# Smart Pressure e-Mat for Human Sleeping Posture and Dynamic Activity Recognition

arXiv: https://arxiv.org/abs/2305.11367 · HTML: https://mlsanity.com/paper/2305.11367/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 60 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [Beyond Short Snippets: Deep Networks for Video Classification](https://mlsanity.com/paper/1503.08909/) (2015)
- [Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition](https://mlsanity.com/paper/2304.07421/) (2023)
- [VGAN-Based Image Representation Learning for Privacy-Preserving Facial Expression Recognition](https://mlsanity.com/paper/1803.07100/) (2018)
- [Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application](https://mlsanity.com/paper/2301.04829/) (2023)

## Abstract (arXiv)

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.