Autism Future Classiciation using Video in Python Projects

Autism Future Classiciation using Video in Python Projects
Abstract
Early diagnosis of Autism Spectrum Disorder (ASD) is critical for timely intervention and support. This project presents an Autism Future Classification System using Video Analysis in Python, which leverages computer vision and machine learning techniques to analyze behavioral cues from video recordings and predict the likelihood of autism in individuals. The system processes video data to extract facial expressions, eye gaze patterns, body movements, and gestures, which are then used as features for predictive modeling. Python libraries such as OpenCV, Mediapipe, TensorFlow/Keras, Scikit-learn, and Pandas are used for video processing, feature extraction, model training, and evaluation. By enabling automated analysis of video behavior, the system provides an effective tool for supporting clinicians in early detection and future classification of autism risk.

Existing System
Existing systems for autism detection largely rely on manual observation, questionnaires, and standardized behavioral assessments such as ADOS or ADI-R. While effective, these methods are subjective, time-consuming, and require trained professionals. Some automated approaches analyze eye-tracking or single-frame images, but they fail to capture dynamic behavioral patterns over time. Video-based analysis is underutilized, and most current systems do not integrate deep learning techniques for temporal modeling of gestures, expressions, and interactions. Consequently, early prediction and future classification of autism risk remain limited, especially for large-scale or continuous monitoring scenarios.

Proposed System

The proposed system introduces a Python-based video analysis framework for autism prediction and future classification. The system first captures video recordings of an individual, and computer vision techniques such as pose estimation, facial landmark detection, and gaze tracking are applied to extract behavioral features. Temporal patterns in gestures, eye movements, and expressions are analyzed using deep learning models such as LSTM (Long Short-Term Memory) networks or 3D CNNs to account for sequential dependencies. Extracted features are then input into machine learning classifiers like Random Forest, SVM, or Neural Networks to predict the likelihood of autism and classify future risk levels. The system can provide visualizations of key behavioral metrics and generate reports to support clinicians. By combining video analysis, deep learning, and predictive modeling, this approach offers an automated, scalable, and accurate solution for early autism detection and future risk classification.

Customer Reviews (0)

Share Your Experience

Rating

Your Comment

Submit Review Cancel

No reviews yet. Be the first!