Analysis of EMG signals during stance and swing phases for controlling Magnetorheological Brake applications

The development of ankle foot orthoses (AFO) for lower limb rehabilitation have received significant attention over the past decades. Recently, passive AFO equipped with magnetorheological brake had been developed based on ankle angle and electromyography (EMG) signals. Nonetheless, the EMG signals were categorized in stance and swing phases through visual observation as the signals are stochastic. Therefore, this study aims to classify the pattern of EMG signals during stance and swing phases. Seven-time domains features will be extracted and fed into artificial neural network (ANN) as a classifier. Two different training algorithms of ANN namely Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) will be applied. As number of inputs will affect the classification performance of ANN, different number of input features will be employed. In this study, three participants were recruited and walk on the treadmills for 60 seconds by constant the speed. The ANN model was designed with 2, 10, 12, and 14 inputs features with LM and SCG training algorithms. Then, the ANN was trained ten times and the performances of each inputs features were measured using classification rate of training, testing, validation and overall. This study found that all the inputs with LM training algorithm gained more than 2% average classification rate than SCG training algorithm. On the other hand, classification accuracy of 10, 12 and 14 inputs were 5% higher than 2 inputs. It can be concluded that LM training algorithm of ANN was performed better than SCG algorithm with at least 10 inputs

Effect of Magnetorheological Grease’s Viscosity to the Torque Performance in Magnetorheological Brake

Recently, magnetorheological grease (MRG) has been utilized in magnetorheological (MR) brakes to generate a braking torque based on the current applied. However, the high initial viscosity of MRG has increased the off-state torque that led to the viscous drag of the brake. Therefore, in this study, the off-state viscosity of MRG can be reduced by the introduction of dilution oil as an additive. Three samples consist of pure MRG (MRG 1) and MRG with different types of dilution oil; hydraulic (MRG 2) and kerosene (MRG 3) were prepared by mixing grease and spherical carbonyl iron particles (CIP) using a mechanical stirrer. The rheological properties in the rotational mode were examined using a rheometer and the torque performances in MR brake were evaluated by changing the current of 0 A, 0.4 A, 0.8 A, and 1.2 A with fixed angular speed. The result shows that MRG 3 has the lowest viscosity which is almost 93% reduction while the viscosity of MRG 2 has lowered to 25%. However, the torque performances generated by MRG 3 were highest, 1.44 Nm, when 1.2 A of current was applied and followed by MRG 2 and MRG 1. This phenomenon indicated that the improvement of torque performances was dependent on the viscosity of MRG. By reducing the viscosity of MRG, the restriction on CIP to form chain formation has also decreased and strengthen the torque of MRG brake. Consequently, the utilization of dilution oil in MRG could be considered in MR brake in near future

Material characterizations of gr-based magnetorheological elastomer for possible sensor applications: rheological and resistivity properties

Considering persistent years, many researchers continuously seek an optimum way to utilize the idea of magnetorheology (MR) materials to be practically used for everyday life, particularly concerning resistivity sensing application. The rheology and resistivity of a graphite (Gr)-based magnetorheological elastomer (Gr-MRE) were experimentally evaluated in the present research. Magnetorheological elastomer (MRE) samples were prepared by adding Gr as a new additive during MRE fabrication. The effect of additional Gr on the rheological and resistivity properties were investigated and compared with those of typical MREs without a Gr additive. Morphological aspects of Gr-MRE were characterized using field emission scanning electron microscopy (FESEM) and energy dispersive X-ray spectroscopy (EDX). Rheological properties under different magnetic fields were evaluated using a parallel-plate rheometer. Subsequently, the resistivity of all samples was measured under different applied forces and magnetic fields. From the resistivity evaluation, two relationship curves resistance (R) under different applied forces (F) and different magnetic fields (B) were established and plotted by using an empirical model. It was observed from the FESEM images that the presence of Gr fractions arrangement contributes to the conductivity of MRE. It was also observed that, with the addition of Gr, rheological properties such as the field-dependent modulus can be improved, particularly at low strain amplitudes. It is also demonstrated that the addition of Gr in MRE can contribute to the likely use of force detection in tactile sensing devices

