Nowadays,the use of detection technologies is essential to meet the needs of emerging applications that require the development of contactless and innovative structures.New applications using acoustic signals for monitoring vital signals in humans including breathing rate and heartbeat has attracted a lot of attention in mobile sensing.The focus of this thesis is related to the study and development of new methods based on smartphones with blended signals for the detection of vital signs in humans.The main research contents are summarized as follows:
　　Firstly,the thesis designs a ULCW method that identifies the high level of heartbeat and breathing rate in simultaneous multi-user detection.This method uses the constant time difference between the FMCW and CW signals transmitted by the speaker,controls the two microphones to effectively distinguish the two signals at different receiving frequencies,and can accurately identify multiple monitoring subjects.
　　Secondly,the thesis proposes an effective signal segmentation and peak extraction algorithm,and designs a multi-layer framework for noise cancellation and feature extraction to effectively mitigate environmental noise and intra-frequency interference.
　　Finally,the structure and algorithm applied in this thesis is evaluated in real indoor scenarios.The experimental results show that ULCW has better comparative advantages in both interference cancellation and detection accuracy.