M. Osman Tokhi

Bio: M. Osman Tokhi is an academic researcher from London South Bank University. The author has contributed to research in topics: PID controller & Control theory. The author has an hindex of 5, co-authored 18 publications receiving 240 citations. Previous affiliations of M. Osman Tokhi include University of Sheffield.

A fuzzy clustering neural network architecture for multifunction upper-limb prosthesis

Abstract: Accurate and computationally efficient means of classifying surface myoelectric signals has been the subject of considerable research effort in recent years. The aim of this paper is to classify myoelectric signals using new fuzzy clustering neural network (NN) architectures to control multifunction prostheses. This paper presents a comparative study of the classification accuracy of myoelectric signals using multilayered perceptron NN using back-propagation, conic section function NN, and new fuzzy clustering NNs (FCNNs). The myoelectric signals considered are used in classifying six upper-limb movements: elbow flexion, elbow extension, wrist pronation and wrist supination, grasp, and resting. The results suggest that FCNN can generalize better than other NN algorithms and help the user learn better and faster. This method has the potential of being very efficient in real-time applications.

Enhanced stochastic fractal search algorithm with chaos

Abstract: This study presents modifications to a metaheuristic algorithm inspired by natural phenomenon of growth with its performance assessment in comparison to its original predecessor algorithm on various standard classical benchmark functions. The modified algorithm aims to improve the Stochastic Fractal Search (SFS) algorithm in terms of convergence speed and fitness accuracy. The performance of SFS is affected by a constant p that is used to decrease the size of Gaussian jumps and then encourage a more localized search for individuals. Five different chaotic maps have been selected in this study. The influence of these chaotic maps on convergence rate and solution accuracy is investigated using several classical standard benchmark functions. Overall results show that SFS algorithm with Gauss/Mouse map results in significant improvement in comparison to its original version.

A New Bats Echolocation-based Algorithm for Single Objective Optimisation

Abstract: Bats sonar algorithm (BSA) as a swarm intelligence approach utilises the concept of echolocation of bats to find prey. However, the algorithm is unable to achieve good precision and fast convergence rate to the optimum solution. With this in mind, an adaptive bats sonar algorithm is introduced with new paradigms of real bats echolocation behaviour. The performance of the algorithm is validated through rigorous tests with several single objective optimisation benchmark test functions. The obtained results show that the proposed scheme outperforms the BSA in terms of accuracy and convergence speed and can be efficiently employed to solve engineering problems.

Fuzzy logic feedforward active noise control with distance ratio and acoustic feedback using Takagi–Sugeon–Kang inference

Abstract: Noise, as undesired sound, severely affects the quality of human life. Currently, active noise control method has demonstrated its capability in low-frequency noise cancellation and the advance in ...

A Modified Bats Echolocation-Based Algorithm for Solving Constrained Optimisation Problems

Abstract: A modified adaptive bats sonar algorithm (MABSA) is presented that utilises the concept of echolocation of a colony of bats to find prey. The proposed algorithm is applied to solve the constrained optimisation problems coupled with penalty function method as constraint handling technique. The performance of the algorithm is verified through rigorous tests with four constrained optimisation benchmark test functions. The acquired results show that the proposed algorithm performs better to find optimum solution in terms of accuracy and convergence speed. The statistical results of MABSA to solve all the test functions also has been compared with the results from several existing algorithms taken from literature on similar test functions. The comparative study has shown that MABSA outperforms other establish algorithms, and thus, it can be an efficient alternative method in the solving constrained optimisation problems.

