A Learning Machine for Cognitive Ability Improvement

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Often handicapped communities like Down syndrome, autism and mental disability are associated with some impairment of cognitive ability. Although some of their physical genetic limitations cannot be overcome, but proper training will improve their abilities. Early Intervention Program (EIP) which includes screening, analyzing and training program is an approach to improve the cognitive ability. In order to make the EIP works sufficiently, a learning machine has been developed. The machine is able to interact with user, analyze user’s ability level and formulate a proper training program. The generation of individual training program is done with intelligent algorithm based on supervised learning. The machine uses Radio Frequency Identification (RFID) technology as input unit and microcomputer as data processing unit. The interaction with user is done through audio visual display. This machine can screen and train the cognitive ability of children under 6 years old, includes sensing and processing abilities. The sensing ability includes auditory response, visual response, vestibular response, tactile sensitivity and temperature response. While the processing ability includes memory ability, reading ability, object arrangement ability and interested object response. The machine has been tested by normal and Down syndrome children for acceptability, functionality, effectiveness and consistency. Test result shows that the machine is reliable to be used in children’s learning process to improve their cognitive ability.


Keywords: Learning Machine, Cognitive Ability, Early Intervention Program, RFID Technology, Supervised Learning
Stream: Technology in Community
Presentation Type: Paper Presentation in English
Paper: A paper has not yet been submitted.


Dr. Eko Supriyanto

Head of Medical Electronic Laboratory, Faculty of Electrical Engineering, Universiti Teknologi Malaysia
Skudai, Johor, Malaysia

Dr.-Ing. Eko Supriyanto is a senior lecturer in Faculty of Electrical Engineering as well as Faculty of Biomedical Engineering and Health Sciences, Universiti Teknologi Malaysia. He has a Master degree in Biomedical Engineering and Doctor degree in Electronics from University of Federal Armed Forces Hamburg, Germany. His research areas are biomedical ultrasound, health care management system, and children development. He worked for several years as a product development manager in Duesseldorf and a lecturer in Hamburg, Germany. He has more than 20 publication papers and developed more than 10 electronic products.

Syee Chin Seow

Research Asistant, Faculty of Electrical Engineering, Universiti Teknologi Malaysia
Skudai, Johor, Malaysia


Ref: T08P0227