@conference{Rusli2016226,
title = {Non-invasive assessment of affective states on individual with autism spectrum disorder: A review},
author = {N B Rusli and S N Sidek and H Md Yusof and M H Abd Latif},
editor = {Ahmad Usman M Y J Ibrahim F. Mohktar M.S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952767451&doi=10.1007%2f978-981-10-0266-3_47&partnerID=40&md5=f4aafb2216ef5c9d03ae7a1db352e4bd},
doi = {10.1007/978-981-10-0266-3_47},
issn = {16800737},
year = {2016},
date = {2016-01-01},
journal = {IFMBE Proceedings},
volume = {56},
pages = {226-230},
publisher = {Springer Verlag},
abstract = {Individuals with Autism Spectrum Disorder (ASD) are identified as a group of people who have social interaction and communication impairment. They have difficulty in producing speech and explaining what they meant. They also suffer from emotional or cognitive states requirement that stance challenges to their interest in communicating and socializing. Hence, it is vital to know their emotion to help them develop better skills in social interaction. Emotion can be derived from affective states and can be detected through physical reaction and physiological signals. There are numerous known modalities available to detect the affective states either through invasive and non-invasive methods. In order to evaluate the affective states of individuals with ASD, amongst the methods used are through electrodermal activity (EDA), electromyographic (EMG) activity, and cardiovascular activity (ECG) and blood flow analyses. Though considered non invasive, these methods require sensor to be patched on to the skin causing discomfort to the subjects and might distract their true emotion. We propose non-invasive methods which is also contactless to address the problem to detect emotion of individual with ASD that is through thermal imaging. Through the impact of cutaneous temperature in blood flow, thermal imprint is radiated and can be detected in this method. To date, no research has been reported of the use of thermal imaging analysis of facial skin temperature on the individuals with ASD. In this paper we will justify the method and also discuss the merits and demerits of other methods. © International Federation for Medical and Biological Engineering 2016.},
note = {cited By 1},
keywords = {Affective State, Autism, Biomedical Engineering, Blood, Diseases, Emotion, Facial Expression, Hemodynamics, Infrared Imaging, Noninvasive Medical Procedures, Physiological Signals, Physiology, Signal Detection, Skin, Social Sciences},
pubstate = {published},
tppubtype = {conference}
}

@conference{KhazaalShams2011653,
title = {Characterizing autistic disorder based on principle component analysis},
author = {W Khazaal Shams and A W Abdul Rahman},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84855644760&doi=10.1109%2fISIEA.2011.6108797&partnerID=40&md5=c486566e2d7ff404d830704c0b404067},
doi = {10.1109/ISIEA.2011.6108797},
isbn = {9781457714184},
year = {2011},
date = {2011-01-01},
journal = {2011 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2011},
pages = {653-657},
abstract = {Autism is often diagnosed during preschool or toddled age. This diagnosis often depends on behavioral test. It is known that individuals with autism have abnormal brain signals different from typical persons yet this difference in signals is slight that it is often difficult to distinguish from the normal. However, Electroencephalogram (EEG) signals have a lot of information which reflect the behavior of brain functions which therefore captures the marker for autism, help to early diagnose and speed the treatment. This work investigates and compares classification process for autism in open-eyed tasks and motor movement by using Principle Component Analysis (PCA) for feature extracted in Time-frequency domain to reduce data dimension. The results show that the proposed method gives accuracy in the range 90-100% for autism and normal children in motor task and around 90% to detect normal in open-eyed tasks though difficult to detect autism in this task. © 2011 IEEE.},
note = {cited By 6},
keywords = {Autism, Brain Function, Brain Signals, Classification Process, Data Dimensions, Diseases, Electroencephalogram Signals, Electroencephalography, Frequency Domain Analysis, Industrial Electronics, Motor Movements, Motor Tasks, PCA, Principal Component Analysis, Signal Detection, Time Frequency Domain},
pubstate = {published},
tppubtype = {conference}
}