  We present an IoT-based intelligent bed sensor system that collects and
analyses respiration-associated signals for unobtrusive monitoring in the home,
hospitals and care units. A contactless device is used, which contains four
load sensors mounted under the bed and one data processing unit (data logger).
Various machine learning methods are applied to the data streamed from the data
logger to detect the Respiratory Rate (RR). We have implemented Support Vector
Machine (SVM) and also Neural Network (NN)-based pattern recognition methods,
which are combined with either peak detection or Hilbert transform for robust
RR calculation. Experimental results show that our methods could effectively
extract RR using the data collected by contactless bed sensors. The proposed
methods are robust to outliers and noise, which are caused by body movements.
The monitoring system provides a flexible and scalable way for continuous and
remote monitoring of sleep, movement and weight using the embedded sensors.
