Electrocardiogram (ECG) Sensor

The electrocardiogram sensor can detect the electrical and muscular functions of the heart. This sensor is composed by three connectors (positive, negative and neutral) that need to be attached to the body of the patient while lying on a flatbed.

This is one of the sensors that use the highest sample frequency due the level of accuracy needed to graph the heart activity waveform. At the same time this sensor present a high sensitivity to the movement of the subject, then if the patient moves while the sample is taken, then the values change drastically.

Like the GSR, the connectors of the sensor are attached directly to the skin of the patient but in this case the connector has a small piece of conductive gel that help to get a good conductivity.

Internally the sensor is connected to the analog pin 0 of the Arduino and can measure values between 0 and 1023. The software library does not have a minimal sample frequency because as mentioned before, this sensor needs a high value in order to get a good waveform.

The e-Health Sensor Platform is presented as a very good option for research and study of human emotions but early tests have shown that the platform sometimes is unstable and not very reliable.

The objective of this thesis is to test the platform, modify and develop the necessary software to make it more stable and reliable for future studies related to Affective Computing specially oriented in the field of learning but not restricted only to this area.

The work includes testing the e-Health Sensor Platform with different hardware and software architectures to define which presents the best performance and obtaining 2 or more biomedical signals simultaneously.

The first part of this work is to analyse the hardware. As an intrinsic condition of this work the platform must consider the Arduino board and e-Health Shield. The only hardware that can be changed is the electrical contact sensors and the computers responsible for capturing and processing data and graph.

The second part, is to analyse the software. In this specific point there are many options that was explored both in Arduino software and processing software.

This chapter details how the prototypes were designed and the benchmark tests used to compare them, and finally how the high performance prototype was selected and used for testing purpose and data capture. In order to obtain signals noise free useful to detect emotional changes, was necessary to test different software architectures to communicate the hardware devices, process the noise, graph and store the data.