate that the application of this technique reduces variance in gesture trajectories, and is competitive in terms of throughput for mobile devices. This paper provides a practical example of a user interface making uncertainty explicit to the user, and probabilistic feedback from hypothesised goals has general application in many gestural interfaces, and is well-suited to support multimodal interaction.
Got rhythm? Haptic-only lead and follow dancing

This paper describes the deign and implementation of a lead and follow dance to be performed with a human follower and PHANToM leader, or executed by two humans via two PHANToMs reciprocally linked. In some contexts, such as teaching calligraphic character writing, haptic-only communication of information has demonstrated limited effectiveness. This project was inspired by the observation that experienced partner dance followers can easily decipher haptic-only cues, perhaps because of the structure of dancing, wherein a set of possible moves is known and changes between moves happen at times signaled by the music. This work is a demonstration of dancing, meaning coordination of a randomly sequenced set of known movements to a musical soundtrack, with a PHANToM as a leader, and of a human leader and follower dancing via a dual PHANToM teleoperation system.
Nonlinear Predictive Control with a Gaussian Process Model

Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of nonlinear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coefficients to be optimized. This chapter illustrates possible application of Gaussian process models within model predictive control. The extra information provided by the Gaussian process model is used in predictive control, where optimization of the control signal takes the variance information into account. The predictive control principle is demonstrated via the control of a pH process benchmark.
Self-tuning Control of Non-linear Systems Using Gaussian Process Prior Models

Gaussian Process prior models, as used in Bayesian non-parametric statistical models methodology are applied to implement a nonlinear adaptive control law. The expected value of a quadratic cost function is minimised, without ignoring the variance of the model predictions. This leads to implicit regularisation of the control signal (caution) in areas of high uncertainty. As a consequence, the controller has dual features, since it both tracks a reference signal and learns a model of the system from observed responses. The general method and its unique features are illustrated on simulation examples.
Learning a Gaussian Process Model with Uncertain Inputs

Learning with uncertain inputs is well-known to be a difficul t task. In order to achieve this analytically using a Gaussian Process prior model, we expand the original process around the input mean (Delta method), assuming the random input is normally distributed. We thus derive a new process whose covariance function accounts for the randomness of the input. We illustrate the effectiveness of the proposed model on a simple static simulation example and on the modelling of a nonlinear noisy time-series.
Multiple-step ahead prediction for non linear dynamic systems: A Gaussian Process treatment with propagation of the uncertainty

We consider the problem of multi-step ahead prediction in ti me series analysis using the non-parametric Gaussian process model. k-step ahead forecasting of a discrete-time non-linear dynamic system c an be performed by doing repeated one-step ahead predictions. For a s tate-space model of the formyt = f(yt 1; : : : ; yt L), the prediction ofy at time t+ k is based on the estimates ŷt+k 1; : : : ; ŷt+k L of the previous outputs. In this paper, we show how, using an analytical Gaussia n approximation, we can formally incorporate the uncertainty about f uture regressor values, thus updating the uncertainty on the current pre diction.
Nonlinear adaptive control using non-parametric Gaussian Process prior models

Nonparametric Gaussian Process prior models, taken from Bayesian statistics methodology are used to implement a nonlinear adaptive control law. The expected value of a quadratic cost function is minimised, without ignoring the variance of the model predictions. This leads to implicit regularisation of the control signal (caution), and excitation of the system. The controller has dual features, since it is both tracking a reference signal and learning a model of the system from observed responses. The general method and its main features are illustrated on a simulation example.
Supporting Early Intervention Programmes for special- needs children with personal digital assistants

We describe flexible logging software designed for use in behavioural therapy programmes for special needs children. This software runs on a handheld computer or Personal Digital Assistant (PDA) and is flexible and easy to use, so that parents or teachers can customise the logger to the needs of individual children and programmes. The software is intended to allow parents or teachers to record high quality behavioural data with minimal intrusion into the teaching and interaction with children. We also give an example of how PDAs can be used directly as a teaching tool, with a program which allows parents to design a speech/picture association game for their own child.
Derivative Observations in Gaussian Process Models of Dynamic Systems

Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model. This is of particular importance in identification of nonlinear dynamic systems from experimental data. 1) It allows us to combine derivative information, and associated uncertainty with normal function observations into the learning and inference process. This derivative information can be in the form of priors specified by an expert or identified from perturbation data close to equilibrium. 2) It allows a seamless fusion of multiple local linear models in a consistent manner, inferring consistent models and ensuring that integrability constraints are met. 3) It improves dramatically the computational efficiency of Gaussian process models for dynamic system identification, by summarising large quantities of near-equilibrium data by a handful of linearisations, reducing the training set size - traditionally a problem for Gaussian process models.
Gaussian Process Priors with Uncertain Inputs - Application to Multiple-Step Ahead Time Series Forecasting

We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. k-step ahead forecasting of a discrete-time non-linear dynamic system can be performed by doing repeated one-step ahead predictions. For a state-space model of the form yt = f(yt-1,...,yt-L), the prediction of y at time t + k is based on the point estimates of the previous outputs. In this paper, we show how, using an analytical Gaussian approximation, we can formally incorporate the uncertainty about intermediate regressor values, thus updating the uncertainty on the current prediction.