  In this paper, we first present a single-input, multiple-output convolutional
neural network that can estimate both heart rate and respiration rate
simultaneously by exploiting the underlying link between heart rate and
respiration rate. The inputs to the neural network are the amplitude and phase
of channel state information collected by a pair of WiFi devices. Our
WiFi-based technique addresses privacy concerns and is adaptable to a variety
of settings. This system overall accuracy for the heart and respiration rate
estimation can reach 99.109% and 98.581%, respectively. Furthermore, we
developed and analyzed two deep learning-based neural network classification
algorithms for categorizing four types of sleep stages: wake, rapid eye
movement (REM) sleep, non-rapid eye movement (NREM) light sleep, and NREM deep
sleep. This system overall classification accuracy can reach 95.925%
