Salvatore Tedesco of Tyndall National Institute, Ireland

Abstract: Recent years have witnessed an explosive growth in wearable technology. This area is experiencing a massive expansion thanks to huge technical advances in information and communication technology driven by changes in demography, lifestyle, environment, etc. Wearable sensors are currently popular as personal tracking devices, but wearables can assume a more significant role in multiple applications, such as personalized health, sports, rehabilitation, personal entertainment, etc. In conjunction with technological advances in smart systems, the continuous growth in numbers of connected wearable devices raises major issues in terms of dealing with huge amounts of data originating from heterogeneous devices. Machine learning and artificial intelligence will enable a new capability to provide real-time recognition of patterns in the sensor data which can help to identify events of interest and provide real-time feedback on such events to the wearer or caregiver so appropriate decisions can be made. This presentation is focused on investigating the integration of wearable technology and developing machine learning models in the application spaces of healthcare, rehabilitation, and fitness monitoring. The works discussed in the presentation demonstrate the potential of wearables in diverse applications and emphasizes the role of machine learning in enhancing their practical use for widespread adoption in healthcare and fitness.

Biography:

Short Bio: Salvatore Tedesco (Member, IEEE) received the B.Sc. degree (Hons.) in information technology engineering and the M.Sc. degree (Hons.) in telecommunications engineering from the University of Salento, Lecce, Italy, and the Ph.D. degree in electrical and electronic engineering from University College Cork (UCC), Cork, Ireland. Since April 2012, he has been with the Wireless Sensor Networks Group, Tyndall National Institute, UCC, where he is currently a Senior Researcher and a Team Leader responsible for leading a research team in the wearable and data analytics area. He has authored more than 80 articles in international journals and conference proceedings. He has received over 1.1 million euro in grant funding as a Principal Investigator and a Co-Principal Investigator. Since joining the Tyndall National Institute, he has managed and successfully led over 25 industrial and research-oriented projects focused on his main research interests on wearable technologies for healthcare and well-being, human motion analysis in sports and clinical populations, digital health, physiological monitoring, signal processing, edge analytics, and machine learning. Further contributions deal with radio frequency identification (RFID) technology and antenna design, ultrawideband localization systems for indoor applications, sensor calibration, and industry 4.0.