Pressure-sensitive mat (PSM) technology offers several advantages as a sensor modality for patient monitoring since it is non-contact and unobtrusive. However, as we move to deploy PSM for long-term continuous patient monitoring, we must consider and characterize their metrological properties that arise due to their electrical, mechanical or optical construction. We evaluate the dynamic metrological properties of rise time, creep, percent change in creep, drift, and repeatability for three different PSM technologies from three vendors, namely, S4 (Kinotex fiber-optics), Tekscan (resistive ink), and XSensor (capacitive). Both long-term (14.5 hrs) and repeated short-term experiments (1 min) were conducted using two anthropometric models exhibiting contact pressures representative of adult and neonatal patients. Long-term experiments were conducted to characterize rise time, creep, percent change in creep, and drift for each sensor. With both pressure models, the XSensor exhibited the fastest dynamic response in terms of rise and recovery times, while Tekscan exhibited the slowest responses. S4 and Tekscan present with an expected decrease in drift with application of the adult model, but XSensor shows the opposite trend. Short-term experiments were conducted to measure repeatability with four application-removal repetitions for 1 min each. The coefficient of variation (CoV) was computed for each sensor as a measure of repeatability. For both pressure models, the smaller CoV of XSensor implies greater repeatability and hence, greater reliability.

Additional Metadata
Keywords adult, coefficient of variation, continuous, creep, drift, metrological, neonatal, patient monitoring, pressure-sensitive mat, recovery, repeatability, rise time
Persistent URL dx.doi.org/10.1109/SAS.2017.7894054
Conference 12th IEEE Sensors Applications Symposium, SAS 2017
Citation
Nizami, S. (Shermeen), Cohen-Mcfarlane, M. (Madison), Green, J.R. (James R.), & Goubran, R. (2017). Comparing metrological properties of pressure-sensitive mats for continuous patient monitoring. In SAS 2017 - 2017 IEEE Sensors Applications Symposium, Proceedings. doi:10.1109/SAS.2017.7894054