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 because of its advantages of simple structure, easy access, visualization application, and harmlessness. This paper introduces a Smart Pressure e-Mat (SPeM) system based on piezoresistive material, Velostat, for human monitoring applications, including recognition of sleeping postures, sports, and yoga. After a subsystem scans the 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 13 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 in both applications, demonstrating the high accuracy and generalizability 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：随着对医疗保健、幼儿教育和健身的重视，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、可视化应用及无害性等优势而被广泛研究。本文介绍了一种基于压阻材料Velostat的智能压力电子垫系统，用于人体监测应用，包括睡姿、运动和瑜伽动作的识别。子系统扫描电子垫读数并处理信号后，生成压力图像流。采用深度神经网络对压力图像流进行拟合训练，以识别人体相应行为。研究以四种睡姿及受任天堂Switch健身环大冒险启发的13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实现高精度识别，证明了模型的高准确性与泛化能力。相较于其他基于压力传感器的系统，SPeM具备更灵活的应用场景和商业化前景，并具有可靠、鲁棒及可重复的特性。