Autism spectrum disorder and cerebral palsy are called developmental disorders that affect the brain development, communication, and behaviour of a child or an adult. Individuals with Cerebral palsy can also display symptoms of autism. Both conditions have varying degrees of severity, which can make it difficult to form a clear diagnosis. This research paper proposes the model-free green environment for the prediction of the above-mentioned disorders by doing gait analysis only with the camera. The new intelligent algorithm CAGLearner (cognitive analysis for gait) works on the standards of graphical extreme machines. CAGLearner uses the new powerful algorithm called bat optimized ELM for classification, which is then related with the prevailing machine learning algorithms such as artificial neural networks (ANN), support vector machines (SVM), and random forest (RF) algorithms in which the accuracy, sensitivity, and response time were analyzed. In terms of prediction time and precision, the model provided in this paper also yields more benefits.
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机译：自闭症谱系障碍和脑瘫被称为发育障碍，会影响儿童或成人的大脑发育、沟通和行为。脑瘫患者也可能表现出自闭症的症状。这两种情况都有不同程度的严重程度，这使得难以形成明确的诊断。本研究论文提出了一种无模型的绿色环境，用于仅使用相机进行步态分析来预测上述疾病。新的智能算法CAGLearner（步态认知分析）适用于图形极限机器的标准。CAGLearner 使用称为蝙蝠优化 ELM 的新型强大算法进行分类，然后将其与流行的机器学习算法相关联，例如人工神经网络 （ANN）、支持向量机 （SVM） 和随机森林 （RF） 算法，其中分析了准确性、灵敏度和响应时间。在预测时间和精度方面，本文提供的模型也产生了更多的收益。
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