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The damping characteristic of a healthy limb changes throughout the gait cycle. However, for amputees who are wearing mechanically passive damping prosthesis, the lack of ability to change the damping values might expose them to injuries and health problems. The use of magnetorheological fluid damper in prosthetic limb, which provides wide dynamic...
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... the case of PID controller, the input is at a single frequency. To exhibit the performance of the controller at varying frequency, chirp signal was used as the input in Simulink. From Figure 14, it is shown that PID controller is not capable of controlling the vibration at varying frequency. Thus, fuzzy logic was integrated to the PID controller, to further enhance the performance of the controller. The F-PID works by adjusting the value of k p , k i and k d required by the system. Initially, the equation describing the PID controller was expressed ...
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... general, the control system block diagram of MRF damper in transtibial prosthetic limb is shown in Figure 10. In this section, only system controller was studied. To represent the process represented by 'Damper controller' and 'MRF damper' blocks, a look-up table is used in the simulation. The look-up table contains experimental data, which was done ...
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... controller was first used to run the system and its control system block diagram is pictured in Figure 11. Here, the values of K p , K i and K d were set to 5, 15 and 2, respectively. The input applied was in the form of a pulse. The PID controller will determine the amount of force needed to reduce the effect of the force experienced during an impact. The required damping force from the system will be passed to the MRF damper in transtibial pros- thetic limb plant, which will then decide the amount of current to be used for the required damping force. In this work, the data obtained through the experiment were added in a look-up table. The response of the system is shown in Figure 12, along with the plots of damping force generated, shown in Figure ...
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... controller was first used to run the system and its control system block diagram is pictured in Figure 11. Here, the values of K p , K i and K d were set to 5, 15 and 2, respectively. The input applied was in the form of a pulse. The PID controller will determine the amount of force needed to reduce the effect of the force experienced during an impact. The required damping force from the system will be passed to the MRF damper in transtibial pros- thetic limb plant, which will then decide the amount of current to be used for the required damping force. In this work, the data obtained through the experiment were added in a look-up table. The response of the system is shown in Figure 12, along with the plots of damping force generated, shown in Figure ...
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... controller was first used to run the system and its control system block diagram is pictured in Figure 11. Here, the values of K p , K i and K d were set to 5, 15 and 2, respectively. The input applied was in the form of a pulse. The PID controller will determine the amount of force needed to reduce the effect of the force experienced during an impact. The required damping force from the system will be passed to the MRF damper in transtibial pros- thetic limb plant, which will then decide the amount of current to be used for the required damping force. In this work, the data obtained through the experiment were added in a look-up table. The response of the system is shown in Figure 12, along with the plots of damping force generated, shown in Figure ...
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... controlled outputs of the system, using PID and F-PID controllers are plotted in Figure 14. It is clear that the implementation of F-PID controller manages to suppress the vibration and thus protecting the whole struc- ture of the prosthetic limb as well as the ...
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... t (s) Figure 14. Comparison of the response of the system using PID and F-PID controllers. ...
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... gait cycle comprises of two main phases which are stance phase and swing phase. Stance phase, which occurs around 60% of the normal human walking cycle, is the moment at which the foot is in contact with the ground whereas swing phase refers to the period when the foot is in the air. The gait cycle can be further classified into eight phases based on the knee motion and angular velocity. 27 These phases are initial contact (heel strike), loading response, mid-stance, terminal stance, pre-swing, initial swing (toe-off), mid swing and terminal swing (Figure ...
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Citations
... In recent decades, MR dampers (MRD) have become very popular in controlling vibration in structural and rotordynamics systems. In the last 20 years, MRD has been used to attenuate vibration in various applications like suspension systems in the high-speed train [1,2], buildings and bridges [3][4][5], large washing machines [6][7][8], aircraft landing gear [9,10], helicopter rotor systems [11], and advanced exoskeleton system [12,13], etc. The MRF consists of base oil (silicon oil, mineral oil, synthetic oil), magnetic particles (electrolytic iron, carbonyl iron, nickel, cobalt), additives (aerosol 200, white grease, oleic acid), and surfactants (oleic acid, citric acid). ...
