IEEE Trans Neural Syst Rehabil Eng. 2022;30:2623-2629. doi: 10.1109/TNSRE.2022.3205026. Epub 2022 Sep 19.
A-mode ultrasound has the advantages of high resolution, easy calculation and low cost in predicting dexterous gestures. In order to accelerate the popularization of A-mode ultrasound gesture recognition technology, we designed a human-machine interface that can interact with the user in real-time. Data processing includes Gaussian filtering, feature extraction and PCA dimensionality reduction. The NB, LDA and SVM algorithms were selected to train machine learning models. The whole process was written in C++ to classify gestures in real-time. This paper conducts offline and real-time experiments based on HMI-A (Human-machine interface based on A-mode ultrasound), including ten subjects and ten common gestures. To demonstrate the effectiveness of HMI-A and avoid accidental interference, the offline experiment collected ten rounds of gestures for each subject for ten-fold cross-validation. The results show that the offline recognition accuracy is 96.92% ± 1.92%. The real-time experiment was evaluated by four online performance metrics: action selection time, action completion time, action completion rate and real-time recognition accuracy. The results show that the action completion rate is 96.0% ± 3.6%, and the real-time recognition accuracy is 83.8% ± 6.9%. This study verifies the great potential of wearable A-mode ultrasound technology, and provides a wider range of application scenarios for gesture recognition.
A 模式超声在预测灵巧手势方面具有高分辨率、易于计算和低成本的优势。为了加速 A 模式超声手势识别技术的普及，我们设计了一个人机界面，可以实时与用户交互。数据处理包括高斯滤波、特征提取和 PCA 降维。选择了 NB、LDA 和 SVM 算法来训练机器学习模型。整个过程使用 C++编写，以实时分类手势。本文基于 HMI-A（基于 A 模式超声的人机界面）进行了离线和实时实验，包括 10 名受试者和 10 个常见手势。为了证明 HMI-A 的有效性并避免意外干扰，离线实验对每个受试者进行了十轮手势采集，进行了十折交叉验证。结果表明，离线识别准确率为 96.92%±1.92%。实时实验通过四个在线性能指标进行评估：动作选择时间、动作完成时间、动作完成率和实时识别准确率。结果表明，动作完成率为 96.0%±3.6%，实时识别准确率为 83.8%±6.9%。这项研究验证了可穿戴 A 模式超声技术的巨大潜力，为手势识别提供了更广泛的应用场景。
