Employing EMG sensors in Bionic limbs based on a New Binary Trick Method

Abstract

Human muscles can be read by using electromyography (EMG) sensors, which are electrical signals generated by the muscles of human and animal bodies. This means it is possible to use electricity generated by muscles to control actuators/servo motors for any specific tasks. This could support a wide range of applications, especially for people with disabilities. One such application would be making bionic limbs based on servo motors. According to a study held by the K4D helpdesk report based on estimations that 15.3% of the world’s population has a moderate or severe disability, this proportion is likely to increase to 18-20% in conflict- affected areas (Thompson, 2017). The goal of this study is to make bionic limbs affordable by minimizing the cost while maintaining accuracy at an acceptable rate. To achieve this goal, the study proposes a new idea for using electromyography (EMG) sensors in bionic limbs, which suggests a decrease in the number of EMG sensors to decrease the cost and power consumption. Decreasing the number of EMG sensors will result in a loss of accuracy in controlling actuators (servo motors) because usually, each sensor is responsible for activating one servo motor. In normal projects, one will need at least six EMG sensors to control six servo motors. The study will use only three EMG sensors to control/activate six servo motors depending on the binary trick idea suggested by this study, which is manipulating all three input signals from EMG sensors at once and then deciding which servo motor to activate by using a supervised machine learning technique such as K-nearest neighbors (kNN).

Keywords

• Electromyography sensor (EMG)
• Human computer interfacing
• Bionic limbs
• Machine learning
• Arduino