Smart Pressure e-Mat for Human Sleeping Posture and Dynamic Activity
  Recognition
Liangqi Yuan Yuan Wei Jia Li
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.