Physics Education论文代写：在这一章中，将讨论足踝肌电信号包括踝足矫形器、肌电信号和电极。本章分为六节，介绍，背景，前人的文献综述，文献综述的摘要和结论。
联合可以称为关节或关节病，是一个点的两骨之间的接触，骨骼和软骨之间，或在骨和牙齿。它是由有弹性的结缔组织形成的，保持骨骼在一起形成一定程度的运动。在结构上，接头分为三个不同类型的纤维连接，关节软骨和滑膜关节。这些不同类型的关节提供了各种关节，如膝关节、髋关节、肘关节、腕关节、踝足关节等。对于这个项目，踝足关节运动是用来进行分类的基础上标准的实验程序。这种踝足关节可分为滑膜关节。
In this chapter, it will discuss about ankle foot EMG signal which included ankle foot orthosis, EMG signals and electrodes. This chapter consists of six subchapters which are introduction, background, literature review of previous work, summary of literature review and conclusion.
A joint can be called an articulation or arthosis, is a point of contact between two bones, between bones and cartilage, or between bone and teeth. It is formed from flexible connective tissues that hold bones together to form some degree of movement. Structurally, joints are classified in three different types which are Fibrous joint, Cartilaginous joint and Synovial joint. These different types of joint provides various joints such as knee joint, hip joint, elbow joint, wrist joint, ankle foot joint and so on. For this project, ankle foot joint movement is used to be classified based on standard experiment procedures. This ankle foot joint can be classified as synovial joint.
In order to designate the movements that can occur at synovial joints, specified terminology is used. It is to indicate the form of motion, the direction of movement or the relationship of one body part to another during movement. Thus, movements at synovial joints are grouped into four main categories which are gliding, angular movements, rotation and special movements. Ankle foot joint can be categories as special movements which are dorsiflexion and plantar flexion that occur only at certain joints. Dorsiflexion refers to bending of the foot at the ankle in the direction of the dorsum (superior surface). Plantar flexion involves bending of the foot at the ankle joint in the direction of the plantar or inferior surface (sole).
However, human cannot avoid any physical inability like drop foot on their ankle foot. Severing of nerve, stroke, cerebral palsy and multiple sclerosis are diseases that influence drop foot to occur. Drop foot is the condition of an individual inability to lift their foot because of lower extremity muscles of individual lose the control of ankle joint as a results of reduced or no muscle activity around their ankle. It will result of two complications which are slap foot and toe drag during gate cycle. Slap foot occur when individual cannot control the falling of their foot after heel strike so he/she will slaps the ground on every step while toe drag occur where individual have inability to clear their toe during swing so he will drag their toe on the ground throughout swing. This condition will lead to uncontrolled movement of human ankle foot.
2.2.2 Active Ankle Foot Orthoses (AAFO)
In the increasing of robotic technology, it is crucial to develop therapy tools or permanent assistive device in order to treat of individuals suffering from physical disability. However, lower extremity robotic device has also been developed by the researchers. This robotic device is developed in order to help disabled persons to walk with better gait cycle. Drop foot is one of example of physical disability that occur when an individual was diagnosed with stroke or other several diseases for like cerebral palsy, multiple sclerosis and severing of nerve.
AAFO is an orthotic device intended to support or to restore the movement of the complex ankle foot in stroke patient as well as to treat individual who suffering from gait pathology is known as drop foot. It is made up of rigid polypropylene structure that prevents any ankle motion. AAFO is used as a controller for dorsiflexion and plantarflexion movement with the help of EMG signal. In order to ensure that this device is functioned well, which mean that this device can be used as a controller for the ankle foot movement, so input and output is need to consider in the design of AAFO.
The input of the device is any peripheral used to provide data and control signals to an information processing system like computer and so on. Therefore, based on the application to control of AAFO, ankle foot joint signals are used as the input to the device of AAFO. It is because, AAFO device is only able to be functioned as to control the dorsiflexion and plantar flexion when it received EMG signals from the ankle foot joint. This EMG signals act as a command to interact with AAFO device.
