Smart Pressure e-Mat for Human Sleeping Posture and Dynamic Activity Recognition
This work addresses healthcare, early childhood education, and fitness needs by providing a flexible and robust pressure-sensing solution, though it is incremental as it builds on existing pressure sensor methods.
The paper tackles the problem of non-invasive human monitoring by introducing a Smart Pressure e-Mat system that uses piezoresistive material and deep neural networks to recognize sleeping postures and dynamic activities, achieving high accuracies in preliminary validation.
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.