Toward Vulnerable Person Monitoring Using Intelligence From Ambient UHF RFID Tags

We demonstrate how the RSSI and phase data from ambient RFID tags for a sleeping person can be used to monitor 4 different modalities. Prone, supine and side sleeping positions are detected with 100% accuracy when the aforementioned parameters are used as features for posture detection using machine learning. The ability to detect incontinence for fluid volumes of 10-200 ml is demonstrated. Moreover, the hydration status of a person can also be detected for a range of urine salt concentration of 20 - 280 mmol/l. We also demonstrate the ability to detect breath rate (correct to ±1 breaths/min) across 4 different individuals, resting HR of 68 beats per min (bpm) across 2 individuals and high heart rates of up to 160 bpm across one individual. We quantify the utility of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tag select</i> command to improve the sampling rate from targeted RFID tags to 112.63 Hz even when in the presence of a large co-located population of tags and discuss the RSSI and phase data extraction and processing in detail. Limitations of our current work and future directions are also discussed.