Improving oil palm fresh fruit bunch grading system via software and hardware modifications

An improved technique is proposed on how to increase the quality of oil palm ripeness grading in the Real Time Fresh Fruit Bunch (FFB) Oil Palm Grading System. This technique improvised the existing prototype grading system into a higher level “towards commercialization” grading machine. The improved grading machine changed the hardware design and system. Previously, the grading system used two software platforms which were MATLAB and LabVIEW and this was time consuming problem. This problem is due to the size of the image captured which is 1Gb per image. Therefore, the algorithm was migrated to a standalone software using LabVIEW. In this improved implementation, correctly human graded samples of oil palm bunches were image captured and analyzed for two categories. As a result, two sets of low resolved intensity images are captured by the Charged Coupled Device (CCD) camera. The grading system involves the hardware component which is the CCD camera and the software algorithm that is, the LabVIEW software for imaging purposes. The image analysis uses Artificial Neural Network (ANN) technique which includes training and testing of data. Model for the ANN is created based on the training data which is stored in the software memory. The ANN model is then used in the testing process where the software decides the grade of the oil palm fruit bunch. A significant improvement in the design specifications is made between the prototype and the new grading machine, which include weight measurement, sorting process, grip belting and feeder system. In the new machine, the speed for grading 60 bunches per minute is obtained compared to the existing system which is 10 bunches per minute. The design specification shows that the machine completes this process in one minute 33 seconds

A study on the oil palm fresh fruit bunch (FFB) ripeness detection by using Hue, Saturation and Intensity (HSI) approach

To increase the quality of palm oil means to accurately grade the oil palm fresh fruit bunches (FFB) for processing. In this paper, HSI color model was used to determine the relationship between FFB ' s color with the underipe and ripe category so that the grading system could be accurately done. From the analysis manipulation, a formula was generated and applied to the data obtained. The by linear regression in the data shows an average success rate at 45% accuracy for oil palm ripeness detection. Artificial Neural Network (ANN) however return a better accuracy result for both underipe and ripe categories which are 60% and 80% respectively. This yield an overall accuracy of 70%. This can be increased more by improving the grading system

Control reference parameter for stance assistance using a passive controlled ankle foot Orthosis - a preliminary study

This paper aims to present a preliminary study of control reference parameters for stance assistance among different subjects and walking speeds using a passive-controlled ankle foot orthosis. Four young male able-bodied subjects with varying body mass indexes (23.842 ± 4.827) walked in three walking speeds of 1, 3, and 5 km/h. Two control references, average ankle torque (aMa), and ankle angular velocity (aω), which can be implemented using a magnetorheological brake, were measured. Regression analysis was conducted to identify suitable control references in the three different phases of the stance. The results showed that aω has greater correlation (p) with body mass index and walking speed compared to aMa in the whole stance phase (p1(aω) = 0.666 > p1(aMa) = 0.560, p2(aω) = 0.837 > p2(aMa) = 0.277, and p3(aω) = 0.839 > p3(aMa) = 0.369). The estimation standard error (Se) of the aMa was found to be generally higher than of aω (Se1(aMa) = 2.251 > Se1(aω) = 0.786, Se2(aMa) = 1.236 > Se2(aω) = 0.231, Se3(aMa) = 0.696 < Se3(aω) = 0.755). Future studies should perform aω estimation based on body mass index and walking speed, as suggested by the higher correlation and lower standard error as compared to aMa. The number of subjects and walking speed scenarios should also be increased to reduce the standard error of control reference parameters estimation

Rheological properties of Mg substituted cobalt nickel ferrite nanoparticles as an additive in magnetorheological elastomer

