School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China.
Experimental Teaching Center for Teacher Education, East China Normal University, Shanghai 200241, China.
Sensors (Basel). 2021 Jan 8;21(2):411. doi: 10.3390/s21020411.
The recognition of stereotyped action is one of the core diagnostic criteria of Autism Spectrum Disorder (ASD). However, it mainly relies on parent interviews and clinical observations, which lead to a long diagnosis cycle and prevents the ASD children from timely treatment. To speed up the recognition process of stereotyped actions, a method based on skeleton data and Long Short-Term Memory (LSTM) is proposed in this paper. In the first stage of our method, the OpenPose algorithm is used to obtain the initial skeleton data from the video of ASD children. Furthermore, four denoising methods are proposed to eliminate the noise of the initial skeleton data. In the second stage, we track multiple ASD children in the same scene by matching distance between current skeletons and previous skeletons. In the last stage, the neural network based on LSTM is proposed to classify the ASD children's actions. The performed experiments show that our proposed method is effective for ASD children's action recognition. Compared to the previous traditional schemes, our scheme has higher accuracy and is almost non-invasive for ASD children.
刻板动作的识别是自闭症谱系障碍（ASD）的核心诊断标准之一。然而，它主要依赖于家长访谈和临床观察，这导致了冗长的诊断周期，并使 ASD 儿童无法及时得到治疗。为了加快刻板动作的识别过程，本文提出了一种基于骨骼数据和长短期记忆网络（LSTM）的方法。在我们方法的第一阶段，使用 OpenPose 算法从 ASD 儿童的视频中获取初始骨骼数据。此外，还提出了四种去噪方法来消除初始骨骼数据的噪声。在第二阶段，通过匹配当前骨骼和上一骨骼之间的距离，跟踪同一场景中的多个 ASD 儿童。在最后阶段，提出了基于 LSTM 的神经网络来对 ASD 儿童的动作进行分类。所进行的实验表明，我们提出的方法对于 ASD 儿童的动作识别是有效的。与之前的传统方案相比，我们的方案具有更高的准确性，并且对 ASD 儿童几乎没有侵入性。
