UC Santa Cruz

Personalized health-care is trending and individuals tend to wear sensors in order to record their own health data. As a part of this trend, any redundancy in the data captured by wearable sensors must be exploited to reduce the number of devices one may wear. In this thesis, we work with a device which senses breathing and pulse through pressure tube and pulse oximetry, respectively. Extracting the dependency between these two measurements, we approximately predict the breathing rate by first reconstructing the breathing signal using the data coming from the finger-tip sensor, and then detecting the peaks in the reconstructed signal. For breathing signal reconstruction, two different techniques are used: (1) applying low- and high-pass filters on the pulse signal (2) training a neural network on a prepared dataset. Our experiments show that neural networks have a better performance comparing to filters in reconstructing the breathing signal, and consequently, predicting the breathing rate.