CardioID Technologies is a spin-off company of Instituto Superior Técnico and Instituto de Telecomunicações (IT) that started in 2014, after several years of research at IT, 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).
CardioID Technologies was launched with the goal of exploiting the use of the ECG for identity recognition, as well as other innovative applications built around this signal. The automotive vertical was the first focus, and CardioWheel the first product – a steering wheel cover that acquires in an non-intrusive way the ECG of the driver, and triggers alerts of driver-change and drowsiness. The integration with other advanced driver assisting systems (ADAS), as Mobileye and Geotab is allowing to monitor the driver, and the driving behaviour in an innovative way.
ABOUT André Lourenço
André Lourenço holds a Licenciatura (2001), a MSc (2002), and a PhD in Electrotechnical and Computers Engineering (2014), all from Instituto Superior Técnico (IST), Universidade de Lisboa. After a brief period in the industry, working on IT projects at WeDo Consulting (2001) and on instrumentation and testing at Lusospace (2003-2005), Lourenço has developed his work on the academia, and on the scientific transfer of academic research to industry. Since 2002, he lecturers at Instituto Superior de Engenharia de Lisboa (ISEL) and collaborates as a researcher at Instituto de Telecomunicações (IT), focused on signal processing and programming. Lourenço’s speciality is pattern recognition, beginning with clustering algorithms and applications during his doctoral studies. Currently, he is CEO and one of the founders of CardioID Technologies, a Portuguese company that works with sensors, electronics, signal processing, and machine learning for biometrics and health monitoring applications, mainly using physiological signals acquired in unobtrusive and seamless ways in challenging settings, such as vehicles.