Myoelectric Forearm Prostheses: State of the Art from a User-Centered Perspective

Abstract: User acceptance of myoelectric forearm prostheses is currently low. Awkward control, lack of feedback, and difficult training are cited as primary reasons. Recently, researchers have focused on exploiting the new possibilities offered by advancements in prosthetic technology. Alternatively, researchers could focus on prosthesis acceptance by developing functional requirements based on activities users are likely to perform. In this article, we describe the process of determining such requirements and then the application of these requirements to evaluating the state of the art in myoelectric forearm prosthesis research. As part of a needs assessment, a workshop was organized involving clinicians (representing end users), academics, and engineers. The resulting needs included an increased number of functions, lower reaction and execution times, and intuitiveness of both control and feedback systems. Reviewing the state of the art of research in the main prosthetic subsystems (electromyographic [EMG] sensing, control, and feedback) showed that modern research prototypes only partly fulfill the requirements. We found that focus should be on validating EMG-sensing results with patients, improving simultaneous control of wrist movements and grasps, deriving optimal parameters for force and position feedback, and taking into account the psychophysical aspects of feedback, such as intensity perception and spatial acuity.

A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control

Abstract: This paper presents a heuristic fuzzy logic approach to multiple electromyogram (EMG) pattern recognition for multifunctional prosthesis control. Basic signal statistics (mean and standard deviation) are used for membership function construction, and fuzzy c-means (FCMs) data clustering is used to automate the construction of a simple amplitude-driven inference rule base. The result is a system that is transparent to, and easily "tweaked" by, the prosthetist/clinician. Other algorithms in current literature assume a longer period of unperceivable delay, while the system we present has an update rate of 45.7 ms with little postprocessing time, making it suitable for real-time application. Five subjects were investigated (three with intact limbs, one with a unilateral transradial amputation, and one with a unilateral transradial limb-deficiency from birth). Four subjects were used for system offline analysis, and the remaining intact-limbed subject was used for system real-time analysis. We discriminated between four EMG patterns for subjects with intact limbs, and between three patterns for limb-deficient subjects. Overall classification rates ranged from 94% to 99%. The fuzzy algorithm also demonstrated success in real-time classification, both during steady state motions and motion state transitioning. This functionality allows for seamless control of multiple degrees-of-freedom in a multifunctional prosthesis.

Command shaping for flexible systems: A review of the first 50 years

Abstract: The control of flexible systems is a large and important field of study. Unwanted transient deflection and residual vibration are detrimental to many systems ranging from nano-positioning devices to large industrial cranes. Thousands of researchers have worked diligently for decades to provide solutions to the challenging problems posed by flexible dynamic systems. The work can roughly be broken into three categories:1) Hardware design, 2) Feedback control, and 3) Command shaping. This paper provides a review of command-shaping research since it was first proposed in the late 1950’s. The important milestones of the research advancements, as well as application examples, are used to illustrate the developments in this important research field.

A Supervised Feature-Projection-Based Real-Time EMG Pattern Recognition for Multifunction Myoelectric Hand Control

Abstract: Electromyographic (EMG) pattern recognition is essential for the control of a multifunction myoelectric hand. The main goal of this study was to develop an efficient feature- projection method for EMG pattern recognition. To this end, a linear supervised feature projection is proposed that utilizes a linear discriminant analysis (LDA). First, a wavelet packet transform (WPT) is performed to extract a feature vector from four-channel EMG signals. To dimensionally reduce and cluster the WPT features, an LDA, then, incorporates class information into the learning procedure, and identifies a linear matrix to maximize the class separability for the projected features. Finally, a multilayer perceptron classifies the LDA-reduced features into nine hand motions. To evaluate the performance of the LDA for WPT features, the LDA is compared with three other feature-projection methods. From a visualization and quantitative comparison, it is shown that the LDA produces a better performance for the class separability, plus the LDA-projected features improve the classification accuracy with a short processing time. A real-time pattern-recognition system is then implemented for a multifunction myoelectric hand. Experiments show that the proposed method achieves a 97.4% recognition accuracy, and all processes, including the generation of control commands for the myoelectric hand, are completed within 97 ms. Consequently, these results confirm that the proposed method is applicable to real-time EMG pattern recognition for multifunction myoelectric hand control.