Innovative Application of RFID Systems to Special Education Schools

Innovation is a new way of doing something. It may be incremental, radical, or revolutionary changes in thinking, products, processes, or organizations. Different from invention, which is an idea made manifest, innovation is ideas applied successfully. In this work, we strive to apply innovative Radio Frequency Identification (RFID) systems to special education school campus because in this modern age of science and technology, there still exists a wide digital gap in special education schools such that they have not yet benefited from technology advancements such as RFID. Supported by the ministry of education in Taiwan, we successfully designed and deployed RFID technology to the campus of a special education school at Chiayi in Taiwan. Though the technology was applied to eight different use case scenarios, we will focus on five of the more innovative ones in this work, including student temperature monitoring (STM), body weight monitoring (BWM), garbage disposal monitoring (GDM), mopping course recording (MCR), and campus visitor monitoring (CVM). Both active and passive tags and readers were employed to implement these five systems within the same campus. The benefits obtained from these systems by the students, teachers, and administrators were three-folds. First, student health monitoring through STM and BWM systems allowed the teachers and administration real-time control over changing health conditions that significantly affects such students. Second, course monitoring and recording through GDM and MCR allowed teachers to easily grasp and tune the learning curve of each student and also to implement a more guided training based on past learning efforts. Last but not least, campus safety monitoring through CVM allowed the administration to monitor the location of visitors in the campus and thus safeguard the students and teachers from dangerous or troublesome visitors. Novel techniques and creative methods were employed in the five systems, including tempera- - ture correction algorithm in STM, BMI-based weight tuning strategy in BWM, multiple route-tracking in GDM, learning improvement through history analysis in MCR, and face detection in CVM. The project was successfully deployed and is currently in use by the Chiayi School of Special Education which has more than 300 students and 150 administration staff and faculty.