Title | Super Low Resolution RF Powered Accelerometers for Alerting on Hospitalized Patient Bed Exits |
Publication Type | Conference Proceedings |
Year of Conference | 2019 |
Authors | Chesser, M., A. Jayatilaka, R. Visvanathan, C. Fumeaux, A. Sample, and D. C. Ranasinghe |
Conference Name | IEEE International Conference on Pervasive Computing and Communications (Percom 2019) |
Date Published | 2019 |
Abstract | Falls have serious consequences and are prevalent inacute hospitals and nursing homes caring for older people. Mostfalls occur in bedrooms and near the bed. Technological interven-tions to mitigate the risk of falling aim to automatically monitorbed-exit events and subsequently alert healthcare personnel toprovide timely supervisions. We observe that frequency-domaininformation related to patient activities exist predominantly invery low frequencies. Therefore, we recognise the potential toemploy a low resolution acceleration sensing modality in contrastto powering and sensing with a conventional MEMS (MicroElectro Mechanical System) accelerometer. Consequently, weinvestigate a batteryless sensing modality with low cost wirelesslypowered Radio Frequency Identification (RFID) technology withthe potential for convenient integration intoclothing, such ashospital gowns. We design and build apassiveaccelerometer-based RFID sensor embodiment—ID-Sensor—for our study. Thesensor design allows deriving ultra low resolution accelerationdata from the rate of change of unique RFID tag identifiersin accordance with the movement of a patient’s upper body.We investigate two convolutional neural network architecturesfor learning from rawRFID-onlydata streams and compareperformance with a traditional shallow classifier with engineeredfeatures. We evaluate performance with 23 hospitalized olderpatients. We demonstrate, for the first time and to the best ofknowledge, that: i) the low resolution acceleration data embeddedin the RF poweredID-Sensordata stream can provide a practica-ble method for activity recognition; and ii) highly discriminativefeatures can be efficiently learned from the raw RFID-only datastream using a fully convolutional network architecture. |
DOI | 10.1109/PERCOM.2019.8767398 |