The output of the device is any piece of hardware equipment used to communicate the results carried out by an information processing system to the outside world. The output allows the device to interact with the input command. The movement of dorsiflexion and plantarflexion can be controlled by the control system of the AAFO. The control system of the AAFO is the output of the system. Thus, this output is used as the control system of the device based on the input of the EMG signals.
Previously, AAFO is designed to control the dorsiflexion and plantarflexion movement which involves artificial pneumatic muscles which in turn are controlled by computer controlled air pressure based on acquires EMG signals. This computer system is used as a controller and thus making the system huge and not portable. However, a standalone embedded system is developed for control of ankle foot orthosis using sEMG signals. This system do not use computer control system control the movement of ankle foot. Therefore, this new system is more practical to be used for the physical disability person and also for a long time use.
2.2.3 EMG signals
The electromyography (EMG) signals is an electrical activity measurement of the either voluntary or involuntary of muscle contraction. This electrical activity was generated due to the functional variations in muscle fiber membranes. Actually, motor unit is the basic unit of muscle contraction. Motor unit contained of a single alpha motor neuron and all the fibers. This muscle fiber contracts when the action potentials of the motor nerve which supplies it reaches a depolarization threshold. The depolarization generates an electromagnetic field which is measured as a very small voltage that is called as EMG. Thus, EMG is the result summation of all Motor Unit Action Potential (MUAP) in the region near the electrodes. The typical EMG signal levels are in the region of microvolt up to millivolt and depending on many factors. Thus, the measured of EMG signal should be amplified and processed to eliminate low-frequency or high frequency noise or other possible artifacts.
Besides that, EMG contains two types of crucial information which are timing of muscle activity and relative intensity. EMG signal is obtained from the subject by using invasive or non-invasive method. Invasive method is measure directly to the subject muscle while non-invasive method is measure indirectly out of the subject muscle which on the skin of the subject. However, non-invasive method is more preferable to be used because it is easier to apply and provides less risk to the subject compared to the invasive method. It is because, invasive method can be used only by a trained professional person while non-invasive method can be used by any person although that person do not attend any training to used that method.
Non-invasive technique is used widely in biomedical engineering area. This technique involves the user to use electrode. However, there are several factors that need to be considered in order to use this technique such as electrode sizes, positions, orientations and the inter electrode distance (IED). Electrode size varies between 1mm square until a few centimeters square. It is clearly influences the EMG signals because the increase size of electrode will make the view of electrode increase as the size perpendicular to muscle fibers also increase. Therefore, the recommended size of electrode should not exceed than 10mm. The position of electrode is recommended to be placed halfway the distal motor endplate zone and the distal tendon with respect to the longitudinal location of the muscle. The orientation and IED of the electrode is advised to place around the optimal sensor location which directed parallel to the muscle fibres. Electrode is preferable to be placed with IED of 20mm because maximal EMG amplitude is expected within this distance.
There are two main issues that should be concerned when detecting and recording the signal because it will influence the fidelity of the signal like signal-to-noise ratio and the distortion of the signal. Signal-to-noise ration is the ratio of the energy in the EMG signals to the energy in the noise signal. Noise is the unwanted signals of the desired EMG signals. The distortion in the noise signal means that the relative contribution of any frequency component in the EMG signal should not be altered.
However, the quality of EMG signal can be maximized by following ways:
The signal-to-noise ratio should contain the highest amount of information from EMG signal as possible and minimum amount of noise contamination.
The distortion of EMG signal must be as minimal as possible with no unnecessary filtering and distortion of signal peaks and notch filters are not recommended.
It can say that EMG signals is a complicated signal because it is affected by the anatomical and physiological properties of the muscles, the control scheme of the peripheral nervous system, the instrumentation used for detection of the EMG signal and the process used to record the EMG signals. If there are any changes for those criteria, automatically it will affect the character of the signal, the analysis and conclusions drawn from the data. However, no matter how difficult EMG signals to be analyzed, most of the clinical diagnostic reports is based on the muscle activity and intensity components of the EMG signal. Nowadays, analysis of EMG signals with powerful and advanced techniques is becoming a very essential requirement in biomedical engineering. Therefore, this project that is involved of EMG signals to control the dorsiflexion and plantarflexion movements of the ankle foot are developed.
