With the emphasis on healthcare, early childhood education, and fitness, non-invasive measurement and recognition methods have received more attention. Pressure sensing has been extensively studied due to its advantages of simple structure, easy access, visualization application, and harmlessness. This paper introduces a smart pressure e-mat (SPeM) system based on a piezoresistive material Velostat for human monitoring applications, including sleeping postures, sports, and yoga recognition. After a subsystem scans e-mat readings and processes the signal, it generates a pressure image stream. Deep neural networks (DNNs) are used to fit and train the pressure image stream and recognize the corresponding human behavior. Four sleeping postures and five dynamic activities inspired by Nintendo Switch Ring Fit Adventure (RFA) are used as a preliminary validation of the proposed SPeM system. The SPeM system achieves high accuracies on both applications, which demonstrates the high accuracy and generalization ability of the models. Compared with other pressure sensor-based systems, SPeM possesses more flexible applications and commercial application prospects, with reliable, robust, and repeatable properties.

翻译：随着医疗健康、早期教育和健身领域的重视，非侵入式测量与识别方法受到更多关注。压力传感因其结构简单、易于获取、可视化应用及无伤害性等优势被广泛研究。本文介绍了一种基于压阻材料Velostat的智能压力电子垫（SPeM）系统，用于人体监测应用，包括睡眠姿势、运动及瑜伽识别。子系统扫描电子垫读数并处理信号后，生成压力图像流。采用深度神经网络（DNNs）对压力图像流进行拟合与训练，进而识别相应的人体行为。以任天堂Switch健身环大冒险（RFA）中的四种睡眠姿势和五种动态活动作为SPeM系统的初步验证。该系统在两类应用中均达到高精度，展示了模型的高准确率与泛化能力。相比其他基于压力传感器的系统，SPeM具有更灵活的应用场景和商业应用前景，并具备可靠、鲁棒及可重复的特性。