Estimation of Torque Based on EMG using ANFIS

© 2017 The Authors. There are wide verities of possible human movements that involve a range from the gait for the lifting of a load by a factory worker to the performance of a superior athlete. Output of the movement can be described by a large number of kinematic variables like knee joint angle, torque. This paper proposes a system that contains a non-parametric model with EMG signal of two muscles is used as input to estimate torque. The mapping of EMG to any joint dynamics is very subject dependent. It also depends on walking, running, jumping or climbing. Each type of posture consists of combination of isometric, eccentric and concentric type of muscle contraction with different intensity level depending on velocity, angle and lifted weight (muscle activation level). To capture the EMG signal pattern which is complex and so dynamic in time and space, an adaptive feature in computational intelligence is desired which will not only learn but also make decision based on EMG channel signal pattern to estimate torque. The EMG signal has been collected from volunteer who has completed the knee joint extension with maximum voluntary contraction (MVC) at different degree/sec ranging from 5deg/Sec to 360deg/Sec. The volunteer was also asked to perform extension with moderate and low effort against different impedance like 5deg/Sec, 20deg/Sec, and 45deg/Sec. RMS feature along with 2nd order digital filter has been used to smooth the raw EMG signal. The proposed study is intended to explore an ANFIS like Neuro-Fuzzy type knowledge based adaptive network with embedded RBF kernel neuron to estimate torque

The 1st International Conference on Computational Engineering and Intelligent Systems

Computational engineering, artificial intelligence and smart systems constitute a hot multidisciplinary topic contrasting computer science, engineering and applied mathematics that created a variety of fascinating intelligent systems. Computational engineering encloses fundamental engineering and science blended with the advanced knowledge of mathematics, algorithms and computer languages. It is concerned with the modeling and simulation of complex systems and data processing methods. Computing and artificial intelligence lead to smart systems that are advanced machines designed to fulfill certain specifications. This proceedings book is a collection of papers presented at the first International Conference on Computational Engineering and Intelligent Systems (ICCEIS2021), held online in the period December 10-12, 2021. The collection offers a wide scope of engineering topics, including smart grids, intelligent control, artificial intelligence, optimization, microelectronics and telecommunication systems. The contributions included in this book are of high quality, present details concerning the topics in a succinct way, and can be used as excellent reference and support for readers regarding the field of computational engineering, artificial intelligence and smart system

Analysis and control of FES-assisted paraplegic walking with wheel walker.

The number of people with spinal cord injury (SCI) is increasing every year and walking has been found to be the most exciting and important prospect to these patients to improve their quality of life. Many individuals with incomplete SCI have the potential to walk and everyone of them wants to try. Unfortunately up to now, there is less than one third of patients could walk again after SCI. Residual function, the orthotic support, energy expenditure, patient motivation and control technique are some of the factors that influence the walking outcome of spinal cord injured people. In this thesis, a series of studies are carried out to investigate the possibility of enhancing the performance of the functional electrical stimulation (PES) assisted paraplegic walking with wheel walker through the development and implementation of intelligent control technique and spring brake orthosis (SBO) with full utilization of the voluntary upper body effort. The main aim of this thesis is to enable individuals with complete paraplegia to walk again with maximum performance and the simplest approach as possible. Firstly, before simulation of the system can be made, it is important to select the right model to represent the actual plant. In this thesis, the development of a humanoid and wheel walker models are carried out using MSC.visualNastran4D (vN4D) software and this is integrated with Matlab Simulink® for simulation. The newly developed quadriceps and hamstrings muscle models from the series of experiments are used to represent subject muscles after comparison and validation with other two well-known muscle models are performed. Several experiments are conducted to investigate the effect of stimulation frequency and pulse-width in intermittent stimulation with isometric measurement from paraplegic subjects. The results from this work can serve as a guidance to determine the optimum stimulation parameters such as frequency and pulse-width to reduce muscle fatigue during PES application. The ability test is introduced to determine the maximum leg force that can be applied to the specific paraplegic subject during FES functional task with minimum chance of spasm and leg injury. Investigations are carried out on the control techniques implemented for FES walking with wheel walker. PID control and fuzzy logic control (FLC) are used to regulate the electrical stimulation required by the quadriceps and hamstrings muscles in order to perform the FES walking manoeuvre according to predefined walking trajectory. The body weight transfer is introduced to increase the efficiency of FES walking performance. The effectiveness of body weight transfer and control strategy to enhance the performance of FES walking and reduce stimulation pulses required is examined. Investigations are carried out on the effectiveness of spring brake orthosis (SBO) for FES assisted paraplegic walking with wheel walker. A new concept in hybrid orthotics provides solutions to the problems that affect current 'hybrid orthosis, including knee and hip flexion without relying on the withdrawal reflex or a powered actuator and foot-ground clearance without extra upper body effort. The use of SBO can also eliminate electrical stimulation pulses required by the hamstrings muscle for the same FES walking system. Further improvement of the FES walking system is achieved by introducing finite state control (FSC) to control the switching time between springs, brakes and electrical stimulation during FES assisted walking with wheel walker with the combInation of FLC to regulate the electrical stimulation required for the knee extension. The results show that FSC can be used to accurately control the switching time and improve the system robustness and stability

