TY - GEN
T1 - Real-time detection of apnea via signal processing of time-series properties of RFID-based smart garments
AU - Mongan, William M.
AU - Rasheed, Ilhaan
AU - Ved, Khyati
AU - Levitt, Ariana
AU - Anday, Endla
AU - Dandekar, Kapil
AU - Dion, Genevieve
AU - Kurzweg, Timothy
AU - Fontecchio, Adam
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/2/7
Y1 - 2017/2/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85015994064&partnerID=8YFLogxK
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U2 - 10.1109/SPMB.2016.7846871
DO - 10.1109/SPMB.2016.7846871
M3 - Conference contribution
AN - SCOPUS:85015994064
T3 - 2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Proceedings
BT - 2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2016
Y2 - 3 December 2016
ER -