Texas A&M University, a Qatar Foundation partner university, and Rheinmetall Barzan Advanced Technologies have signed a Memorandum of Understanding (MoU) to collaborate on exploring, investigating, and developing joint activities in the fields of engineering, science, and technology. Per the MOU, both entities will explore research and development projects that provide solutions to local challenges and investigate avenues for strengthening collaboration and furthering academic excellence in Qatar, all in alignment with national priorities toward becoming a knowledge-based economy and supporting Qatar National Vision 2030.
Andre Conradie, CEO of Rheinmetall Barzan Advanced Technologies, said, “This MoU will support the establishment of new relations towards fruitful cooperation between RBAT and Texas A&M University in Qatar. This partnership will encourage the development of technological and operational capabilities to enhance military protection and empower key stakeholders in the defense industry. RBAT looks forward to its continued collaboration with Texas A&M University.”
As part of the MoU, the two entities will also develop collaborative workshops, seminars, training, conferences and other joint programs to further academic excellence, exploring joint projects using the state-of-the-art facilities and expertise of researchers and engineers at both organizations.
Dr. César Octavio Malavé, dean of Texas A&M University at Qatar, said, “We are not only committed to building engineering leaders of the future but also to bring the remarkable expertise of our people to develop innovative engineering solutions for the real-world challenges we are facing.”
As an imminent need, teams led by Dr. Khalid Qaraqe at Texas A&M in Qatar and Mourad Elmaalaoui, the Chief Technology Officer at Rheinmetall Barzan Advanced Technologies, are exploring the potential of developing a noninvasive, proactive prototype for real-time Fatigue Monitoring and Prediction (FMP) system, which will track typical signs of tiredness, such as tremor, body temperature, heart rate and blood oxygen saturation. The framework, highly justified by a need to safeguard military operations, aims to distinguish users’ early signs of tiredness and exhaustion; it predicts the onset of fatigue using motion and physiological data to prevent users from unfavorable fatigue outcomes such as delayed decision making and fatigue-related accidents.
The researchers will use advanced artificial intelligence/machine learning techniques to alert the user when the first signs of fatigue appear. The suggested approach will help address a fundamental and pragmatic requirement for care for the user’s physical, mental, and emotional well-being.
Dr. Malavé also said, “Through our partnership with Rheinmetall Barzan Advanced Technologies, we are looking to develop a prototype which can detect fatigue earlier, thus contributing to reducing work-related accidents, stress, and lack of productivity and motivation.”
Source: Texas A&M University at Qatar