Pilot Study Of Electromyography Analysis Of The Arm Muscle Using Levenberg-Marquadt Back Propagation Neural Network

The study on the EMG signal is useful in providing the information regarding to the force and motion command that can be used in clinical research, rehabilitation and assistive technology. However, it is difficult for one feature parameter to reflect as a unique feature of the measured EMG signals to the force and motion commands perfectly. Moreover, it is challenging to identify and classify the muscle force that exerted by a muscle and the muscle activities according to a specific movement. This research aims to propose EMG signal pattern recognition that based on back propagation neural network approach to identify the force and motion commands. This research focuses on the upper arm muscle, which is the biceps brachii muscle that leads in the improvement of the system of the prosthetic upper arm. The proposed EMG signal pattern recognition is consisting of data acquisition, data processing, data classification and data testing. The data acquisition phase is designed to acquire EMG signal from the subject. Features extraction for the EMG signal is carried out in the data processing phase. In this phase, statistical features such as maximum amplitude, mean and root mean square are computed for the features extraction purpose. In data classification phase, the three extracted time domain features are used as inputs to train the measured EMG signal via Levenberg-Marquadt backpropagation neural network training function. Then the EMG signal is classified via conjugate gradient backpropagation neural network training function. In data testing phases, three additional subjects are selected to follow the proposed EMG signal pattern recognition phases. In this research, muscle force model is used to determine the value of the force exerted by the biceps muscle. The muscle force model is based on the lever system in human body, which is third class lever. The results from the statistical analysis shows that the changes of the amplitude of the EMG signal are changing correlated to the changes of the muscle force exerted by the biceps muscle depending on the size of the loads. The analysis of pattern recognition for the measured EMG signal shows a good performance of the classification. The EMG signal can be classified based on the tasks of different weight of loads and different angle of the arm motion. The analysis of the muscle force model shows that the value of the muscle force exerted by the biceps muscle is different for all subjects. As the conclusion, it is proved that this research has been successfully accomplished and the relevance of the relationship between the changes in the movement of the hand towards the EMG signal changes and the changes of the force exerted by the biceps muscle has been proved. These findings are useful to be applied on the development of the assistive technology in helping the disabled person. These findings also can lead to improve the system of the assistive technology, especially for the improvement of prosthetic arms

Myoelectric forearm prostheses: State of the art from a user-centered perspective

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

New developments in prosthetic arm systems

Absence of an upper limb leads to severe impairments in everyday life, which can further influence the social and mental state. For these reasons, early developments in cosmetic and body-driven prostheses date some centuries ago, and they have been evolving ever since. Following the end of the Second World War, rapid developments in technology resulted in powered myoelectric hand prosthetics. In the years to come, these devices were common on the market, though they still suffered high user abandonment rates. The reasons for rejection were trifold - insufficient functionality of the hardware, fragile design, and cumbersome control. In the last decade, both academia and industry have reached major improvements concerning technical features of upper limb prosthetics and methods for their interfacing and control. Advanced robotic hands are offered by several vendors and research groups, with a variety of active and passive wrist options that can be articulated across several degrees of freedom. Nowadays, elbow joint designs include active solutions with different weight and power options. Control features are getting progressively more sophisticated, offering options for multiple sensor integration and multi-joint articulation. Latest developments in socket designs are capable of facilitating implantable and multiple surface electromyography sensors in both traditional and osseointegration-based systems. Novel surgical techniques in combination with modern, sophisticated hardware are enabling restoration of dexterous upper limb functionality. This article is aimed at reviewing the latest state of the upper limb prosthetic market, offering insights on the accompanying technologies and techniques. We also examine the capabilities and features of some of academia’s flagship solutions and methods

