Towards 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 tag select 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.