  Falls have serious consequences and are prevalent in acute hospitals and
nursing homes caring for older people. Most falls occur in bedrooms and near
the bed. Technological interventions to mitigate the risk of falling aim to
automatically monitor bed-exit events and subsequently alert healthcare
personnel to provide timely supervisions. We observe that frequency-domain
information related to patient activities exist predominantly in very low
frequencies. Therefore, we recognise the potential to employ a low resolution
acceleration sensing modality in contrast to powering and sensing with a
conventional MEMS (Micro Electro Mechanical System) accelerometer.
Consequently, we investigate a batteryless sensing modality with low cost
wirelessly powered Radio Frequency Identification (RFID) technology with the
potential for convenient integration into clothing, such as hospital gowns. We
design and build a passive accelerometer-based RFID sensor
embodiment---ID-Sensor---for our study. The sensor design allows deriving ultra
low resolution acceleration data from the rate of change of unique RFID tag
identifiers in accordance with the movement of a patient's upper body. We
investigate two convolutional neural network architectures for learning from
raw RFID-only data streams and compare performance with a traditional shallow
classifier with engineered features. We evaluate performance with 23
hospitalized older patients. We demonstrate, for the first time and to the best
of knowledge, that: i) the low resolution acceleration data embedded in the RF
powered ID-Sensor data stream can provide a practicable method for activity
recognition; and ii) highly discriminative features can be efficiently learned
from the raw RFID-only data stream using a fully convolutional network
architecture.
