Our story started around 2009, at Instituto de Telecomunicações, within a research group under the supervision of Prof. Ana Fred. The main focus of this group was the area of Physiological Computing, especially regarding the development of signal processing and pattern recognition methods for the automatic analysis of biosignals such as the electrocardiogram (ECG), electrodermal activity (EDA), electromyogram (EMG), and electroencephalogram (EEG).

One fundamental step of our work is the acquisition of these signals, which requires robust hardware equipment, typically at a high price. To contain costs, we started developing our own hardware, which eventually led to the creation of BITalino, a low-cost toolkit to prototype applications using body signals. This toolkit was complemented with BioSPPy, a suite of signal processing methods, written in Python, to easily analyze the acquired signals.

The core of our research revolved around the ECG, a tremendously rich signal, being continuously available, related to the psychophysiological state of the subject, and easy to acquire unobtrusively in an off-the-person approach. In particular, we are able to have an accurate measure of the cardiac signal using just two electrodes placed at the hands, without using any gel. This setup is simple enough to be embeddable into many everyday-life objects.

Additionally to being an established wellbeing indicator, the ECG can be used to identify and distinguish individuals, much like a fingerprint. This particular application led to the dynamization of the Vitalidi project, which won the first place of the InovPortugal challenge in 2013. Following further product development, we officially launched our company, now called CardioID Technologies, with the goal of exploiting the use of the ECG for identity recognition, as well as other innovative applications built around this signal.



Strategic to our success, we have established partnerships with multiple entities, both in the industry and academic domains. These partnerships allow us to test and validate our systems, as well as gather market intelligence essential for the development of our products.




Generation Mobi

Generation Mobi is a Research & Development project that aims at developing and validating a dynamic mobility management system for new generation services. It is based upon the concept of social network of interactive bikes, which are interoperable with the city ecosystem.

Date: March 2016 – September 2018



SOUL-FI funding allowed us to develop a pilot program with a transportation company to test CardioWheel in real world scenarios. To that end, we applied our technology to a steering wheel cover connected with an electronic unit, that autonomously processes all the data, and an M2M interface that exchanges information with a set of cloud services powered by FIWARE technology.

Date: January – July 2016

Would you like to join us?

Send your CV to jobs at cardio-id.com