No abstract

Electromyography (EMG) is a well known technique used for recording electrical activity produced by human muscles. In the last few decades, EMG signals are used as a control input for prosthetic hands. There are several multifunctional myoelectric prosthetic hands for amputees on the market, but so forth, none of these devices permits the natural control of more than two degrees of freedom. In this paper we present our implementation of the pattern classification using custom made components (electrodes and an embedded EMG amplifier). The components were evaluated in offline and online tests, in able bodied as well as amputee subjects. This type of control is based on computing the time domain features of the EMG signals recorded from the forearm and using these features as input for a Linear Discriminant Analysis (LDA) classifier estimating the intention of the prosthetic user.

The physiological data acquisition system described in this paper is developed for the purposes of student laboratory exercises in biomedical engineering program. The system has the functionality of electrocardiography and electromyography monitoring and can be used as a photoplethysmograph. Alongside the portability, other significant capabilities of the system are concerned with the resources that allow the system's complete functionality without the cable connections to other devices. Besides, the system's open architecture enables various types of expanding and modifications, which is suitable for student project realizations. 56The physiological data acquisition system proposed in [2] was intended for the educational and research development purposes, as well. This portable device was realized on a Raspberry PI platform, implemented two System-on-Chip solutions and functioned as an ECG recorder and an impedance cardiographer (ICG). The recordings were transferred via Bluetooth to a PC, where they were displayed, along with certain hemodynamic parameters. The similar functionalities and the same purpose were accomplished by a Raspberry PI based mobile system proposed in [3].Another mobile system for ECG and ICG continuous monitoring was proposed in [4]. The recordings were transferred to a PC via Bluetooth, where they were displayed in the Mat lab application.In [5], a physiological measurement system has been described that would be applied on students during the learning process in order to examine the teaching effect. The signals were acquired on a sensor stage, and then wirelessly transferred to a sensor node. The data was sent from the sensor node to a PC computer through the RS232 interface, and then displayed in a real-time application in LabVIEW.All previous solutions used a PC as a means of signal (processing and) display. However, researchers in the field, for these purposes, also tend to exploit a vast availability of mobile devices like smartphones. For instance, a portable device based on a Raspberry PI platform, presented in [6] integrated a wireless transfer module and acted like a communication bridge between the ECG front end and a personalized mobile device application used for an ECG visualization. [7] proposed an Android based system for the assessment of the person's physiology and behavior in phobias. During the follow-up of the reactions to context dependent audio-visual stimuli, the physiological responses were acquired with an ECG measuring device and behavior was observed through the accelerometer/gyroscope and GPS measurements. The Android based system application comprised of both, the monitoring and the stimulus exposure feature. Another android-based physiological data acquisition system was presented in [8]. The system functioned as a portable ECG device applicable in telemedicine. Namely, the acquired signals were transferred via Bluetooth to a smartphone with Android operating system, after which they were becoming accessible to a remote health provider.Another system for the ...