2.2.4 Electrode
Electrode can be classified into two types which are surface electromyography and needle electromyography. Surface electromyography is a non-invasive technique used to detect EMG signals from skin surface of the muscles while needle electromyography is an invasive technique used to detect EMG signals from inside of the muscles. However, surface electromyography is more preferable to be used because it is very easy to apply compared with needle electromyography signals are difficult to use practically. Besides that, surface electromyography also provides minimal pain when used, very good for movements application and more reproducible to use.
Surface electromyography can divided into two which are pre-gelled electrode and non-gelled electrode. Pre-gelled electrode is disposable electrodes that can be used by paste it directly on the skin surface and no need to use electrode gel. Non-gelled electrode need to be used with an electrode gel to ensure a good skin contact. Both pre-gelled electrode and non-gelled electrode provides similar performances but pro-gelled electrode is recommended. It is because, non-gelled electrode is bigger that pre-gelled electrode so it need wider space of the skin and it is also time consuming when used.
Surface electromyography is made up of different types of materials for examples Ag/AgCl, AgCl, Ag, Au and many more. This material will form the contact layer with the skin, so it needs to be a good electrode with skin contact. In addition, electrode material needs to provide low electrode skin impedance and have a stationary behavior when it is being used. Therefore, Ag/AgCl electrode is the most common electrode that is used by the user. It is because, it provide a stable transition with low noise and are easily available.
2.3 Literature review on previous works
According to the journal "Real-time control of active ankle foot orthosis using Labview and Compact-Rio" Kanthi.M, I.S.V. Karteek, Mruthyunjaya .H.S and V.I.George explained functional variations in muscle fiber membranes have generated electrical activity where it can be recorded and acquired by using electromyography (EMG) technique. The proposed control system is a real time embedded system for controlling Ankle Foot Orthosis using sEMG signals from the Tibialis Anterior and Medial Gastrocnemius muscles that responsible for the movement of ankle. The algorithm is required for analyzing acquired sEMG and implementing control method is developed in NI Labview and it is deployed to cRIO to make the whole AFO system can be used as a standalone embedded system without use of computer for process of signals and control of motor for realizing ankle movements. The control signal is based on the extracted RMS value of sEMG signals. The result shows an average threshold value signal from dorsiflexion is 0.2V and plantarflexion is 0.1V.
According to the journal "Active Ankle Foot Orthoses (AAFO)" Joaquin Blaya, Dava Newman and Hugh Herr used a polypropylene Ankle Foot orthosis (AFO) with a hinge joint at the ankle. Ankle angle is measured by placing a rotary potentiometer at the ankle while total ground reaction force and the center of pressure is measured by placing six capacitive force sensors between AFO and the shoe. A Series Elastic Actuator (SEA) is used to power the Active Ankle Foot Orthosis (AAFO). A finite state machine is used to divide the gait cycle into three states. The first state occurs from heel strike until the body passes over the foot to eliminate slap foot by acting as a virtual linear, rotary spring around the ankle. AAFO will be able to determine the abnormal stiffness of the user and add the correct amount of resistance to eliminate drop foot by using the force and position sensors. The second state will continue until the person has pushed off with their foot and entered into swing. It will have the actuator apply zero force to the ankle to not disrupt normal ankle function. The third state occurs during swing. The actuator will lift the toe acting as a linear 275 rotary spring and damper combination. This will ensure that the toe clears the ground during swing.
