Authors: Samiya Ali Zaidi
Posted: Tue, August 20, 2024 - 11:55:00
To get started, we partnered with the Autism Spectrum Disorder Welfare Trust (ASDWT) in Pakistan to create a unique dataset. It will include eye-tracking data from Pakistani children, reflecting a diverse range of ages, genders, and other demographic factors. The diversity in our dataset is crucial because it helps ensure that our model is robust and can be generalized across different populations. Collecting this data will be a collaborative effort, involving the ASDWT, parents, and other stakeholders who provided informed consent and valuable insights. Once the dataset is collected, we will prepare it for analysis. This step includes pre-processing the data, removing noise, and normalizing it to ensure consistency. These features will play a vital role in training our computer vision-based model to detect patterns that may indicate ASD.
In addition to eye-tracking data, we recognize the importance of incorporating textual inputs to improve the accuracy of our model. This step involves gathering information from parental reports, medical histories, and other relevant checklists. By integrating this textual data, we can provide our model with a more comprehensive understanding of each child's development, allowing for a more accurate detection of ASD-related patterns. Textual inputs add context to the eye-tracking data, helping identify correlations between visual behavior and other developmental cues. This approach aligns with HCI's focus on creating systems that understand and respond to human behavior in meaningful ways. By combining visual and textual data, we aim to create a model that not only detects ASD with greater accuracy but also helps healthcare professionals and families make informed decisions about treatment and support.
The heart of our project lies in building and training a custom computer vision-based model. Given the complexity of ASD and the variability of eye-tracking patterns, we understand that a robust model is essential. To achieve this, we will split our dataset into three subsets: training, validation, and testing. The training subset, comprising approximately 70 percent of the data, is used to teach the model to recognize patterns. The validation subset (20 percent) helps us fine-tune and optimize the model's hyperparameters. Finally, the testing subset (10 percent) is where we evaluate the model's accuracy and generalizability. The training process involves applying state-of-the-art computer vision techniques to analyze the eye-tracking data. We will also use transformer-based architectures to explore different approaches to pattern recognition. By testing multiple architectures, we aim to determine which methods are most effective in identifying ASD-related patterns.
After training and testing the model, we moved on to the analysis phase. This step involves evaluating the model's performance using various metrics. We compared our results with existing studies to gauge the effectiveness of our approach and to identify areas for improvement. During this phase, we paid special attention to how variations in visual stimuli and experimental conditions influenced the model's accuracy. We also considered the potential impact of demographic factors, ensuring that our model could generalize across different groups. This analysis is crucial, as it helps us understand the limitations of our approach and provides insights into how we can refine the model to improve its reliability.
The findings from our project, as seen in the initial reports, will have the potential to significantly impact early ASD detection. By combining eye-tracking data with textual inputs, we can create a model that offers earlier and more accurate diagnoses, which can lead to more effective interventions. This directly benefits healthcare professionals and families by providing them with tools to support children with ASD promptly. Moreover, our work demonstrates the potential of Human-Computer Interaction in healthcare. By leveraging computer vision and deep learning, we can address complex problems like ASD diagnosis with innovative solutions. This approach has implications beyond ASD, showcasing how HCI can play a pivotal role in improving diagnostic processes for other neurological and developmental conditions.
As we continue to refine our model, we are excited about the possibilities ahead. Our findings could guide future research into applying HCI and computer vision to a broader range of diagnostic scenarios, ultimately leading to more reliable and efficient methods for early diagnosis. We believe this project is just the beginning of a journey toward a more inclusive and effective approach to healthcare. By incorporating HCI into the process, we're not just creating technology for technology's sake; we're creating solutions that can make a real difference in people's lives. And that's what makes this work truly exciting.
Posted in: on Tue, August 20, 2024 - 11:55:00
Samiya Ali Zaidi
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