Use of accelerometers in the control of practical prosthetic arms

Accelerometers can be used to augment the control of powered prosthetic arms. They can detect the orientation of the joint and limb and the controller can correct for the amount of torque required to move the limb. They can also be used to create a platform, with a fixed orientation relative to gravity for the object held in the hand. This paper describes three applications for this technology, in a powered wrist and powered arm. By adding sensors to the arm making these data available to the controller, the input from the user can be made simpler. The operator will not need to correct for changes in orientation of their body as they move. Two examples of the correction for orientation against gravity are described and an example of the system designed for use by a patient. The controller for all examples is a distributed set of microcontrollers, one node for each joint, linked with the Control Area Network (CAN) bus. The clinical arm uses a version of the Southampton Adaptive Manipulation Scheme to control the arm and hand. In this control form the user gives simpler input commands and leaves the detailed control of the arm to the controller

Within-socket Myoelectric Prediction of Continuous Ankle Kinematics for Control of a Powered Transtibial Prosthesis

Objective. Powered robotic prostheses create a need for natural-feeling user interfaces and robust control schemes. Here, we examined the ability of a nonlinear autoregressive model to continuously map the kinematics of a transtibial prosthesis and electromyographic (EMG) activity recorded within socket to the future estimates of the prosthetic ankle angle in three transtibial amputees. Approach. Model performance was examined across subjects during level treadmill ambulation as a function of the size of the EMG sampling window and the temporal \u27prediction\u27 interval between the EMG/kinematic input and the model\u27s estimate of future ankle angle to characterize the trade-off between model error, sampling window and prediction interval. Main results. Across subjects, deviations in the estimated ankle angle from the actual movement were robust to variations in the EMG sampling window and increased systematically with prediction interval. For prediction intervals up to 150 ms, the average error in the model estimate of ankle angle across the gait cycle was less than 6°. EMG contributions to the model prediction varied across subjects but were consistently localized to the transitions to/from single to double limb support and captured variations from the typical ankle kinematics during level walking. Significance. The use of an autoregressive modeling approach to continuously predict joint kinematics using natural residual muscle activity provides opportunities for direct (transparent) control of a prosthetic joint by the user. The model\u27s predictive capability could prove particularly useful for overcoming delays in signal processing and actuation of the prosthesis, providing a more biomimetic ankle response

Near Real-Time Data Labeling Using a Depth Sensor for EMG Based Prosthetic Arms

Recognizing sEMG (Surface Electromyography) signals belonging to a particular action (e.g., lateral arm raise) automatically is a challenging task as EMG signals themselves have a lot of variation even for the same action due to several factors. To overcome this issue, there should be a proper separation which indicates similar patterns repetitively for a particular action in raw signals. A repetitive pattern is not always matched because the same action can be carried out with different time duration. Thus, a depth sensor (Kinect) was used for pattern identification where three joint angles were recording continuously which is clearly separable for a particular action while recording sEMG signals. To Segment out a repetitive pattern in angle data, MDTW (Moving Dynamic Time Warping) approach is introduced. This technique is allowed to retrieve suspected motion of interest from raw signals. MDTW based on DTW algorithm, but it will be moving through the whole dataset in a pre-defined manner which is capable of picking up almost all the suspected segments inside a given dataset an optimal way. Elevated bicep curl and lateral arm raise movements are taken as motions of interest to show how the proposed technique can be employed to achieve auto identification and labelling. The full implementation is available at https://github.com/GPrathap/OpenBCIPytho

Feature Analysis for Classification of Physical Actions using surface EMG Data

Based on recent health statistics, there are several thousands of people with limb disability and gait disorders that require a medical assistance. A robot assisted rehabilitation therapy can help them recover and return to a normal life. In this scenario, a successful methodology is to use the EMG signal based information to control the support robotics. For this mechanism to function properly, the EMG signal from the muscles has to be sensed and then the biological motor intention has to be decoded and finally the resulting information has to be communicated to the controller of the robot. An accurate detection of the motor intention requires a pattern recognition based categorical identification. Hence in this paper, we propose an improved classification framework by identification of the relevant features that drive the pattern recognition algorithm. Major contributions include a set of modified spectral moment based features and another relevant inter-channel correlation feature that contribute to an improved classification performance. Next, we conducted a sensitivity analysis of the classification algorithm to different EMG channels. Finally, the classifier performance is compared to that of the other state-of the art algorithm