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

Real-time Knowledge-based Fuzzy Logic Model for Soft Tissue Deformation

In this research, the improved mass spring model is presented to simulate the human liver deformation. The underlying MSM is redesigned where fuzzy knowledge-based approaches are implemented to determine the stiffness values. Results show that fuzzy approaches are in very good agreement to the benchmark model. The novelty of this research is that for liver deformation in particular, no specific contributions in the literature exist reporting on real-time knowledge-based fuzzy MSM for liver deformation

Proceeding Of Mechanical Engineering Research Day 2015 (MERD’15)

This Open Access e-Proceeding contains 74 selected papers from the Mechanical Engineering Research Day 2015 (MERD’15) event, which is held in Kampus Teknologi, Universiti Teknikal Malaysia Melaka (UTeM) - Melaka, Malaysia, on 31 March 2015. The theme chosen for this event is ‘Pioneering Future Discovery’. The response for MERD’15 is overwhelming as the technical committees have received more than 90 papers from various areas of mechanical engineering. From the total number of submissions, the technical committees have selected 74 papers to be included in this proceeding. The selected papers are grouped into 12 categories: Advanced Materials Processing; Automotive Engineering; Computational Modeling and Analysis & CAD/CAE; Energy Management & Fuels and Lubricants; Hydraulics and Pneumatics & Mechanical Control; Mechanical Design and Optimization; Noise, Vibration and Harshness; Non-Destructive Testing & Structural Mechanics; Surface Engineering and Coatings; Others Related Topic. With the large number of submissions from the researchers in other faculties, the event has achieved its main objective which is to bring together educators, researchers and practitioners to share their findings and perhaps sustaining the research culture in the university. The topics of MERD’15 are based on a combination of advanced research methodologies, application technologies and review approaches. As the editor-in-chief, we would like to express our gratitude to the editorial board members for their tireless effort in compiling and reviewing the selected papers for this proceeding. We would also like to extend our great appreciation to the members of the Publication Committee and Secretariat for their excellent cooperation in preparing the proceedings of MERD’15

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018

Development of Novel Compound Controllers to Reduce Chattering of Sliding Mode Control

The robotics and dynamic systems constantly encountered with disturbances such as micro electro mechanical systems (MEMS) gyroscope under disturbances result in mechanical coupling terms between two axes, friction forces in exoskeleton robot joints, and unmodelled dynamics of robot manipulator. Sliding mode control (SMC) is a robust controller. The main drawback of the sliding mode controller is that it produces high-frequency control signals, which leads to chattering. The research objective is to reduce chattering, improve robustness, and increase trajectory tracking of SMC. In this research, we developed controllers for three different dynamic systems: (i) MEMS, (ii) an Exoskeleton type robot, and (iii) a 2 DOF robot manipulator. We proposed three sliding mode control methods such as robust sliding mode control (RSMC), new sliding mode control (NSMC), and fractional sliding mode control (FSMC). These controllers were applied on MEMS gyroscope, Exoskeleton robot, and robot manipulator. The performance of the three proposed sliding mode controllers was compared with conventional sliding mode control (CSMC). The simulation results verified that FSMC exhibits better performance in chattering reduction, faster convergence, finite-time convergence, robustness, and trajectory tracking compared to RSMC, CSMC, and NSFC. Also, the tracking performance of NSMC was compared with CSMC experimentally, which demonstrated better performance of the NSMC controller

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research