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Exploring dynamic difficulty adjustment mechanism for rehabilitation tasks using physiological measures and subjective ratings

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IET SIGNAL PROCESSING 2019年第3期13卷 378-386页

作者： Ozkul, Fatih Palaska, Yunus Masazade, Engin Erol-Barkana, Duygun Yeditepe Univ Dept Elect & Elect Engn Istanbul Turkey

Task engagement is a key factor in sustaining patients' participation in rehabilitation. Adaptive task difficulty level adjustment techniques are designed to determine the appropriate exercise difficulty level in where subjects are appropriately challenged and engaged without causing any distress. Such adaptive difficulty adjustment within rehabilitation tasks has the potential to individualise training. In this study, the authors have compared two dynamic difficulty level adjustment algorithms, partially ordered set master (POSM) and increment/decrement one level (IDOL), those change the difficulty levels for each individual adaptively based on his/her performance. These two algorithms are integrated into the robot-assisted rehabilitation system, RehabRoby, and their functionality is explored via a small user study with 20 healthy subjects. The subjects were asked to perform a computer-based fruit picker game using RehabRoby under these two algorithms. The impacts of the adaptation algorithms are evaluated in terms of engagement of the subjects by looking at their physiological signals, performance (score), and survey results. Experiments show that although POSM on the average suggests easier difficulty levels to the subjects than IDOL, the subjects experience a wider range of difficulty levels in POSM that may help them to become more engaged in the game, which can also be observed by lower skin temperatures of the subjects.

关键词： medical robotics learning (artificial intelligence) patient rehabilitation human factors skin computer games psychology robot-assisted rehabilitation system 20 healthy subjects computer-based fruit picker game adaptation algorithms POSM easier difficulty levels dynamic difficulty adjustment mechanism rehabilitation tasks physiological measures subjective ratings task engagement adaptive task difficulty level adjustment techniques appropriate exercise difficulty level adaptive difficulty adjustment dynamic difficulty level adjustment algorithms increment decrement one level individual adaptively

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A framework for efficient resource management in IEEE 802.11 WLANs

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WIRELESS COMMUNICATIONS & MOBILE COMPUTING 2004年第8期4卷 835-848页

作者： Zhang, LQ Zeadally, S Wayne State Univ Dept Comp Sci High Speed Networking Lab Detroit MI 48202 USA

Currently deployed IEEE 802.11 WLANs work mostly with distributed coordination function (DCF) mode at the media access control (MAC) layer, which does not provide quality of service (QoS) support. The upcoming IEEE standard 802.11e achieves service differentiation by assigning different channel access parameters (CAPs) to different traffic classes at the MAC layer. However, such relative differentiation does not yield QoS guarantee. In practice, appropriately selecting CAPs a priori is difficult. Time-varying traffic loads also make the use of fixed CAPs, inefficient for both QoS support and channel utilization. In this work, we propose a novel architecture called HARMONICA, in which the access point (AP) dynamically selects the best CAPs for each traffic class to optimally match their QoS requirements. We present and discuss a simple admission control (AC) mechanism used by HARMONICA to avoid congestion. Our simulation results demonstrate that under an interference-free environment, HARMONICA can guarantee the QoS for all traffic classes while simultaneously achieving quasi-optimal channel utilization. Copyright (C) 2004 John Wiley Sons, Ltd.

关键词： quality of service multimedia IEEE 802.11 WLANs admission control adaptation algorithms channel access parameters

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DYNAMIC RATIO QUANTIZER

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PROCEEDINGS OF THE INSTITUTION OF ELECTRICAL ENGINEERS-LONDON 1978年第1期125卷 25-29页

作者： XYDEAS, CS STEELE, R Department of Electronic & Electrical Engineering Loughborough University of Technology Loughborough UK

An instantaneously adaptive nonlinear ratio quantiser, called a dynamic ratio quantiser (d.r.q.) is described. It enables a wide range of input signals to be quantised with a nearly constant signal/noise ratio (s.n.r.).The d.r.q. consists of an instantaneously adaptive nonlinear elementEL, whose output samples are a function of the ratio of the input and the feedback samples. Theoretically,ELwill transform input samples of any amplitude to samples within the range of a fixed uniform quantiser. The feedback samples are produced from the quantised output samples with the aid of an inverseELand a transversal *** theory of the d.r.q. is presented, and its s.n.r. performance for different adaptation algorithms is given.

