Embedded system for upper-limb exoskeleton based on electromyography control

Abstract

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.

A prosthetic limb managed by sensors-based electronic system: Experimental results on amputees

“…Following the developed functions are analyzed. − handlePoseData() function:the prosthesis movements are managed by exploiting five predefined poses (spread fingers, make fist, wrist flexion, wrist extension, fingers touch) recognized by the armband thanks to data provided by EMG electrodes and 9-axis IMU [18,19]. When the armband recognizes a preset pose, a callback event is transmitted to Arduino microcontroller and by the setPoseEventCall Back() function, performed pose is identified.…”

The application of instrumentation system on a contactless robotic triage assistant to detect early transmission on a COVID-19 suspect

“…All sensor readings are regulated by the Arduino microcontroller board to execute user's command created on the computer [17], [18]. It is also used as the core for measuring the vital signs of the user's since its specifications are suitable for real-time processing [19], [20]. Microcontroller boards such as Arduino have also been widely used for instrumentations and control applications in the medical field [21]- [30].…”

“…Coronavirus disease (COVID- 19), discovered in 2019, is a disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. It is highly contagious to human regardless of age [1], but people with weak immune systems, such as the elderly, pregnant women, and young children, have a higher risk of being infected [2].…”

Improved myoelectric pattern recognition of finger movement using rejection-based extreme learning machine

“…Myoelectric control has been implemented in two ways: non-pattern recognition and pattern recognition [8]. In the first application, the EMG signal has been utilized to detect force or any physical information such as angle to control the exoskeleton devices [9][10][11]. The second type of EMG implementation is the EMG-based pattern recognition.…”