Honorary Mentions at the IEEE ComSoc Student Competition 2019

The IEEE Communications Society holds an annual student competition, encouraging communications engineering students to expand their knowledge, test and showcase new skills, and inspire innovation. IEEE ComSoc Student Competition 2019 was held with theme ‘Communications Technology Changing the World Student Competition’. Two teams; AKMA Bionics and RcubeH from the Department of Electronic and Telecommunication Engineering were able to secure Honorary Mentions (being ranked among the top 15 in the world) at the IEEE ComSoc Student Competition 2019.

Team ‘AKMA Bionics’ comprised of 4 final year undergraduates, Ashwin De Silva, Malsha Perera, Kithmin Wickramasinghe and Asma Naim. Their project was titled ‘Wearable Cost Effective Wireless Dry Contact sEMG Sensor System for Controlling Digital Technologies’. Surface electromyograms (sEMG) is a biopotential recording technique by which we record a muscles’ electrical activity from the surface of the skin; which reflects the generation and propagation of Motor Unit Action Potentials. This project focuses on designing a dry contact electrode for acquiring sEMG signals from forearm, including the design and development of the interfacing systems and circuits for the muscle-computer interface (MCI), under a reasonable cost with the intention of creating a novel finger gesture based wireless interaction device using modern communication technologies. The sEMG signals acquired from the dry contact electrode system is used for real-time hand motion recognition. These recognised hand motions will be used to control different digital technologies such as a smart switch, a smart light bulb, a computer, a smartphone and most importantly a bionic arm. Current available devices for acquisition of sEMG have shortcomings in terms of design, speed, reusability and reconfigurability. The dry contact, stainless steel based electrode system presented by this team is designed in order overcome the shortcomings of the currently available sEMG acquisition systems.

Team ‘RcubeH’ comprised of 4 final year undergraduates, Ranjula Hettiarachchi , Rashinda Wijethunga, Ravindu Rashmin, Hashini De Silva. Their project was titled ‘Indoor Navigation for Visually Impaired’. At present there are over 30 million people around the world who are completely blind. Navigation at an unknown location has been one of their biggest challenges. Even though many outdoor navigation technologies have been invented to aid them in outdoor navigation, no widely adapted method has been launched to support the indoor navigation. Team RcubeH presents the idea of developing a method which uses only the proximity to a Bluetooth beacon and combines that information with mobile phone sensor data to navigate the visually impaired person along a certain route, so a major advantage of this method is number of beacons needed for a building can be reduced from a significant number. The team has developed filtering mechanisms and machine learning algorithms to obtain accurate distance estimation with high accuracy and robustness. Another advantage of the proposed method is the customizability, when the system is designed it will be adaptable to various buildings and various common features of a certain building without needing extra training/modification of algorithms. The beacons are placed strategically around limited points of interest around the building and use the mobile phones’ built-in sensors to navigate through these points of interest. The beacons are only used for marking a certain landmark and only the proximity (distance to a certain beacon) is used to for the navigation process. The main advantage of this approach is using a limited number of BLE beacons which reduces the cost by a large portion. Even though similar BLE proximity-based approaches have been taken by some researchers, the measures they have taken to increase the system’s accuracy and customizability is rather low. The team is intending to optimize the distance estimation process by implementing machine learning and filtering techniques and make the system customizable to different building environments. Once the system is developed it can be implemented in any building with a simple architectural design and a visually impaired person will be able to navigate to a certain destination inside the building. This system can be further improved to be used by the general public.