The magnetorheological damper (MRD) is a smart damper that can produce a wide range of damping forces with respect to the diferent current supply. The said damper contains magnetorheological fuid (MRF), whose property helped to achieve controlled viscous force that enables to attenuate the system’s vibration. This paper analyzes the weight reduction and damping force variations by changing MRD’s piston confguration and annular gap. The annular gap is varied from 0.5 to 1 mm with a step size of 0.25 mm, and the piston has a chamfer of 2 mm at diferent locations. Six diferent MRD models are designed with diferent confgurations named MRD1 to MRD6, respectively. The magnetic fux density is also calculated through the magnetic reluctance circuit of the MRD model. The damping force is estimated by considering the magnetic fux density at diferent magnitudes of current in the annular gap of the damper model. The MRD4 having both sides chamfer on the piston of the pole length zone shows a lesser weight by 1.5% than the simple MRD model. The MRD4 model obtains a signifcant amount of magnetic force, which is 22% more than the MRD1. The analytical results were compared with the simulated result, and it found the magnetic fux density was altered by 3.5%. This study may be benefcial for the selection of the best MRD model with respect to maximum magnetic force and reduced weight.
... This event was soon followed by significant developments such as the first hydraulic knee in 1947, SACH (solid ankle cushion heel) introduced by Anthony Staros in 1957, a computer-aided robotic arm in 1980, and a motor-powered lower limb prosthetic in 2012 [7]. Implementation of unique properties of MR (magnetorheological) effect as a brake for lower limb prosthetics [8], and by 2020, Nathan Brown et al., conducted research to improve the comfort of amputees by proposing the use of metamaterials as a liner in socket for a transtibial prosthetic [9]. In ancient Egypt, wooden prosthetic toes and feet were crafted for amputees. ...
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... As a healthy subject step through a gait cycle, the damping force varies based on the flexion angles and other forces (Nordin et al., 2018). The MR fluid is a type of innovative material that is capable of altering its rheological property. ...
Abstract
Purpose – This paper aims to design and analyze a controlled magnetorheological damper-based ankle-foot prosthesis prototype.
Design/methodology/approach – The ankle-foot prostheses prototype is proposed using the lightweight three dimensional (3 D)-printed parts,
MR damper and digital servomotor. Initially, the computer-aided design (CAD) model of the prosthetic foot, leaf spring, retention spring and the
various connecting parts required to connect the pylon and damper actuator assemblies are designed using CAD software. Later, the fused
deposition modeling 3 D printer-based technique prints a prosthetic foot and other connecting parts using Acrylonitrile Butadiene Styrene filament.
The prototype consists of two control parts: the first part controls the MR actuator that absorbs the impacts during walking. The second part is the
control of the electric actuator intended to generate the dorsiflexion and plantar flexion movements. Finally, the prototype is tested on a transtibial
amputee under the supervision of a prosthetist.
Findings – The ANalysis SYStems software-based analysis has shown that the prosthetic foot has a factor of safety values between 4.7 and 8.7 for
heel strike, mid-swing and toe-off; hence, it is safe from mechanical failure. The designed MR damper-based ankle-foot prosthesis prototype is
tested on an amputee for a level-ground walk; he felt comfortable compared to his passive prosthesis.
Originality/value – The design of an MR damper-based prosthesis prototype offers a better dynamic range for locomotion than passive prostheses.
It reduces the injuries and provides relief to the transtibial amputees.
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... For this reason, MRFs have been incorporated in the implementation of semi-active dampers for vehicle suspensions. Magnetorheological (MR) dampers are semiactive control devices that are widely used in various applications such as transtibial prosthetic limb [7][8], tremor attenuation [9], seismic structural control [10], automotive suspension application [11] and landing gear [12]. Filled with MR fluid, MR dampers have gained interest due to their controllability, response time, low power consumption and fault-safe characteristics [13]. ...
The nonlinearity behaviour of magnetorheological fluid (MRF) can be described using a number of established models such as Bingham and Modified Bouc-Wen models. Since these models require the identification of model parameters, there is a need to estimate the parameters’ value carefully. In this paper, an optimization algorithm, i.e., the Particle Swarm Optimization (PSO) algorithm, is utilized to identify the models’ parameters. The PSO algorithm distinctively controls the best fit value by minimizing marginal error through root-mean-square error between the models and the empirical response. The validation of the algorithm is attained by comparing the resulting modified Bouc-Wen model behaviour using PSO against the same model’s behaviour, identified using Genetic Algorithm (GA). The validation results indicate that the application of PSO is better in identifying the model parameters. Results from this estimation can be used to design a controller for various applications such as prosthetic limbs.
... Herr and Wilkenfeld [18] designed an MR brake using a finite state controller with three states in the stance and two states in the swing phase. Nordin et al. [19] designed a fuzzy PID controller and used a look up table based on damper characterization data to supply input current to the damper. This may require a large repository of experimental data sets of the damper under study to effectively supply the input current to the damper. ...