According to the journal "Development of an Ankle-Foot Orthosis for Hemiplegic Patients" Jung Yoon Kim, Sung Jae Hwang and Young Ho Kim explained that a conventional AFO alone does not provide a satisfactory gait pattern because it is used just for the stability in stance and toe clearance in swing. It prevents toe drag during swing phase in hemiplegic patients but does not prevent foot slap during stance phase. Active Ankle Foot Orthosis (AAFO) is developed by using four FSR sensors under the foot to determine the four gait cycles of Heel strike, Foot flat, Heel off and Toe off based on the output signals of FSR sensors and then controlled by the motor. 3D analysis is used as to differentiate the gait cycle in hemiplegic patient based on the gait without AFO, gait with the conventional AFO and gait with the AAFO. The results show that AAFO could prevent not only foot drop but proper plantar flexion during loading response but also toe drag by sufficient amount of plantarflexion in pre-swing and reasonable dorsiflexion during swing phase, enhancing almost all temporal gait parameters. AAFO might have more clinical benefits to treat foot drop and toe drag in hemiplegic patients, comparing with conventional AFO.
According to the journal "A Method to Control Ankle Exosskeleton with Surface Electromyography Signals" Zhen Zhang, Jiaxin Jiang, Liling Peng and Hongchao Fan explained a relationship between the complex movements of human body and a single muscle is identified based on studied that focused on analyzing disabilities, abnormalities and how to track progress rehabilitation. Studied of surface EMG is done to clarrify understanding about how muscles work internally and the conditions during activation. However, only a few studied are focused on surface EMG real-time to control biomechanical robot as the EMG signal is difficult to be mapped into the force a muscle that is producing. Therefore, a method of controlling the ankle exoskeleton using surface EMG signals is innovated in this paper by using neural network. After training, the joint angle could be predicted by neural network. After post processing, the prediction result is very close to the real measurement value. Experiment proves that the proposed method can accurately predict the movement of ankle joint.
According to the journal "Development of an Active Ankle Foot Orthosis for the Prevention of Foot Drop and Toe Drag" Sungjae Hwang, Jungyoon Kim, Jinbock Yi, Kisik Tae, Kihong Ryu and Youngho Kim explained Ankle Foot Orthosis (AFO) method can improve gait pattern in patients with foot drop and toe drag. However, the use of conventional AFO alone does not provide a satisfactory gait pattern because it is used just for the stability in stance and toe clearance in swing and also does not provide sufficient ankle plantar flexion movement. Thus, it does not guarantee weight support during loading response, shock absorption, push off and the acceleration of the swinging leg. In this paper, an active ankle foot orthosis (AAFO) is designed to provide proper ankle moment to prevent foot drop and toe drag based on an accurate detection of the gait phase by comparing the conventional AFO with the developed AAFO using a near-infrared 3D motion analysis system. As a result, this AAFO can induce the normal gait compared to conventional AFO. It is because, AAFO can prevent the foot drop by proper plantarflexion moment as driving force to walk forward by the sufficient push-off during pre-swing, prevent toe drag by proper dorsiflexion during swing phase and can induce an inefficient gait with the similar movement as in the normal gait. The developed AAFO could be useful in polio patients with other orthotic devices.
According to the journal "A control method of ankle foot orthosis (AFO) with artificial muscle" Masanori Sugisaka, Jiwu Wang, Hiroshi Tsumura and Masashi Kataoka explained that an Ankle Foot Orthosis of articial muscle (AFOAM) is developed to improve the rehabilitation in this paper. Two pressure sensors are placed on the bottom of the foot with the assistance of sensor cable. However, the best placement of pressure sensor can be determined based on the highest voltage value measurement. The measured reading shows that the best arrangement to put the pressure sensor is on the heel and toe. Nevertheless, there are five steps of walking condition of pressure sensor is in ON and OFF condition to determine the condition of artificial muscle. One artificial muscle is used as it is correspond to tibialis anterior muscle. An action program is made by using C++ for the AFOAM to change the air pressure for the artificial muscle. Thus, AFOAM can support actively the movement of dorsiflexion from pressurization and decompression of artificial muscle by differentiates steps of walking pressure based on voltage of pressure sensors. As a result, AFOAM is superior to the conventional AFOAM. However, the walking time with the AFOAM is short, so it is insufficient of time to determine the effectiveness of rehabilitation of AFOAM as the experimental data is not obtained very well with the insufficient of time.