关键词： digital communication systems feedback sample S/N ratio Codes dynamic ratio quantiser adaptation algorithms encoding

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A posteriori error learning in nonlinear adaptive filters

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IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING 1999年第6期146卷 293-296页

作者： Mandic, DP Chambers, JA Univ London Imperial Coll Sci Technol & Med Dept Elect & Elect Engn Commun & Signal Proc Grp London England

The authors provide relationships between the a priori and a posteriori errors of adaptation algorithms for real-time output-error nonlinear adaptive filters realised as feedforward or recurrent neural networks. The analysis is undertaken for a general nonlinear activation function of a neuron, and for gradient-based learning algorithms, for both a feedforward (FF) and recurrent neural network (RNN). Moreover, the analysis considers both contractive and expansive forms of the nonlinear activation functions within the networks. The relationships so obtained provide the upper and lower error bounds for general gradient based a posteriori learning in neural networks.

关键词： prediction theory adaptive filters gradient-based learning algorithms feedforward neural networks adaptation algorithms lower bounds gradient methods error analysis a posteriori error learning Neural nets Filtering methods in signal processing neuron Neural computing techniques upper bounds feedforward neural nets recurrent neural networks filtering theory learning (artificial intelligence) nonlinear filters recurrent neural nets nonlinear adaptive filters general nonlinear activation function Signal processing theory

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Partial-update NLMS algorithms with data-selective updating

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2004年第4期52卷 938-949页

作者： Werner, S de Campos, MLR Diniz, PSR Aalto Univ Signal Proc Lab FIN-02015 Espoo Finland Univ Fed Rio de Janeiro COPPE Poli BR-21945970 Rio De Janeiro Brazil

In this paper, we present mean-squared convergence analysis for the partial-update normalized least-mean square (PU-NLMS) algorithm with closed-form expressions for the case of white input signals. The formulae presented here are more accurate than the ones found in the literature for the PU-NLMS algorithm. Thereafter, the ideas of the partial-update NLMS-type algorithms found in the literature are incorporated in the framework of set-membership filtering, from which data-selective NLMS-type algorithms with partial-update are derived. The new algorithms, referred to herein as the set-membership partial-update normalized least-mean square (SM-PU-NLMS) algorithms, combine the data-selective updating from set-membership filtering with the reduced computational complexity from partial updating. A thorough discussion of the SM-PU-NLMS algorithms follows, whereby we propose different update strategies and provide stability analysis and closed-form formulae for excess mean-squared error (MSE). Simulation results verify the analysis for the PU-NLMS algorithm and the good performance of the SM-PU-NLMS algorithms in terms of convergence speed, final misadjustment, and computational complexity.

关键词： adaptation algorithms data-selective MSE analysis NLMS algorithm order statistics partial update set-membership filtering

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algorithms of Fast Adaptive Compensation of Disturbance in Linear Systems with Arbitrary Input Delay

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IFAC-PapersOnLine 2017年第1期50卷 12892-12897页

作者： Gerasimov, Dmitry N. Paramonov, Aleksei V. Nikiforov, Vladimir O. ITMO University 49 Kronverkskiy ave Saint Petersburg197101 Russia

The problem of fast compensation of multisinusoidal disturbance affecting on linear time-invariant (LTI) plants with known parameters and arbitrary input delay is considered. The disturbance is modeled as unmeasurable bounded output of linear exosystem of known order but with unknown parameters. Disturbance compensation is provided by means of direct adaptive control with two schemes of adaptation algorithm. The first scheme is based on the standard gradient adaptation algorithm, while the second one uses modified adaptation algorithm with integral cost function and improved parametric convergence. The stability of the closed-loop system in presence of delay and integral action (due to direct adaptation) in the feedforward loop is ensured by appropriate modifications of the adaptation schemes. These modifications allow to remove the limitations on upper bounds on adaptation gains (gain margins) corresponding to certain upper bounds of time delays (delay margins). The performance of proposed schemes of disturbance compensation is demonstrated via simulation. © 2017

关键词： Adaptive control systems Closed loop systems Cost functions Linear systems Parameter estimation adaptation algorithms Adaptive compensation Adaptive Control Certain upper bound Delayed systems Direct adaptive control Disturbance compensation Linear time invariant

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STATE-OF-THE-ART AND PROSPECTS OF ADAPTIVE SYSTEMS

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AUTOMATICA 1984年第5期20卷 547-557页

作者： VORONOV, AA RUTKOVSKY, VY Moscow U.S.S.R.

