Syed Faris Syed Omar is currently the Head of ISN Tech responsible for R&D, innovation and commercialization of sports technology at the National Sports Institute of Malaysia (ISN). Syed Faris received his degree in Mechanical Engineering from University Technology of Malaysia (UTM) in 2002. Upon receiving his degree, he was immediately employed by DAG Technologies, a private company providing engineering solutions for automotive and aerospace industry in Malaysia. Syed Faris has a number of publications under his name focusing in the area of sports technology equipment and system development for real world application. As the current head of ISN Tech, Syed Faris has identified multiple new and emerging technologies and products from the industry to partner with and to bring it to the next level. Dr. Mohd Hasnun Arif Hassan earned his degree in Mechanical Engineering from the Technische Hoschschule Bingen, Germany in 2010. During the final year of his undergraduate study, he was offered a scholarship by Universiti Malaysia Pahang (UMP) to pursue a master's degree in Mechanical Engineering at the University of Malaya in Kuala Lumpur, which he graduated with distinction in 2012. His PhD research pertained to head injuries sustained by soccer players due to heading maneuvers. He completed his PhD in 2016, then continue to serve UMP as a senior lecturer. His research interests include finite element modelling of the interaction between human and sports equipment, instrumentation of sports equipment, and injury prevention particularly with regards to sports and traffic accidents. His work aims to apply engineering principles in sports not only to enhance the performance of an athlete, but also to prevent injuries. Dr. Alan Godfrey is an Associate Professor of Digital Health in the Department of Computer and Information Sciences at Northumbria University, Newcastle. He heads the Digital Health & Wellbeing research group. He is a Fellow of the Institute of Engineering and Technology and Senior Member if the Institute of Electrical and Electronic Engineers. His research includes algorithms for data science and analytics in healthcare. This includes areas of artificial intelligence, data mining and multidimensional signal processing. He has an applied research interest in healthcare, often utilising wearable technology for gait analysis to inform clinical practice. Dr. Alex Casson is a Reader in the Materials, Devices and Systems division of the Department of Electrical and Electronic Engineering at the University of Manchester; Visiting Reader in the School of Medicine at the University of Leeds; Bioelectronics technology platform lead for the EPSRC Henry Royce Institute for advanced materials; and a Fellow of the Alan Turing Institute for data science and artificial intelligence. His research focuses on non-invasive bioelectronic interfaces: the design and application of wearable sensors, and skin-conformal flexible sensors, for human body monitoring and data analysis from highly artefact prone naturalistic situations Dr Anwar P.P. Abdul Majeed graduated with a first-class honours B.Eng. in Mechanical Engineering from Universiti Teknologi MARA (UiTM), Malaysia. He obtained an MSc. in Nuclear Engineering from Imperial College London, United Kingdom. He then received his PhD in Rehabilitation Robotics from the Universiti Malaysia Pahang (UMP). He is currently serving as an Associate Professor at the School of Robotics, XJTLU. Prior to joining XJTLU, he was a Senior Lecturer (Assistant Professor) and the Head of Programme (Bachelor of Manufacturing Engineering Technology (Industrial Automation)) at the Faculty of Manufacturing and Mechatronics Engineering Technology, UMP. He is also currently serving as an adjunct lecturer at UCSI University, Malaysia. Dr Anwar is also a Visiting Research Fellow at EUREKA Robotics Centre, Cardiff Metropolitan University, UK. His research interest includes rehabilitation sciences, computational and applied mechanics, renewable and nuclear energy, sports engineering and performance analysis, as well as the application of machine learning.
