In-bed posture classification using pressure sensor data and spiking neural network

Observing and evaluating sleeping positions is crucial in the treatment of cardiovascular episodes, pressure ulcers and respiratory diseases. Therefore, in-bed posture recognition systems become necessary at home as well as in hospitals. Many studies have shown that the use of gravity sensors in combination with the second generation of neural network (NN) architectures are extremely effective in assessing and classifying sleeping positions. However, the disadvantage of the second generation NN architecture is that it is quite energy-intensive. While the third NN generation - Spiking Neural Network (SNN) is projected to solve the power consumption problem while providing an equal performance or even better performance than the old ones. Surprisingly, none of the studies consider combining SNN in sleeping position classification based on pressure sensor assessment. In this paper, we propose the development of a converted CNN-to-SNN network for sleeping posture recognition algorithm supported by preprocessing technique. Experimental results confirm that our proposed method can achieve an accuracy of nearly 100% in 5-fold as well as 10-fold cross-validation and 90.56% in the Leave-One-Subject-Out (LOSO) cross-validation for 17 sleeping postures, which greatly surpasses the previous method performing the same task. Furthermore, the power consumption of our SNN model is 140 times lower than that of the published CNN model.