Hand gesture recognition plays a crucial role in the field of human–computer interaction (HCI). In terms of the multimodal sensing of hand gestures, the A-mode ultrasound (AUS) signal is far less investigated, especially for dynamic hand gestures, than its counterparts, such as surface electromyography (sEMG). In this article, we explore the recognition of dynamic hand gestures by proposing an AUS-based deep learning algorithm that codes time correlation in the long short-term memory (LSTM) framework. First, a dynamic handwritten numbers 0 through 9 dataset was created and recorded. Then, after preprocessing the data, we propose an algorithm based on the deep learning framework. Also, we designed two different strategies that used two different structures for comparison. Finally, through experiments, the accuracy of different deep learning structures [convolutional neural network (CNN) and LSTM] and traditional feature extraction [support vector machine (SVM)] on dynamic gesture recognition of ultrasonic (US) signals are compared, and we prove that LSTM has better performance. The experiment results prove that the proposed method achieves 89.5% accuracy, which outperforms its counterparts. It paves the way for potential HCI applications involving dynamic hand gestures. It is anticipated that more uses of dynamic gesture recognition will be discussed in the future to bring the research into real-life applications.

Copious evidence shows that impaired visual–motor integration (VMI) is intrinsically linked to the core deficits of autism spectrum disorder (ASD) and associated with an anomalous social capability. Therefore, an effective evaluation method of visual–motor behaviour can provide meaningful insight into the evaluation of VMI towards social capability. The current pilot study aims to explore the appropriate quantified metrics for evaluating VMI ability based on a hand-drawn imitation protocol. First, a simple and interesting hand-drawn protocol was designed, and six healthy participants were recruited to perform the task. Then, based on the collected hand–eye behaviour data, several metrics were applied to infer the participant’s social capability and VMI in engagement and visual–motor complexity based on hand–eye properties with Hausdorff distance and cross-recurrence quantification analysis (CRQA). Finally, those quantified metrics were verified through statistical significance. This study proposed a set of quantitative metrics to construct a comprehensive VMI evaluation, including outcome and progress measures. The results revealed the proposed method as a directly interpretable indicator providing a promising computational framework and biomarker for VMI evaluation, paving the way for its future use in ASD diagnosis and guiding intervention.