IJERT-EMG Signal Analysis and Application for Arm Exoskeleton Control (original) (raw)
Related papers
Embedded system for upper-limb exoskeleton based on electromyography control
TELKOMNIKA Telecommunication Computing Electronics and Control, 2019
A major problem in an exoskeleton based on electromyography (EMG) control with pattern recognition-based is the need for more time to train and to calibrate the system in order able to adapt for different subjects and variable. Unfortunately, the implementation of the joint prediction on an embedded system for the exoskeleton based on the EMG control with non-pattern recognition-based is very rare. Therefore, this study presents an implementation of elbow-joint angle prediction on an embedded system to control an upper limb exoskeleton based on the EMG signal. The architecture of the system consisted of a bio-amplifier, an embedded ARMSTM32F429 microcontroller, and an exoskeleton unit driven by a servo motor. The elbow joint angle was predicted based on the EMG signal that is generated from biceps. The predicted angle was obtained by extracting the EMG signal using a zero-crossing feature and filtering the EMG feature using a Butterworth low pass filter. This study found that the range of root mean square error and correlation coefficients are 8°-16° and 0.94-0.99, respectively which suggest that the predicted angle is close to the desired angle and there is a high relationship between the predicted angle and the desired angle.
International Journal of Engineering Research and Technology (IJERT), 2013
https://www.ijert.org/design-and-control-of-low-cost-portable-emg-driven-exoskeleton-device-for-human-wrist-rehabilitation https://www.ijert.org/research/design-and-control-of-low-cost-portable-emg-driven-exoskeleton-device-for-human-wrist-rehabilitation-IJERTV2IS101125.pdf Exercise is an effectual healing process for people who have lost their body functioning of motions due to flimsy muscles. In this paper we propose a method of initiating motion in disabled or physically weak human wrist using his/her diminutive muscular force. The present work introduces a process of sensing Electromyography signals for wrist motion. A lowcost device is presented which involves active bidirectional (hyperextension/flexion) movement of the wrist joint, controlled by specific EMG signals triggered by forearm muscles. The design undertakes all procedures and techniques for extraction of EMG signal, sensatory circuit, signal acquisition, amplification and filtering, ADC, and interfacing of simple model hand controlled by a controller (Arduino) via DC motor for bidirectional wrist movement. The instrument assists its user in moving and strengthening respective muscles. The concept is well-suited for rehabilitation robotics and prosthetic devices for handicap individuals.
Application of Surface Electromyographic Signals to Control Exoskeleton Robots
method are presented in this chapter. Then, the upper-limb muscle activities during daily upper-limb motions have been studied to enable exoskeleton robots to estimate human upper-limb motions based on EMG signals of related muscles. The muscle combinations are identified to separate some motions of upper-limb. Minimum number of muscles to extract signals to control frequent daily upper-limb motions has been identified. In the next step, EMG signal of identified muscles are used to control two upper-limb exoskeleton robots. A three degree of freedom (DOF) exoskeleton robot (W-EXOS) for the forearm pronation/supination, wrist flexion/extension and ulnar/radial deviation are controlled by applying the surface EMG signals of six muscles. Surface EMG signals of upper-limb muscles are applied as input information to control a 6DOF exoskeleton robot (SUEFUL-6). In each case of applying EMG signals experiments have been carried out to evaluate the effectiveness of the EMG based control method. In the next section, the detection and processing of surface EMG signals are presented. The experimental study of upper-limb surface EMG is explained in section 3. Application of EMG signals to control the W-EXOS is described in section 4. Section 5 explains the EMG based control of the SUEFUL-6. The discussion in section 6 is followed by the conclusion in the section 7.
Estimating arm motion and force using EMG signals: On the control of exoskeletons
2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008
There is a great effort during the last decades towards building robotic devices that are worn by humans. These devices, called exoskeletons, are used mainly for support and rehabilitation, as well as for augmentation of human capabilities. Providing a control interface for exoskeletons, that would guarantee comfort and safety, as well as efficiency and robustness, is still an issue. This paper presents a methodology for estimating human arm motion and force exerted, using electromyographic (EMG) signals from muscles of the upper limb. The proposed method is able to estimate motion of the human arm as well as force exerted from the upper limb to the environment, when the motion is constrained. Moreover, the method can distinguish the cases in which the motion is constrained or not (i.e. exertion of force versus free motion) which is of great importance for the control of exoskeletons. Furthermore, the method provides a continuous profile of estimated motion and force, in contrast to other methods used in the literature that can only detect initiation of movement or intention of force. The system is tested in an orthosis-like scenario, during planar movements, through various experiments. The experimental results prove the system efficiency, making the proposed methodology a strong candidate for an EMG-based control scheme applied in robotic exoskeletons.
Exercise is an effectual healing process for people who have lost their body functioning of motions due to flimsy muscles. In this paper we propose a method of initiating motion in disabled or physically weak human wrist using his/her diminutive muscular force. The present work introduces a process of sensing Electromyography signals for wrist motion. A lowcost device is presented which involves active bidirectional (hyperextension/flexion) movement of the wrist joint, controlled by specific EMG signals triggered by forearm muscles. The design undertakes all procedures and techniques for extraction of EMG signal, sensatory circuit, signal acquisition, amplification and filtering, ADC, and interfacing of simple model hand controlled by a controller (Arduino) via DC motor for bidirectional wrist movement. The instrument assists its user in moving and strengthening respective muscles. The concept is well-suited for rehabilitation robotics and prosthetic devices for handicap individuals.
