In-Bed Mobility Monitoring Using Pressure Sensors

Automated assessment of older adult health is needed due to an impending demographic shift. Mobility is considered an indicator of health and is more tangible than some other health measures. Currently, many papers aim to examine a discrete movement in detail, but none describe one system of algorithms aiming to automatically identify discrete and continuous patient positions and transitions. This paper aims to develop such a system of algorithms. Discrete and continuous data were generated by 32 subjects performing a series of position-transition movements, captured by fiber-optic pressure sensor mats. Algorithm set 1 part 1 aimed to identify and distinguish between three positional states by extracting seven occupancy and dispersion features, then using 1-D and 2-D support vector machine (SVM) and linear classifiers to classify the data. Set 1 part 2 aimed to identify and distinguish between state transitions by calculating percentage pressure difference on a per sensor and large area basis, then monitoring these signals for pressure relief. The second set aimed to examine all movements by extracting six geometric features from center of pressure signals, then using 1-D and 2-D SVM and linear classifiers to classify two subtly different transitions. All methods resulted in at least a 98% identification accuracy, and some methods were able to shed light on the subtleties of transitions. The results suggest that, with more development, the presented algorithmic methods could be implemented in hospital settings to assist with identification and assessment of elderly patient mobility.