According to the journal "Developing an ankle-foot muscular model using Bayesian estimation for the influence of an ankle foot orthosis on muscles" Jun Inoue, Kazuya Kawamura and Masakatsu G. Fujie explained that foot orthosis play an important as part of walking assistance and rehabilitation for disabled people. In contrast, the application of foot orthosis is depends on the condition of disorder. Thus, an Ankle Foot Orthosis (AFO) is designed to enable the disabled person to move with proper walking movement and helpful for rehabilitation. However, a foot-ankle muscular model is developed for designing AFO by considering all gait factors. It can evaluate muscle activity by using Bayesian network estimation (statistical method). In this paper, MP joint is used to measure the gait by measuring the angle of MP joint and the sole pressure of the toe part. The data will be used to build an estimation model of the foot muscular activity during the gait.
Table 2.0 Summary of the Literature Review
Articles or Journal Title
Author
No of subject
Features
Classifier
Best Results
Best Accuracy
Real- Time Control of Active Ankle Foot Orthosis using Labview and Compact-RIO
Kanthi.M, I.S V.Karteek, Mruthyunjaya .H.S, V.I.George
5
Ankle foot motion
Standalone embedded system
Average RMS 0.2V for dorsiflexion and 0.1V for plantarflexion
-
Adaptive Control of a Variable-Impedance Ankle-Foot Orthosis to Assist Drop-Foot Gait
Joaquin A. Blaya, Hugh Herr
2
Ankle foot motion
One-way ANOVA
-
200% for slow, 37% for self-selected, 108% for fast gait speeds
A Method to Control Ankle Exosskeleton with Surface Electromyography Signals
Zhen Zhang, Jiaxin Jiang, Liling Peng, Hongchao Fan
9
Ankle Exoskeleton
Neural Network
Correlation coefficient 0.9558-0.9915. Average correlation coefficients 0.9760
-
A control method of ankle foot orthosis (AFO) with artificial muscle
Masanori Sugisaka, Jiwu Wang, Hiroshi Tsumura and Masashi Kataoka
-
Ankle foot motion
Visual C++
-
-
Development of an Active Ankle Foot Orthosis for the Prevention of Foot Drop and Toe Drag.
Sungjae Hwang, Jungyoon Kim, Jinbock Yi, Kisik Tae, Kihong Ryu, Youngho Kim
5
Ankle foot motion
Gait phase detection algorithm
Maximum plantarflexion 21.5, maximum dorsiflexion 11.9, whole range of motion 33.4
-
Development of an Active Ankle-Foot Orthosis for Hemiplegic Patients
Jung Yoon Kim, Sung Jae Hwang, Young Ho Kim
1
Ankle foot motion
Gait event detection algorithm
Maximum plantarflexion 21.5, maximum dorsiflexion 11.9, whole range of motion 33.4
-
Developing an ankle-foot muscular using Bayesian estimation for the influence of an ankle foot orthosis on muscles
Jun Inoune, Kazuya Kawamura, Masakatsu G. Fujie
1
Ankle foot motion
Bayesian network
-
PL and Ga 80%, TA 30%
2.4 Conclusion
As a conclusion, there are so many methods that were used to design and control the movement of dorsiflexion and plantarflexion. However, the application of EMG is the easiest and convenient method to be used as a controller to the Ankle Foot Orthosis (AFO) device. It is because, the signals of ankle foot movements of dorsiflexion and plantarflexion are easily can get from the tibialis anterior and medial gastrocnemius muscles by using surface EMG compared to another type of biological electrodes. Therefore, this signal can be used to control the movement of dorsiflexion and plantarflexion with the assistive of AFO. The next chapter will be discussed about methodology of the project. It will go in detail about acquisition of the data, how the data will be processed and the suitable of classifier to be used.