... An inverse model is necessary to calculate the driving current to be supplied to the damper which generates the required amount of force. Substituting Eq. (9) in Eq. (8) and eliminating the stiffness K since it is set to zero and after suitable rearrangement, the driving current input can be expressed as, (17) By estimating the parameter z as Z u sgn(̇x) , where Z u represents the ultimate hysteretic strength [7,38], the input current can be estimated by modifying Eq. (19) as, Since the MR damper cannot supply positive power and can only dissipate energy, the passivity constraints given by Eqs. (21), and the limitation constraints given by Eqs. ...
Improving the gait of transfemoral amputees and making it biomimetic and stable has always been a major effort. A dynamic model of the prosthetic device can predict the kinetic and kinematic performances, when incorporated with a musculoskeletal model. In this regard, a dynamic model of a recent trend of variable damping technology will help a great deal in evaluating the performance of the prosthetic device and also in studying the effect of various parameters on the prostheses. The current paper presents the dynamic model of a single axis two segmental prosthetic knee implemented with a magneto-rheological (MR) damper as a variable damping element. The MR damper is modeled mathematically using Bouc–Wen model with model parameters evaluated by minimizing the error norms for time, displacement and velocity between the experimental and the model-generated results using a genetic algorithm. Two different experimental data sets are used, one for mathematical modeling and other to assess the accuracy of the fit model. A Proportional Derivative plus Controlled Torque controller is employed, and the parameters are tuned to minimize the error between the desired and control input torques. Further, an inverse dynamic model using Bouc–Wen model variables is assumed and validated later. This model predicts the current directly and avoids the necessity of solving any quadratic equation, which is required in the case of inverse models based on modified Bouc–Wen. The dynamic model of the prosthesis is analyzed for the swing phase alone, and the results show that the model traces the desired knee angle and also the shank reaches full knee extension at the end of this phase with terminal velocity small enough to be handled by an extension stop.
... The other is voltage regulator based on the maximum and minimum capacities of the MR damper at each time-step (Askari et al., 2016). An adaptive Fuzzy-PID controller was implemented to ensure that the MR damper performs well, even at varying frequencies (Nordin et al., 2018). ...
The magnetorheological (MR) damping material is a kind of smart shock absorption material, and it can be made into MR dampers for reducing the vibration or dynamic response of structures. During the vibration mitigation control of structures with MR dampers, the displacement and acceleration responses are always concerned firstly because the displacement responses determine the safety of structures and, at the same time, the acceleration responses determine the comfort level of the human body staying in structures. That means, the control currents choice of MR dampers during the vibration process is a multi-objective optimization control problem. In this paper, the particle swarm optimization (PSO) algorithm is developed to control the displacement and acceleration responses simultaneously. Numerical analysis for a five-floor steel frame structure with one MR damper installed on each floor is carried out. Simulation results of the PSO control structure are compared with those of passive control (including the ON-control and the OFF-control) structures and the uncontrolled structure. Analysis results demonstrate that the PSO algorithm can reduce the displacement responses of the structure obviously and, at the same time, it can reduce the acceleration responses of the structure to a certain extent. Furthermore, the PSO algorithm reduces the seismic responses of structures more effectively than those of passive control structures.
Background
Magnetorheological (MR) fluid has recently been used in a variety of applications, including vibration control, prostheses, and tactile devices. A magnetic field is used to control the rheological properties of the MR fluid (MRF), allowing for a high pressure drop, high shear stress, and reversibility of these features. The modifiability imparts smartness to the material, and recent studies have investigated their behavior extensively.PurposeThe present work involves the design of an MR fluid-based actuator to function as an ankle in a prosthetic foot.Methods
The initial design constraint of the MR valve and stroke length of the MR damper have been identified based on anthropometric constraints and biomechanical requirements of below-knee amputees (BKA). Second, magnetostatic analysis has been performed to solve an approximated parametric magnetic model (PMM) based on linear electromagnetic systems. Finally, finite element magnetostatic (FEMS) analysis was conducted to understand the magnetic flux density (MFD) of the MR fluid and the pressure drop was modeled and obtained using the Bingham model. Next, optimization of the damper was done. For this, the optimization goal functions were to (a) maximize damping force and (b) minimize the mass of the damper valve (by minimizing the volume of the damper and MR fluid), hence minimizing the weight of the prosthetic ankle. The geometric dimensions of the MR valve are optimized using an integration of a multi-objective genetic algorithm (MOGA) in MATLAB and FEMS in ANSYS APDL software. MOGA chooses values for design variables that are constrained within the specified bounds, and they are coupled by the FEMS analysis; this routine is automated and repeated for all possible values within the specified bounds.ResultsIt was observed that the results of MFD computed from PMM are valid for low ranges of current; however, it over-estimates MFD beyond 0.5 A. Furthermore, optimal solutions obtained are plotted on a Pareto front, which satisfies the objective function of maximizing the damping force and minimizing the weight of the damper. The optimization findings show trade-offs between damping force and damper mass.Conclusions
The optimized outcomes show enhancements in the cost function as compared to the unoptimized values. The proposed methodology integrating FEMS and MOGA shows promise and can be extended to other applications of MR dampers as well.
