The use of video-based monitoring systems for estimating and monitoring physiological and mental health status offers a non-invasive, continuous, and dynamic alternative to traditional methods reliant on physical sensors or intrusive procedures. This study investigates the application of computer vision and machine learning techniques, particularly the Histogram of Oriented Gradients (HOG) and Optical Flow (OF) feature descriptors, coupled with Support Vector Machine (SVM) classifiers, to analyze visual data from IP webcams and thermal cameras. The workflow involves image preprocessing, feature extraction, and classification stages. By resizing images to a standard resolution and extracting key features, the system can recognize behavioral patterns in autistic children, demonstrating significant improvements in classification accuracy. The proposed method was evaluated using the Autismdata.Net dataset and compared with existing techniques, achieving an accuracy of 88.60%, outperforming methods such as k-NN, pLSA, and Naïve Bayes. An ablation study further highlighted the effectiveness of combining HOG and OF features with a multi-class SVM, with a marked increase in accuracy over single feature approaches. Additionally, the system's capability to monitor physiological parameters such as body temperature variations using thermal imaging was demonstrated, offering valuable insights into the child's physical state without physical contact. This research underscores the potential of video-based systems to revolutionize healthcare delivery, particularly in remote monitoring and telemedicine contexts, providing a comprehensive view of an individual's health through continuous, non-invasive monitoring of vital signs and emotional states. The integration of advanced computer vision techniques into healthcare promises enhanced patient outcomes and more efficient healthcare practices.