Senior Design Expo 2022

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Electrical Engineering EMG Wrist-Set Daniel Addison, Ji Ku, Saul Partida, Talaya White, Marc Sorrentino David Vallancourt, Electrical Engineering EMG or Electromyography is a technique that evaluates and records the electrical activity resulting from skeletal muscle movement. Utilizing EMG has been useful to comprehend nerve dysfunction, muscle dysfunction or dysfunction between nerve and muscle [1,2] . This project aims to develop a wearable, sensing wristband that can avail the electrical signaling taking place near the wrists—a heavily innervated region of our bodies—to manipulate an external device. Potentially, we can transduce EMG sensors [3] (Fig.1.) into a digital signal—to be wirelessly transmitted to a computer (for processing and interpreting)—that we then relay to an arrow-based keyboard for applications such as video games. This wristband can also be used for early detection of abnormalities in motor and sensory nerves. The project begins with placement of electrodes on both sides of the forearms and retrieving measurements of arm muscles. Then, a switching circuit and Analog to Digital Converter (ADC) are used to convert analog electrode signals to 8 bit digital signals to be read to an arduino device. The switching circuit multiplexes four independent signals into a single ADC. After digital conversion, signals are classified using a three-layered neural network (implemented using Tensorflow) [4] . The trained model is packaged into an arduino header file to be accessed in the arduino IDE and uploaded to the Machine learning enabled Arduino Nano 33 BLE Sense. The classified signals are broadcasted to a videogame of choice for a comprehensive demonstration. Testing of the circuit shows successful discrimination of two key movements of the wrist: wrist flexion and extension (Fig. 2). The multiplexor cycles promptly and cleanly through multiple different waveforms (Fig. 3) passed simultaneously into the MUX-ADC circuit (Fig. 4). These results demonstrate the feasibility and scalability of the intake of several sensors using limited resources (e.g. the use of a single ADC and 8 digital I/O pins of the arduino). Reference [1] D. Moores, “Electromyography (EMG): Purpose, procedure, and results,” Healthline, 20-Mar-2018. [Online]. Available: https://www.healthline.com/health/electrom yography. [Accessed: 13-Apr-2022]. [2] “Electromyography (EMG),” Mayo Clinic, 21-May-2019. [Online]. Available: https://www.mayoclinic.org/testsprocedures /emg/about/pac-20393913. [Accessed: 13-Apr-2022]. [3] AdvancerTechnologies, “Hack your muscles! A muscle (EMG) sensor for a Microcontroller,” Instructables Workshop, 06-Jul-2011. [Online]. Available: https://forum.arduino.cc/t/hack-your-muscle s-amuscle-emg-sensor-for-a-microcontrolle r/65475. [Accessed: 13-Apr-2022]. [4] S. Mistry and D. Pajak, “Get started with machine learning on Arduino: Arduino documentation,” Arduino Documentation | Arduino Documentation, 11- Apr-2022. [Online]. Available: https://docs.arduino.cc/tutorials/nano-33-ble - sense/get-started-with-machine-learning. [Accessed: 13-Apr-2022]. 28
Electrical Engineering Fig. 1. EMG circuit Fig. 2. Demonstration of MUX circuit alternating between the capture of a 1 Hz sine wave and the EMG output. Fig. 3. switching and Analog to Digital Converter (ADC) circuit Fig. 4 Demonstration of channel 2 (green) spiking after wrist extension and channel 1 (yellow) spiking after wrist flexion. 29
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