Design and Development of a Low Cost Prosthetic Arm Control System Based on sEMG Signal

Abstract

The aim of this paper is to design and develop a low-cost prosthetic arm based on surface electromyography (sEMG) signal activities of the biceps muscle during upper-limb movement. Different methods are described in the literature, but many problems are encountered in dealing with the online processing of raw EMG (rEMG) signals, such as signal sampling and memory requirements. In this paper, the enveloped EMG (eEMG) signal is used as a control signal that reduces signal sampling rate and memory requirements. The relationship between elbow motion and the activity level of the biceps muscle is characterized using relevant extracted features (root mean square (RMS)). Validation of the proposed low-cost system is conducted using comparison with a professional biomedical system (Bioback MP150). In addition, the estimated equation of movements of each subject is estimated based on the recorded data. From this equation, the angle of motion is calculated as the control of the movement of the robotic arm. Finally, the system proposed in this paper considers the eEMG signal rather than the rEMG signal and deals with the signal based on a sample of 1 KHz rather than 10 KHz. This system reduces our target cost (reduction in hardware requirements and processing time) with acceptable accuracy. The experimental results illustrate that the eEMG signal has the same features-print as that of the rEMG signal, and the eEMG signal can generate the control signal required to move the prosthetic arm.

Efficient Control System Based on Hand Nerve Signals

“…These commands are used to control the operation of ON/OFF LEDs as a prototype that can be replaced by any other robot or moving objects. The first experiment adapts itself to record the average of classification accuracy of the ten hand movements in the two case studies of the amputees people by stabilizing the location of the muscle sensor in each case study with measurement of classification success rate of all discussed movements according to equation (1).…”

“…In this experiment, the total average classification accuracy reached to 92.335% from the result of ten muscles movements that are listed in Table VII and Table VIII. The second experiment employs itself to record the average of classification accuracy of the ten hand movements in the two case studies of the amputees people by stabilizing the location of the muscle sensor in each case study with measurement of classification success rate of all discussed actions according to equation (1). In this experiment, we take into account the second testing digital thresholding ranges that are listed in Table IV.…”

“…Upper-limb motions, which can be characterized and estimated using the activation of the upper-limb muscles, are very important for many daily human activities but the loss of the human upper limb is a major disability that terribly limits the everyday capabilities and interactions of individuals and it is difficult for physically amputated and injured individuals to perform those activities [1]. Moreover, it also leads to depression along with other psychological risks.…”

Estimation of joint angle based on surface electromyogram signals recorded at different load levels

“…In our previous study, the proposed low-cost hardware is validated against EMG100C, the professional EMG signal module of the Biopack system, using several stages of validation, and shows that it provides the same results given by the professional Biopack system. Therefore, the proposed system reduces our target costs with acceptable accuracy [16].…”

“…In this study we use our proposed low cost data recording system to record sEMG signals [16]. The proposed system performs the following steps: signal acquisition, then signal digitalization, data segmentation, after that signal processing (feature extraction and classification), and finally, control signal generation unit.…”