Gesture recognition becomes a thriving research area in modern human motion recognition systems. The intensification of demands on efficient interactive human–machine-interface systems, commercial objectives, and many other factors contributes to fuel this revival dynamics. Understanding human gestures becomes essential for prevention and health monitoring applications. In particular, analyzing hand gestures is of paramount importance in personalized healthcare-related applications to help practitioners providing more qualitative assessments of subject’s pathologies, such as Parkinson’s diseases. This work proposes a novel deep neural network approach to forecast future gestures from a given sequence of hand motion using a wearable capacitance sensor of an innovative gesture recognition hardware system. To do this, we use an attention-based recurrent neural network to capture the temporal features of hand motion to unveil the underlying pattern between the gesture and these sequences. While the attention layers capture patterns from the weights of the short term, the gated recurrent unit (GRU) neural network layer learns the inherent interdependency of long-term hand gesture temporal sequences. The efficiency of the proposed model is evaluated with respect to cutting-edge work in the field using several metrics. Note to Practitioners —In this article, the problem of human hand gesture recognition is analyzed using deep learning techniques. The proposed model uses input historical motion sequences collected from a wearable capacitance sensor to predict hand gestures. The model leverages the intrinsic correlation of motion sequences and extracts the salient part of the sequences by taking into consideration their temporal, complex, and nonlinear features. The approach studies the effect of different lengths of historical motion sequences in prediction outcomes. This allows for avoiding using cumbersome data collection, heavy data treatment, and high computational cost. The model performance is trained and assessed on real-world data by performing comparisons with alternative approaches, including well-known classifiers. The model yields very encouraging results and demonstrates that the proposed approach is quite competitive as it can reproduce typical activity trends for important channels. The present findings could help in the development of intelligent wearable devices for predicting hand gestures using a limited number of channels. This work could also help practitioners to provide a more qualitative appraisal of patients suffering from different pathologies such as Parkinson’s diseases to personalized healthcare-related applications and to develop wearable gesture recognition devices on a large scale.

中文翻译：

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基于注意的门控循环单元用于手势识别

手势识别已成为现代人体运动识别系统中蓬勃发展的研究领域。对高效交互式人机界面系统，商业目标以及许多其他因素的需求激增，助长了这种复兴动力。了解人的手势对于预防和健康监测应用至关重要。尤其是，分析手势在与医疗保健相关的个性化应用中至关重要，以帮助从业人员对受试者的病理学（例如帕金森氏病）进行更定性的评估。这项工作提出了一种新颖的深度神经网络方法，可以使用创新的手势识别硬件系统的可穿戴式电容传感器，根据给定的手部动作序列预测未来的手势。去做这个，我们使用基于注意力的递归神经网络来捕获手势的时间特征，以揭示手势与这些序列之间的潜在模式。当注意力层从短期权重中捕获模式时，门控循环单元（GRU）神经网络层将学习长期手势时间序列的内在相互依赖性。相对于该领域的前沿工作，使用几种指标评估了所提出模型的效率。门控循环单元（GRU）神经网络层了解长期手势时间序列的内在相互依赖性。相对于该领域的前沿工作，使用几种指标评估了所提出模型的效率。门控循环单元（GRU）神经网络层了解长期手势时间序列的内在相互依赖性。相对于该领域的前沿工作，使用几种指标评估了所提出模型的效率。执业者须知 —在本文中，使用深度学习技术分析了人类手势识别的问题。所提出的模型使用从可穿戴式电容传感器收集的输入历史运动序列来预测手势。该模型利用运动序列的内在相关性，并考虑到它们的时间，复杂和非线性特征，提取出序列的显着部分。该方法研究了不同长度的历史运动序列对预测结果的影响。这样可以避免使用繁琐的数据收集，繁重的数据处理和高计算成本。通过与包括著名分类器在内的其他方法进行比较，可以对模型性能进行训练并根据实际数据进行评估。该模型产生了令人鼓舞的结果，并证明了所提出的方法具有相当的竞争力，因为它可以重现重要渠道的典型活动趋势。本发现可以帮助开发用于使用有限数量的通道来预测手势的智能可穿戴设备。这项工作还可以帮助从业人员对患有不同疾病（如帕金森氏病）的患者进行更定性的评估，以个性化与医疗保健相关的应用，并大规模开发可穿戴手势识别设备。本发现可以帮助开发用于使用有限数量的通道来预测手势的智能可穿戴设备。这项工作还可以帮助从业人员对患有不同疾病（如帕金森氏病）的患者进行更定性的评估，以个性化与医疗保健相关的应用，并大规模开发可穿戴手势识别设备。本发现可以帮助开发用于使用有限数量的通道来预测手势的智能可穿戴设备。这项工作还可以帮助从业人员对患有不同疾病（如帕金森氏病）的患者进行更定性的评估，以个性化与医疗保健相关的应用，并大规模开发可穿戴手势识别设备。