Characterized Bioelectric Signals by Means of Neural Networks and Wavelets to Remotely Control a Human-Machine Interface

Abstract

Everyday, people interact with different types of human machine interfaces, and the use of them is increasing, thus, it is necessary to design interfaces which are capable of responding in an intelligent, natural, inexpensive, and accessible way, regardless of social, cultural, economic, or physical features of a user. In this sense, it has been sought out the development of small interfaces to avoid any type of user annoyance. In this paper, bioelectric signals have been analyzed and characterized in order to propose a more natural human-machine interaction system. The proposed scheme is controlled by electromyographic signals that a person can create through arm movements. Such arm signals have been analyzed and characterized by a back-propagation neural network, and by a wavelet analysis, in this way control commands were obtained from such arm electromyographic signals. The developed interface, uses Extensible Messaging and Presence Protocol (XMPP) to send control commands remotely. In the experiment, it manipulated a vehicle that was approximately 52 km away from the user, with which it can be showed that a characterized electromyographic signal can be sufficient for controlling embedded devices such as a Raspberri Pi, and in this way we can use the neural network and the wavelet analysis to generate control words which can be used inside the Internet of Things too. A Tiva-C board has been used to acquire data instead of more popular development boards, with an adequate response. One of the most important aspects related to the proposed interface is that it can be used by almost anyone, including people with different abilities and even illiterate people. Due to the existence of individual efforts to characterize different types of bioelectric signals, we propose the generation of free access Bioelectric Control Dictionary, to define and consult each characterized biosignal.

Window length insensitive real-time EMG hand gesture classification using entropy calculated from globally parsed histograms

“…Bu et al 13 tested Time Delayed Feature (TDF) against Time-domain Features (TF) for continuous estimation of upper limb angles using the Random Forest (RF) algorithm. Tinoco et al 16 controlled a simple remote device with a 52 km distance through the internet using EMG as a proof of concept of an Internet of Things (IoT) application. Also, this paper presents EMG as an HMI that anyone with minimal education can use.…”

“…Another alternative method is to design controllers. 8,9 However for both condition diagnosis 10,11 and motion intention detection [12][13][14][15][16] it is shown that ML methods result in effective models that overcome the difficulties of EMG.…”