Applying Biosensors’ Electromyography Signals through an Artificial Neural Network to Control a Small Unmanned Aerial Vehicle
Authors: Mylena McCoggle, Shyra Wilson, Andrea Rivera, Rocio Alba-Flores, Valentin Soloiu
This work describes a system that uses electromyography (EMG) signals obtained from muscle sensors and an Artificial Neural Network (ANN) for signal classification and pattern recognition that is used to control a small unmanned aerial vehicle using specific arm movements. The main objective of this endeavor is the development of an intelligent interface that allows the user to control the flight of a drone beyond direct manual control. The sensor used were the MyoWare Muscle sensor which contains two EMG electrodes used to collect signals from the posterior (extensor) and anterior (flexor) forearm, and the bicep. The collection of the raw signals from each sensor was performed using an Arduino Uno. Data processing algorithms were developed with the purpose of classifying the signals generated by the arm’s muscles when performing specific movements, namely: flexing, resting, and motion of the arm. With these arm motions roll control of the drone was achieved. MATLAB software was utilized to condition the signals and prepare them for the classification. To generate the input vector for the ANN and perform the classification, the root mean square and the standard deviation were processed for the signals from each electrode. The neuromuscular information was trained using an ANN with a single 10 neurons hidden layer to categorize the four targets. The result of the classification shows that an accuracy of 97.5% was obtained. Afterwards, classification results are used to generate the appropriate control signals from the computer to the drone through a Wi-Fi network connection. These procedures were successfully tested, where the drone responded successfully in real time to the commanded inputs.
Keywords: Biosensors, electromyography, Artificial Neural Network, Arduino, drone flight control, machine learning.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 311
 A Brief History of Drones, Imperial War Museums, http://iwm.org.uk/history/a-brief-history-of-drones.
 C. DeLuca, R. LeFever, M. McCue, and A. Xenakis, “Behaviour of human motor units in different muscle during linear-varying contractions”, J. Physiol. (London), 329 (1982), pp. 113-128.
 Jaramillo-Yanes, M. Benalcazar, and E. Mena-Maldonado, “Real-time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review”, Sensors, Feb. 2020
 R. Chowdhury, “Surface Electromyography Signal Processing and Classification Techniques”, Journal Sensors, Published online 2013 Sep. 17. doi: 10.3390/s130912431
 M. Raez, M. Hussain, and F. Mohd-Yasin, “Techniques of EMG signal analysis: detection, processing, classification and applications,” Biol. Proced. Online, vol. 8, pp. 11–35, 2006.
 MyoWare Muscle Sensor form Advancer Technologies, http://www.advancertechnologies.com/p/myoware.html.
 S. Du, and M. Vuskovic, “Temporal vs. Spectral Approach to Feature Extraction from Prehensile EMG Signals, “Proceedings of the 2004 IEEE International Conference on Information Reuse and Integration, Nov. 8-10, 2004, Las Vegas NV.
 Magnus Ekman, Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow, Addison-Wesley, 2022.
 I. Goodfellow, Y. Bengio, and A.Courville, Deep Learning, MIT press, 2016.