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All Journal International Journal of Electrical and Computer Engineering Perfecting a Video Game with Game Metrics Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science ABDI SABHA (Jurnal Pengabdian kepada Masyarakat)
Nooritawati Md Tahir
Universiti Teknologi MARA
Religion Arts Humanities Computer Science & IT Education Electrical & Electronics Engineering Social Sciences

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Journal : Perfecting a Video Game with Game Metrics

 1 
Autism Spectrum Disorders Gait Identification Using Ground Reaction Forces Che Zawiyah Che Hasan; Rozita Jailani; Nooritawati Md Tahir; Rohilah Sahak
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 2: June 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i2.6143

Abstract

 Autism spectrum disorders (ASD) are a permanent neurodevelopmental disorder that can be identified during the first few years of life and are currently associated with the abnormal walking pattern. Earlier identification of this pervasive disorder could provide assistance in diagnosis and establish rapid quantitative clinical judgment. This paper presents an automated approach which can be applied to identify ASD gait patterns using three-dimensional (3D) ground reaction forces (GRF). The study involved classification of gait patterns of children with ASD and typical healthy children. The GRF data were obtained using two force plates during self-determined barefoot walking. Time-series parameterization techniques were applied to the GRF waveforms to extract the important gait features. The most dominant and correct features for characterizing ASD gait were selected using statistical between-group tests and stepwise discriminant analysis (SWDA). The selected features were grouped into two groups which served as two input datasets to the k-nearest neighbor (KNN) classifier. This study demonstrates that the 3D GRF gait features selected using SWDA are reliable to be used in the identification of ASD gait using KNN classifier with 83.33% performance accuracy. 
Co-Authors Abdul Rahim Bin Ridzuan Abdullah Ahmad Ihsan Mohd Yassin Aisyah Hartini Jahidin Arridina Susan Silitonga Che Zawiyah Che Hasan Cholish Fadhlan Hafizhelmi Kamaruzaman Hana’ Abd Razak Haq, Moh Zainul M. Ahmed M. Saleh Maharani Putri Megat Syahirul Amin Megat Ali Mohammad Farid Saaid Mohd Khairi Nordin Mohd Nasir Taib Pius Fernando Hutauruk Priansus Rhein Rumahorbo Rohilah Sahak Rohilah Sahak Rozita Jailani Suprianto