EMG Signal Features Extraction of Different Arm Movement for Rehabilitation Device
Rehabilitation device is used as an exoskeleton for people who had failure of their limb. Arm rehabilitation device may help the rehab program to who suffer from arm disability. The device used to facilitate the tasks of the program should improve the electrical activity in the motor unit and minimize the mental effort of the user. Electromyography (EMG) is the techniques to analyze the presence of electrical activity in musculoskeletal systems. The electrical activity in muscles of disable person is failed to contract the muscle for movements. To prevent the muscles from paralysis becomes spasticity the force of movements should minimize the mental efforts. To minimize the used of mental forced for disable patients, the rehabilitation device should analyze the surface EMG signal of normal people that can be implemented to the device. The signal is collected according to procedure of surface electromyography for non-invasive assessment of muscles (SENIAM). The EMG signal is implemented to set the movements' pattern of the arm rehabilitation device. The filtered EMG signal were extracted for features of Standard Deviation(STD), Mean Absolute Value(MAV), Root Mean Square(RMS) in time-domain. The extraction of EMG data is important to have the reduced vector in the signal features with less of error. In order to determine the best features for any movements, several trials of extraction methods are used by determining the features that can be used in classifier. The accurate features can be appliedin future works of rehabilitation control system in real-time and classification of the EMG signal.
Application of EMG signals for controlling exoskeleton robots
Biomedizinische Technik/Biomedical Engineering, 2006
Exoskeleton robots are mechanical constructions attached to human body parts, containing actuators for influencing human motion. One important application area for exoskeletons is human motion support, for example, for disabled people, including rehabilitation training, and for force enhancement in healthy subjects. This paper surveys two exoskeleton systems developed in our laboratory. The first system is a lower-extremity exoskeleton with one actuated degree of freedom in the knee joint. This system was designed for motion support in disabled people. The second system is an exoskeleton for a human hand with 16 actuated joints, four for each finger. This hand exoskeleton will be used in rehabilitation training after hand surgeries. The application of EMG signals for motion control is presented. An overview of the design and control methods, and first experimental results for the leg exoskeleton are reported.
A study on muscle activities through surface EMG for lower limb exoskeleton controller
Proceedings - 2013 IEEE Conference on Systems, Process and Control, ICSPC 2013, 2013
The motion of human body is complex but perfect and integrated effort of brain, nerves and muscles. Exoskeleton is a promising idea for human rehabilitation of the lower limb that is weak enough to move. EMG signal contains the information of human movement and can be considered as one of the most powerful input to exoskeleton controller. In this research, the activity of the lower limb muscles that are responsible for human sit to stand and stand to sit movement has been studied. In this regard, the activities of three muscles viz. rectus femoris, vastus lateralis and biceps femoris have been observed and recorded to perceive their activation pattern. The experimental results show that the maximum voltage of vastus lateralis at activation moment is greater or equal to +0.1 mV or lesser or equal to -0.1 mVduring sit to stand and stand to sit movement whereas same throughput was found for biceps femoris during sit to stand and for rectus femoris during stand to sit movement only. © 2013 IEEE. http://ieeexplore.ieee.org/ielx7/6720515/6735086/06735124.pdf?tp=&arnumber=6735124&isnumber=6735086
Electromyogram (EMG) Signal Processing Analysis for Clinical Rehabilitation Application
Analysis of electromyogram (EMG) signal processing and its application to identify human muscle strength of rehabilitation purpose has been successfully carried out in this paper. Single channel EMG signal was obtained from human muscle using non-invasive electrodes and further process by signal acquisition circuit to get a suitable signal to be process. In the first part of signal acquisition, the amplification circuit for the small EMG signal has been design successfully. After amplification stage EMG signal was digitized through analogue and digital converter (ADC) then further process in microcontroller (ATmega328) for getting accurate EMG signal. Finally, the processed EMG signal was classified into 6 different levels in order to display the muscle strength level of the user. This EMG device can be used to help the weak person or an elderly to identity their strength level of muscle for clinical rehabilitation purpose.
Australasian Physical & Engineering Sciences in Medicine, 2019
Electromyography (EMG) is a diagnostic technique allowing for the detection of signals generated by changes in electrical potentials of striated muscles. The application of this technology is becoming an increasingly popular subject of scientific research. With the appearance of new devices retrieving EMG data, novel methods of its processing for various purposes are being developed. One such device is the Myo movement controller, produced by Thalmic Labs (now North). The device has been used for the analysis of muscle activation levels in patients with "tennis elbow" and "golfer's elbow"-conditions of upper limbs which usually result from occupational injuries. The process of their rehabilitation is complex and requires a continuous monitoring of its progress. The data obtained by means of the Myo controller was used for pattern recognition of an injured hand with relation to the healthy one. The study involved examining ten subjects, including five controls. The results indicate that the muscle activation force is considerably lower in injured individuals. The arithmetic mean for the 6 analyzed motions in the injured group is 38.54% lower. The SmartEMG application (https ://www.smart emg.com) enables the implementation of procedures performed during an examination as well as those involved in the management of the collected recordings. The study produced satisfactory results, which indicates the possibility of using the Myo controller in the treatment of elbow enthesopathy.