Real time detection of apnea via signal processing of time series properties of RFID based smart garments
Signal processing of time series properties of Radio Frequency Identification (RFID) tags and novel work in textile knitted antennas for garment devices have enabled real time detection of motion based artifacts through unobtrusive, wireless, wearable devices. Capturing the Received Signal Strength Indicator (RSSI) as a time series signal, we classify whether the subject is breathing or not, estimate the rate at which the subject is breathing, and classify whether the tag is moving in a linear, non stretched fashion. We improve upon previous efforts to classify subject state from RSSI signals by eliminating the need to train the classifier with both breathing and non breathing sample data (which is biologically infeasible) To test our approach, we use a programmable breathing infant mannequin yielding accurate detection of cessation of respiratory activity within 5 seconds, and a maximum root mean square error of 7 per minute when computing the respiratory rate.
2016 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)