@conference{Hasan20192436,
title = {ANN and SVM Classifiers in Identifying Autism Spectrum Disorder Gait Based on Three-Dimensional Ground Reaction Forces},
author = {C Z C Hasan and R Jailani and N M Tahir},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063202256&doi=10.1109%2fTENCON.2018.8650468&partnerID=40&md5=c697d0c43ebd77d76d74cb3726872f42},
doi = {10.1109/TENCON.2018.8650468},
issn = {21593442},
year = {2019},
date = {2019-01-01},
journal = {IEEE Region 10 Annual International Conference, Proceedings/TENCON},
volume = {2018-October},
pages = {2436-2440},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Autism spectrum disorder (ASD) is a complex and lifelong neurodevelopmental condition that occurs in early childhood and is associated with unusual movement and gait disturbances. An automated and accurate recognition of ASD gait provides assistance in diagnosis and clinical decision-making as well as improving targeted treatment. This paper explores the use of two well-known machine learning classifiers, artificial neural network (ANN) and support vector machine (SVM) in distinguishing ASD and normal gait patterns based on prominent gait features derived from three-dimensional (3D) ground reaction forces (GRFs) data. The 3D GRFs data of 30 children with ASD and 30 typically developing children were obtained using two force plates during self-determined speed of barefoot walking. Time-series parameterization techniques were applied to the 3D GRFs waveforms to extract the significant gait features. The stepwise method of discriminant analysis (SWDA) was employed to determine the dominant GRF gait features in order to classify ASD and typically developing groups. The 10-fold cross-validation test results indicate that the ANN model with three dominant GRF input features outperformed the kernel-based SVM models with 93.3% accuracy, 96.7% sensitivity, and 90.0% specificity. The findings of this study demonstrate the reliability of using the 3D GRF input features, in combination with SWDA feature selection and ANN classification model as an appropriate method that may be beneficial for the diagnosis of ASD gait as well as for evaluation purpose of the treatment programs. © 2018 IEEE.},
note = {cited By 0},
keywords = {10 Fold Cross Validation, 3D Modeling, Autism Spectrum Disorders, Biophysics, Clinical Decision Making, Computer Aided Diagnosis, Decision Making, Discriminant Analysis, Diseases, Gait Analysis, Gait Classification, Ground Reaction Forces, Neural Networks, Parameterization Techniques, Pattern Recognition, Petroleum Reservoir Evaluation, Program Diagnostics, Support Vector Machines, Targeted Treatment, Three-Dimensional},
pubstate = {published},
tppubtype = {conference}
}

@conference{Jamil2015342,
title = {Gait-Based Emotion Detection of Children with Autism Spectrum Disorders: A Preliminary Investigation},
author = {N Jamil and N H M Khir and M Ismail and F H A Razak},
editor = {Miskon M F Yussof H.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962833568&doi=10.1016%2fj.procs.2015.12.305&partnerID=40&md5=6893678f1ed83b87147ff9183b94428b},
doi = {10.1016/j.procs.2015.12.305},
issn = {18770509},
year = {2015},
date = {2015-01-01},
journal = {Procedia Computer Science},
volume = {76},
pages = {342-348},
publisher = {Elsevier B.V.},
abstract = {With the disturbing increase of children with Autism Spectrum Disorder (ASD) in Malaysia, a lot of efforts and studies are put forward towards understanding and managing matters related to ASD. One way is to find means of easing the social communications among these children and their caretakers, particularly during intervention. If the caretaker is able to comprehend the children emotional state of mind prior to therapy, some sort of trust and attachment will be developed. However, regulating emotions is a challenge to these children. Nonverbal communication such as facial expression is difficult for ASD children. Therefore, we proposed the use of walking patterns (i.e. gait) to detect the type of emotions of ASD children. Even though using gait for emotion recognition is common among normal individuals, none can be found done on children with ASD. Thus, the aim of this paper is to conduct a preliminary review on the possibilities of carrying out gait-based emotion detection among ASD children with regards to the emotional types, gait parameters and methods of gait data acquisition. © 2015 The Authors.},
note = {cited By 4},
keywords = {Autism Spectrum Disorders, Children with Autism, Data Acquisition, Diseases, Emotion, Emotion Detection, Emotion Recognition, Emotional State, Facial Expression, Gait Analysis, Intelligent Control, Nonverbal Communication, Pattern Recognition, Robotics, Smart Sensors, Social Communications, Speech Recognition},
pubstate = {published},
tppubtype = {conference}
}