The theory and applications of adaptive control have developed very intensively. There are two reasons for this: variable parameters of controlled plants change within a broad range, on the one hand, and accuracy requirements to process equipment, aircraft systems etc. increase, on the other. Moreover, the wide use of digital computers permits now even complex adaptation algorithms to be easily realized. The paper attempts to review methods of design analysis and synthesis of adaptive systems (AS). Extreme systems, systems with passive adaptation and identification problems have not been considered. Since contributions of Soviet authors has been largely overlooked by the writers of earlier reviews (Åström, 1983; Fujii, 1981; and others) we have decided to focus our attention mainly on the results obtained in the U.S.S.R.

关键词： adaptation algorithms Adaptive systems basic loop optimal adaptive control

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Simple adaptive and robust control for a class of time-varying systems

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IFAC-PapersOnLine 2015年第11 11期48卷 511-516页

作者： Gerasimov, Dmitry N. Lyzlova, Maria V. Nikiforov, Vladimir O.

Adaptive and robust control algorithms for the class of uncertain time-varying linear plants of triangular structure corrupted by disturbance are proposed in the paper. The controls are designed on the basis of pole placement approach and have only one adjustable parameter – averaged radius of roots location ω. Parameter ω is increased according to two proposed schemes until the closed-loop system becomes stable and control error exponentially decays to the neighborhood of zero equilibrium. In the first scheme parameter ω is generated by the first order adaptation algorithm, while in the second scheme this parameter is generated by nonlinear static feedback. It is shown that both control algorithms are robust in respect to disturbance and the variations of the plant parameters. The properties of designed control systems are illustrated by means of MatLab/Simulink environment. © 2015

关键词： Adaptive control systems Closed loop systems Parameter estimation Robust control Time varying control systems adaptation algorithms Adjustable parameters Exponentially decays MATLAB/Simulink environment Nonlinear statics Plant parameters Simple adaptive Triangular structures

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Modifications of Finitely-Converging algorithms of Adaptive Control

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IFAC-PapersOnLine 2015年第11 11期48卷 568-573页

作者： Bondarko, Vladimir A. Department of Mathematics and Mechanics St. Petersburg State University Russia

Finitely-converging algorithms were originally proposed by *** to solve problems of pattern recognition. Then, in his seminal paper “To The Theory Of Adaptive Systems,” published in 1968 in Russian Mathematics Doklady, *** applied these algorithms for robot adaptive control. To our knowledge it was the first usage of the word “robot” in scientific literature. The proposed approach consists in reformulation of an adaptive control problem as the solution problem for some countable system of inequalities. A procedure of their solution plays a role of adaptation algorithm. It should converge in a finite time in a closed loop. Initially *** developed algorithms to solve a problem of adaptive control of discrete linear time-invariant systems. With the expansion of a set of the systems under consideration those basic algorithms were modified, but theirs main idea remained the same. Namely, each correction of the vector of estimated parameters ensures decreasing of the distance between current vector and unknown vector of true parameters by the positive value separated from zero. Thus there may be only finite number of these corrections. This article describes Modifications of basic ***'s algorithms in relation to adaptive control of several classes of the plants. Among them are hybrid continuous/discrete systems, nonlinear systems, infinite-dimensional systems, and so on. © 2015

关键词： Adaptive control systems Discrete time control systems Estimation Invariance Linear control systems Linear systems Pattern recognition Problem solving Time varying control systems adaptation algorithms Adaptive Control Countable systems Estimated parameter Infinite dimensional system Linear time invariant systems Scientific literature Solution problems

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Improving performance of adaptive feedforward noise attenuators using a dynamic adaptation gain

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JOURNAL OF SOUND AND VIBRATION 2023年第1期560卷

作者： Landau, Ioan Dore Vau, Bernard Airimitoaie, Tudor-Bogdan Buche, Gabriel Univ Grenoble Alpes GIPSA lab CNRS Grenoble INP F-38000 Grenoble France Ixblue F-94380 Bonneuil Sur Marne France Univ Bordeaux IMS lab Bordeaux INP CNRS F-33405 Talence France

The paper explores in detail the use of dynamic adaptation gain/learning rate (DAG) for improving the performance of adaptive feedforward attenuation schemes. The DAG is an ARMA (poles-zeros) filter embedded in gradient type adaptation/learning algorithms and generalizes the various improved gradient algorithms available in the literature. After introducing the DAG algorithm in the context of adaptive feedforward attenuation schemes and providing relation-ships with other algorithms, its design is developed. Strictly Positive Real (SPR) conditions play an important role in the design of the DAG. Then the stability issues for adaptive/learning systems using a DAG are discussed. The potential of the DAG is then illustrated by experimental results obtained on a relevant adaptive active noise control system.

关键词： Active noise control Adaptive feedforward compensation Youla-Kucera parametrization adaptation algorithms Learning algorithms

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