Traqbeat Technologies PC

Traqbeat is a fast-growing company with acknowledged excellence in conducting high-level research and development of innovative systems and services. Its activities focus on the development of methods and tools in the area of medical sensing and informatics, e-health, m-Health, and Internet of Medical Things. Traqbeat focuses on the development of a novel adaptable wearable health tracking device including quantitative health risk assessment methods and tools focusing on the wider area of medical sensing. Traqbeat’s core technology consist of a proprietary – in house – patented novel wearable sensor and methods capable of continuously or intermittently measuring and recording of multiple health related biomarkers including Heart Rate/ Variability, SpO2, Blood Pressure and more. Its goal is to develop and apply novel ICT technologies in the wider context of personalised, predictive and preventive medicine aiming at the optimal management of diseases and the development of clinical decision support systems, optimisation of diagnosis and disease combating tools and models for enhancing biomedical knowledge discovery. The company realizes its goals through strong interaction between interdisciplinary engineers, product and operating managers with high R&D experience. Traqbeat’s founders have long experience in implementing national and European projects, high level technological competence and thorough knowledge and understanding of the field.

Role within REALM

Traqbeat’s expertise is on modern computational resources, smart biomedical sensors, mobile and wireless technology platforms, Machine and Deep learning-powered solutions, as well as service-oriented infrastructures for specialized embedded systems and ubiquitous monitoring applications.

More specifically, Traqbeat’s role in the project focuses on:

• the development and validation of medical grade smart sensing devices for the Internet of Medical Things, including MDR certification and legal frameworks,
• the design and technical architecture of the system, implementation and realization of specific subsystems,
• security, privacy and trust in medical data management and sharing,
• system integration and middleware deployment,
• AI-driven modelling and synthetic data identification,
• Pilot design, dissemination and exploitation efforts, including end users support and training.