Automated Detection Approaches to Autism Spectrum Disorder Based on Human Activity Analysis: A Review

Rahman, Sejuti; Ahmed, Syeda Faiza; Shahid, Omar; Arrafi, Musabbir Ahmed; Ahad, Md. Atiqur Rahman

doi:10.1007/s12559-021-09895-w

Cited by 9 publications

(2 citation statements)

References 115 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A recent review of automated detection approaches for ASD using Human Activity Analysis [20] categorises the literature into three main areas; gaze analysis, repeated behaviour, and abnormal gait detection. The review also refers to Functional Magnetic Resonance Imaging (fMRI) studies where images of the brain obtained using MRI are used to detect ASD.…”

Section: B Classification and Machine Learningmentioning

confidence: 99%

Encoding Kinematic and Temporal Gait Data in an Appearance-Based Feature for the Automatic Classification of Autism Spectrum Disorder

Henderson,

Yogarajah,

Gardiner

et al. 2023

IEEE Access

View full text Add to dashboard Cite

In appearance-based gait analysis studies, Gait Energy Images (GEI) have been shown to be an effective tool for human identification and gait pathology detection. In addition, model-based studies found kinematic and spatio-temporal features to be useful for gait recognition and Autism Spectrum Disorder (ASD) classification. Adapting the GEI to focus on the strong ASD features would improve the early screening of ASD by allowing the use of powerful appearance-based classifiers such as Convolutional Neural Networks (CNN). This paper introduces an enhanced GEI, by averaging images from a video sequence to produce a single image but by retention of a person's joint positions only, instead of the full body silhouettes. Depth is encoded into the binary images before they are averaged using colour mapping, a technique used in the Chrono-Gait Image. The Joint Energy Image (JEI) therefore embeds both the temporal and depth information of the joints into a 2D image. The image was preprocessed using Principal Component Analysis before being applied to a Multi-Layer Perceptron, and a Random Forest classifier. The JEI was also applied to a CNN directly. Accuracy was improved by using a Test Time Augmentation (TTA) measure where the original and augmented JEIs were used in a voting system to obtain a prediction. The CNN achieved a TTA accuracy of 95.56% when trained on a primary dataset of 100 subjects (50 with ASD and 50 that are typically developed), and 80% TTA accuracy on a secondary dataset of 20 subjects (10 ASD and 10 typically developed) across multiple tests, thus showing the viability of both the CNN and the JEI to compete with state-of-the-art feature sets and performances.

show abstract

Section: B Classification and Machine Learningmentioning

confidence: 99%

Encoding Kinematic and Temporal Gait Data in an Appearance-Based Feature for the Automatic Classification of Autism Spectrum Disorder

Henderson,

Yogarajah,

Gardiner

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…It offers a potent technique for creating and locating social attentional biomarkers in complex disorders like ASD. Like several biomarker technologies, ET-related biomarkers for ASD could develop numerous contexts of use [9], for example, as response biomarkers, diagnostic, prognostic, or predictive. Additionally, BIOMARKERS may act as diagnostic enrichment biomarkers to reduce study population heterogeneity, enabling a more accurate assessment of interference in small homogeneous samples [10].…”

Section: Introductionmentioning

confidence: 99%

Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

Thanarajan,

Alotaibi,

Rajendran

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Autism spectrum disorder (ASD) can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics, like changes in behavior, social disabilities, and difficulty communicating with others. Eye tracking (ET) has become a useful method to detect ASD. One vital aspect of moral erudition is the aptitude to have common visual attention. The eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early detection. Eye-tracking data can offer insightful information about the behavior and thought processes of people with ASD, but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD detection. It operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with autism. The purpose of this research is to use deep learning to identify autistic disorders based on eye tracking. The Chaotic Butterfly Optimization technique is used to identify this specific disturbance. Therefore, this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning (ETASD-CBODL) technique. The presented ETASD-CBODL technique mainly focuses on the recognition of ASD via the ET and DL models. To accomplish this, the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested AREASS. In addition, the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter optimization. Finally, the long-shortterm memory (LSTM) model is exploited for the recognition and classification of ASD. To assess the performance of the ETASD-CBODL technique, a series of simulations were performed on datasets from the figure-shared data repository. The experimental values of accuracy (99.29%), precision (98.78%), sensitivity (99.29%) and specificity (99.29%) showed a better performance in the ETASD-CBODL technique over recent approaches.

show abstract

Towards Explainable and Privacy-Preserving Artificial Intelligence for Personalisation in Autism Spectrum Disorder

Mahmud

Kaiser

Rahman

et al. 2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Automated Detection Approaches to Autism Spectrum Disorder Based on Human Activity Analysis: A Review

Cited by 9 publications

References 115 publications

Encoding Kinematic and Temporal Gait Data in an Appearance-Based Feature for the Automatic Classification of Autism Spectrum Disorder

Encoding Kinematic and Temporal Gait Data in an Appearance-Based Feature for the Automatic Classification of Autism Spectrum Disorder

Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

Towards Explainable and Privacy-Preserving Artificial Intelligence for Personalisation in Autism Spectrum Disorder

Contact Info

Product

Resources

About