Affective state recognition of an individual is based on the emotional cues, such as the activation level. Body expression is a modal able to convey emotions and can be used for autism diagnosis through the presence of stereotyped behaviors (SBs). These behaviors are atypical and repetitive movements of the body, which can be related to a low mental health condition. The development of systems able to both recognize SBs and inferring activation level can automatically aid some therapeutic approaches. In this paper, a computational model of low intrusiveness is proposed to infer activation levels from recognized SBs, Machine Learning Algorithms (MLAs) are for identifying the SBs and for determining the related activation levels. A metric performance is also proposed to evaluate the performance of MLAs considering the time for classification of the SBs, accuracy, and precision. For classifying the SBs, the Hidden Markov Models and Multilayer Perceptron presented the best performance than Support Vector Machine and Convolutional Neural Network. The Adaptive Neuro-Fuzzy technique based on the Fuzzy C-Means algorithm allowed one to determine and differentiate the activation levels of the stereotyped behaviors considered in the present study. The experiments were performed with non-autistic participants, here referred to as pseudo-autistic.

个人的情感状态识别是基于情感线索，例如激活水平。身体表达是一种能够表达情感的方式，可以通过存在刻板印象的行为（SBs）用于自闭症诊断。这些行为是身体的非典型和重复性运动，可能与心理健康状况低下有关。能够识别SB并推断激活水平的系统的开发可以自动辅助某些治疗方法。在本文中，提出了一种低侵入性的计算模型以从公认的SB推断出激活水平，机器学习算法（MLA）用于识别SB并确定相关的激活水平。考虑到SB的分类时间，准确性和精度，还提出了一种度量性能来评估MLA的性能。为了对SB进行分类，隐马尔可夫模型和多层感知器表现出比支持向量机和卷积神经网络更好的性能。基于模糊C均值算法的自适应神经模糊技术使人们能够确定和区分本研究中定型行为的激活水平。实验是在非自闭症参与者（此处称为伪自闭症）下进行的。基于模糊C均值算法的自适应神经模糊技术使人们能够确定和区分本研究中定型行为的激活水平。实验是在非自闭症参与者（此处称为伪自闭症）下进行的。基于模糊C均值算法的自适应神经模糊技术使人们能够确定和区分本研究中定型行为的激活水平。实验是在非自闭症参与者（此处称为伪自闭症）下进行的。