Additive has been used widely in magnetorheological elastomer (MRE) fabrication in order to enhance the magnetic, electrical and rheological properties. In this study, the ferrite nanoparticles namely Magnesium (Mg) substituted Cobalt Nickel Ferrite is introduced as an additive in order to enhance the magnetic and rheological properties of MRE. The conventional co-precipitation method is used to synthesize the Mg substituted Cobalt Nickel Ferrite nanoparticles. The 1 wt% of spherical Mg substituted Cobalt Nickel Ferrite nanoparticles with a range size of 50 nm are then sonicated using ultrasonication before mixing with 70 wt% carbonyl iron particles (CIPs) and silicon-rubber (SR) as a matrix. Two prepared samples of MRE with and without Mg substituted Cobalt Nickel Ferrite nanoparticles are characterized using Vibrating Sample Magnetometer (VSM). Meanwhile, the rheological properties related to the frequency and magnetorheological (MR) effect in off- and on-state condition are determined by using rotational rheometer. The result depicted that the magnetic saturation of MRE with Mg substituted Cobalt Nickel Ferrite nanoparticles increased by 2%. Meanwhile, the maximum storage modulus of MRE with Mg substituted Cobalt Nickel Ferrite nanoparticles enhanced up to 13% as compared to conventional MRE. In the case of energy dissipation, the MRE + S1, exhibit higher energy dissipation as compared to conventional MRE. In the meantime, the relative MR effect of MRE with 1 wt% Mg substituted Cobalt Nickel Ferrite nanoparticles can reach up to 215%, as compared to conventional MRE. The enhancement of magnetic and rheological properties of MRE with Mg substituted Cobalt Nickel Ferrite nanoparticles suggest that the nanoparticles additive fill the void and improved the interaction between CIPs resulted in increment of storage modulus

Effects of magnetic field and particles content on rheology and resistivity behavior of magnetorheological elastomer with embedded cobalt particles

In most studies, carbonyl iron particles were merged with carbon-based particles such as graphite or carbon black particles as fillers to enhance the electrical properties of magnetorheological elastomers (MREs). These combined fillers can be replaced by using a single material in order to reduce brittleness due to the high amount of composition. Therefore, in this research, cobalt particles having dual properties, magnetic and electrical, have been employed as a filler in MRE. A total of three anisotropic MREs containing 53, 60, and 67 wt% of cobalt were fabricated through the mixing process method. The characterization related to morphological and magnetic properties of MRE samples was analyzed by using field emission scanning electron microscopy and vibrating sample magnetometer. Then, the rheological properties of the MRE in various strengths of magnetic field intensity between 0 and 0.8 T were conducted by using a rheometer. Afterward, the effect of the cobalt on the electrical properties was investigated and compared with different applied forces towards the MRE. The outcome showed that the rheological and electrical properties of MRE were improved with the increase of cobalt content embedded in the silicone matrix. Higher cobalt content in MRE contributed to a higher magnetorheological (MR) effect and simultaneously lower the electrical resistivity. Therefore, the addition of cobalt particles as a filler in MRE is a great potential to be applied as sensors

The elastic, mechanical and optical properties of bismuth modified borate glass: experimental and artificial neural network simulation

The introduction of artificial neural networks (ANNs) in the glass field has greatly improved this industry to further increase fabrication productivity. ANNs are the systems that help the glass expert to estimate a few parameters such as density, molar volume, ultrasonic velocity, elastic moduli and optical band gap in the glass composition. The greatness of this system was implemented in a series of bismuth-borate (Bi2O3-B2O3) glasses which have been successfully produced using melting and quenching methods with the configuration of mBi2O3- (100-m)B2O3 where m = 0, 40, 45, 50, 55, 60 mol%. In this present works, the experimental values resulting from the composition of this glass series were compared with the values obtained from the estimation by ANNs. This study has concluded that the ANNs system is relevant to be used in the fields of glass industry since the coefficient of R2 values showed by the density, molar volume, ultrasonic velocity, elastic moduli and optical band gap graph is between 0.998 and 1.0000 which believed highly desirable