Electromyography control of robotic systems

This work describes research of myoelectric interfaces and their application for controlling robotic systems. Hand gesture data collection software has been created. The neural network was designed and trained to recognize various gestures. The accuracy was 0.96 for four gestures and 0.925 for seven gestures. The prototype of myoelectric signals controlled robot with two degrees of freedom was created. Wireless direct control via bluetooth was implemented.This work describes research of myoelectric interfaces and their application for controlling robotic systems. Hand gesture data collection software has been created. The neural network was designed and trained to recognize various gestures. The accuracy was 0.96 for four gestures and 0.925 for seven gestures. The prototype of myoelectric signals controlled robot with two degrees of freedom was created. Wireless direct control via bluetooth was implemented

Does the Use of Surface Electromyography Could Improve Quality of Life among Patients Rehabilitated by Mandibular Overdentures on Different Attachments?

Objective: To analyze and compare changes of quality of life parameter among dental patients rehabilitated by the implant-supported overdentures with different attachment systems. Material and Methods: Forty-eight patients were recruited as a study cohort. The implant placement procedure was based on the results obtained by CBCT scanning and individualized surgical templates manufactured for correct implant placement. Each individual received two k3Pro Implants (Sure Type with 4.0 or 4.5 mm in diameter) at the intraforaminal area due to standard protocol of implantation provided by the manufacturer under local anesthesia. All patients were distributed between two groups based on the fact of using either Locator- or ball-attachments. Rank correlation was measured using Spearman correlation coefficient, while linear correlation was evaluated by Pearson correlation coefficient. Results: No statistically meaningful differences were noted regarding patients’ distribution among groups considering age (p>0.05) and gender (p>0.05). Provided patient-level analysis demonstrated that increase of conventional full denture service time was positively correlated with escalation of OHIP-EDENT scores. The most prominent inter-correspondences were noted specifically between longevity of denture service and elevation of scores within “Functional limitation” (r=0.61; p<0.05), “Physical pain” (r=0.51; p<0.05) and “Physical disability” (r=0.57; p<0.05) subdomains. No statistically argumented regressions were noted between increase tendency of OHIP-EDENT scores and gender (p>0.05) or age (p>0.05) parameters. Conclusion: Significant improvements of quality of life measured with OHIP-EDENT were noted for both types of attachments compared to the pre-treatment situation independently of additionally provided surface electromyography-based alignment

Review of electromyography control systems based on pattern recognition for prosthesis control application

Electromyographic control is a technique that involve with the detection, processing and classification of the electromyography signal that could be applied in human-assisting robots, prosthesis application or rehabilitation devices. This paper reviews recent research and development electromyographic control systems with an emphasis on pattern recognition control for prosthesis application. Various methods used in the different stages of the pattern recognition based control system are discussed in details

Development of threshold based EMG prosthetic hand

There is a real need of EMG (Electromyogram) based prosthetic hand for the amputee which should be economical as well as reliable. The cheap prosthetic hand available in market works passively. In those cases the patient does not feel the feeling of natural human hand. EMG based prosthetic hand provides the amputee feeling of natural human hand. The work that has been discussed here is to develop a prosthetic hand with one degree of freedom. The two motions developed were open and close. Most of the work is done at electronic level. The main work was to acquire the noiseless EMG signal and further to convert it into control signal for prosthetic hand, after suitable processing. For classification a threshold based technique has used rather than any classification technique like Artificial Neural Network (ANN), Fuzzy Logic and Genetic Algorithm (GA). It was tried to use the minimum hardware, without making any compromise with performance. It was done so, to achieve the target of developing a economical and reliable prosthetic hand. The threshold value used was variable and was controllable from outside by just varying the knob of potentiometer. This adds an additional dimension for tuning the device and scope to adjust the threshold according to muscle activity of subject. So the same prosthetic hand can be used by different amputees by just changing the threshold values only. The mechanical hand was having only two fingers to grasp the objects. The work was also extended to develop the frequency based Prosthetic hand. The scheme was to find out the frequency bands where the amplitude of open and close motions is different. The FFTs (Fast Fourier Transform) of EMG signal were calculated in MATLAB. The DSO (Digital Storage oscilloscope) was also having the facility of displaying the FFT of signal. It was found that there is certain possible frequency band which classifies the open and close motion of han

Low-cost and open-source anthropomorphic prosthetics hand using linear actuators

A robust, low cost, open-source, and low power consumption in the research of prosthetics hand is essential. The purpose of this study is to develop a low-cost, open-source anthropomorphic prosthetics hand using linear actuator based on electromyography (EMG) signal control. The main advantages of this proposed method are the low-cost, lightweight and simplicity of controlling the prosthetic hand using only single channel. This is achieved by evaluating the DC motor and exploring number of locations of the EMG signal. The development of prosthetics hand consists of 3D anthropomorphic hand design, active electrodes, microcontroller, and linear actuator. The active electrodes recorded the EMG signal from extensor carpi radialis longus. The built-in EMG amplifier on the electrode amplified the EMG signal. Further, the A/D converter in the Arduino microcontroller converted the analog signal into digital. A filtering process consisted of bandpass and notch filter was performed before it used as a control signal. The linear actuator controlled each finger for flexion and extension motion. In the assessment of the design, the prosthetic hand capable of grasping ten objects. In this study, the cost and weight of the prosthetics hand are 471.99 US$ and 0.531 kg, respectively. This study has demonstrated the design of low cost and open-source of prosthetics hand with reasonable cost and lightweight. Furthermore, this development could be applied to amputee subjects

Development of Cardiac Monitor Through Carotid Pulse, Phonocardiography and Electrocardiography

Heart disease is a very dangerous disease. Even today in Indonesia the disease ranks first as a cause of death. The purpose of this study is to design ECG, PCG, and Carotid Pulse on a computer with Delphi programming. The contribution of this research is that the carotid signal can be displayed on a personal computer monitor so that it can be viewed simultaneously with ECG and PCG signals. In order to intercept the carotid signal on the carotid artery on the surface of the neck, a piezoelectric sensor is used. In this study, the recording of the Carotid Pulse signal was carried out in the left upper neck area of the carotid artery with the help of a piezoelectric sensor. The primary circuit of the cardiac monitor consists of the non inverting amplifier, low pass filter 100hz, high pass filter 0,1 hz, summing amplifier and arduino microcontroller. Carotid Pulse Showed that the correlation between the pattern of the dicrotic notch (the D wave) in the carotid pulse and second heart sound (S2) was occurred simultaneously or just before the D wave pulse from carotid pulse for each respondent. The results of data collection from 10 respondents, were measured from the correlation between the Dicrotic Notch (pattern D) on the carotid pulse and S2 on the phonocardiograph occurred just before the Dicrotic Notch from carotid pulse. From the correlation measurement data obtained an average of 10 respondents, the interval between S2 and Dicrotic Notch was ±0.063 s