Project ID: 189
MUHAMMAD SHARIFF BIN MOHD ZAIN - CS230
2017412166
Supervisor: MUHAMMAD ATIF RAMLAN
Examiner: AHMAD NAZMI BIN FADZAL
HUMAN ACTION RECOGNITION FOR AUTISM ANALYSIS USING K-NEAREST NEIGHBORS ALGORITHM
Abstract
MUHAMMAD SHARIFF BIN MOHD ZAIN
CS2306B
Everything needs to be done rapidly like healthcare facilities. As there are many forms of illness and health condition that occur in the world, in order to prevent, diagnose and manage it, the need for good health care facilities is important for all people around the world. Autism Spectrum Disorder (ASD) is one of the most common disorders in children and it is very important to identify the condition before it is too late (Cohn, Miller, & Tickle-Degnen, 2000). With the advancement of existing technology, the technology can be used to assist the therapist in their work. Various technologies' uses show that it supports the process of diagnosing and evaluating diseases and disorders, but none of them are linked to ASD. To address this problem, a program has been proposed to detect the gesture using the K-Nearest Neighbors (KNN) Algorithm, one of the machine learning techniques. The program was developed to recognize the type of movement that is being made to help the doctor interpret gestures. The classifier tests that are being established are carried out by an accuracy check. This framework needs to be developed for future research by using dataset linked to the ASD.