Acquisition of Myoelectric Signals: Use of Opensource Software and Hardware for Signal Preprocessing

Nowadays there are several ways to acquire the superficial myoelectric signals (sEMG) since they provide a large amount of information about the different movements of the muscles, and the many applications that can be built by having this information. During the development of this project, a portable prototype of a device was designed and implemented to acquire, visualize, transmit and store myoelectric signals using the Raspberry Pi Model 3 B+and the Myo Armband. The algorithms for each task were developed in Python due to the versatility offered by this programming language. Test subjects were selected from among the population of ESPOL, to whom several tests were carried out executing different hand movements to check the correct performance of the equipment, and the results indicated that the data were acquired successfully. This portable prototype eliminates the necessity to have a PC and other electronic equipment, fixed in a specific place, to take the sEMG data of a person.