The steady degeneration of neurons is the hallmark of neurodegenerative illnesses, which are, by definition, incurable. Corticobasal Syndrome (CS), Huntington's Disease (HD), Dementia, Amyotrophic Lateral Sclerosis (ALS), Progressive supranuclear palsy (PSP) and Parkinson's Disease (PD) are some of the common neurodegenerative diseases which has impacted millions of people, predominantly among the older population. Various computational techniques, including but not limited to machine learning, are emerging as discrimination and detection of neuro-related diseases. This research proposed a machine learning-based framework to correctly detect PD, HD, and ALS from the gait signals of subjects both in binary and multi-class detection environment. The detection approach proposed here combines the classification power of Naïve Bayes and Logistic Regression jointly in a modern UltraBoost ensemble framework. The proposed method is unique in its ability to detect neuro diseases with a small number of gait features. The proposed approach ascertains most essential gait features through three state-of-the-art feature selection schemes, infinite feature selection, infinite latent feature selection and Sigmis feature selection. It has been observed that the gait signal features of the subjects are identified through Infinite Feature Selection manifests better detection results than the features obtained through Infinite Latent Feature and Sigmis feature selection while detecting Parkinson's and Huntington's Disease in a multi-class environment. So far as the binary detection environment is concern, the Amyotrophic lateral sclerosis is detected with 99.1% detection accuracy using 18 Sigmis gait features, with 99.1% sensitivity and 98.9% specificity, respectively. Similarly, Huntington's disease was detected with 94.2% detection accuracy, 94.2% sensitivity, and 94.5% specificity using 5 Sigmis gait features. Finally, Parkinson's disease was detected with 98.4% sensitivity, specificity, and detection accuracy. Abstract The steady degeneration of neurons is the hallmark of neurodegenerative illnesses, which are, by definition, incurable. Corticobasal Syndrome (CS), Huntington's Disease (HD), Dementia, Amyotrophic Lateral Sclerosis (ALS), Progressive supranuclear palsy (PSP) and Parkinson's Disease (PD) are some of the common neurodegenerative diseases which has impacted millions of people, predominantly among the older population. Various computational techniques, including but not limited to machine learning, are emerging as discrimination and detection of neuro-related diseases. This research proposed a machine learning-based framework to correctly detect PD, HD, and ALS from the gait signals of subjects both in binary and multi-class detection environment. The detection approach proposed here combines the classification power of Naïve Bayes and Logistic Regression jointly in a modern UltraBoost ensemble framework. The proposed method is unique in its ability to detect neuro diseases with a small number of gait features. The proposed approach ascertains most essential gait features through three state-of-the-art feature selection schemes, infinite feature selection, infinite latent feature selection and Sigmis feature selection. It has been observed that the gait signal features of the subjects are identified through Infinite Feature Selection manifests better detection results than the features obtained through Infinite Latent Feature and Sigmis feature selection while detecting Parkinson's and Huntington's Disease in a multi-class environment. So far as the binary detection environment is concern, the Amyotrophic lateral sclerosis is detected with 99.1% detection accuracy using 18 Sigmis gait features, with 99.1% sensitivity and 98.9% specificity, respectively. Similarly, Huntington's disease was detected with 94.2% detection accuracy, 94.2% sensitivity, and 94.5% specificity using 5 Sigmis gait features. Finally, Parkinson's disease was detected with 98.4% sensitivity, specificity, and detection accuracy.
Magnetorheological fluid is a field-responsive material. The rheological properties of magnetorheological fluid can be precisely controlled and reversed. This change in rheological property controls the damping force by an externally applied magnetic field and makes them suitable for automotive, structural, manufacturing, and military applications. In the last two decades, it has gained a significant impact in the field of intelligent healthcare devices. Various biomedical devices had been developed to mimic and restore the gait cycle for the amputees. However, MR-based devices provide real-time controlled damping to improve the gait cycle. This review briefly discusses the tailor-made properties of magnetorheological fluid based on their constituents and stabilization methods. And, it also addresses the significant contributions of magnetorheological fluid in the lower-limb prosthetic devices.KeywordsMagnetorheological fluidProsthetic devicesSemi-active devices
