Wang Huihui, Ru Bo, Miao Xin, Gao Qin, Habib Masood, Liu Long, Qiu Sen
School of Intelligence and Electronic Engineering, Dalian Neusoft University of Information, Dalian 116023, China.
Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China.
Micromachines (Basel). 2023 Apr 27;14(5):947. doi: 10.3390/mi14050947.
Gesture recognition has found widespread applications in various fields, such as virtual reality, medical diagnosis, and robot interaction. The existing mainstream gesture-recognition methods are primarily divided into two categories: inertial-sensor-based and camera-vision-based methods. However, optical detection still has limitations such as reflection and occlusion. In this paper, we investigate static and dynamic gesture-recognition methods based on miniature inertial sensors. Hand-gesture data are obtained through a data glove and preprocessed using Butterworth low-pass filtering and normalization algorithms. Magnetometer correction is performed using ellipsoidal fitting methods. An auxiliary segmentation algorithm is employed to segment the gesture data, and a gesture dataset is constructed. For static gesture recognition, we focus on four machine learning algorithms, namely support vector machine (SVM), backpropagation neural network (BP), decision tree (DT), and random forest (RF). We evaluate the model prediction performance through cross-validation comparison. For dynamic gesture recognition, we investigate the recognition of 10 dynamic gestures using Hidden Markov Models (HMM) and Attention-Biased Mechanisms for Bidirectional Long- and Short-Term Memory Neural Network Models (Attention-BiLSTM). We analyze the differences in accuracy for complex dynamic gesture recognition with different feature datasets and compare them with the prediction results of the traditional long- and short-term memory neural network model (LSTM). Experimental results demonstrate that the random forest algorithm achieves the highest recognition accuracy and shortest recognition time for static gestures. Moreover, the addition of the attention mechanism significantly improves the recognition accuracy of the LSTM model for dynamic gestures, with a prediction accuracy of 98.3%, based on the original six-axis dataset.
手势识别已在虚拟现实、医学诊断和机器人交互等各个领域得到广泛应用。现有的主流手势识别方法主要分为两类：基于惯性传感器的方法和基于摄像头视觉的方法。然而，光学检测仍存在反射和遮挡等局限性。在本文中，我们研究基于微型惯性传感器的静态和动态手势识别方法。通过数据手套获取手势数据，并使用巴特沃斯低通滤波和归一化算法进行预处理。使用椭球拟合方法进行磁力计校正。采用辅助分割算法对手势数据进行分割，并构建手势数据集。对于静态手势识别，我们重点关注四种机器学习算法，即支持向量机（SVM）、反向传播神经网络（BP）、决策树（DT）和随机森林（RF）。我们通过交叉验证比较来评估模型预测性能。对于动态手势识别，我们研究使用隐马尔可夫模型（HMM）和双向长短期记忆神经网络模型的注意力偏差机制（Attention - BiLSTM）对10种动态手势进行识别。我们分析了不同特征数据集对复杂动态手势识别准确率的差异，并将其与传统长短期记忆神经网络模型（LSTM）的预测结果进行比较。实验结果表明，随机森林算法在静态手势识别中实现了最高的识别准确率和最短的识别时间。此外，注意力机制的加入显著提高了LSTM模型对动态手势的识别准确率，基于原始六轴数据集，预测准确率达到98.3%。
