In-Bed Mobility Monitoring Using Pressure Sensors

Abstract

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.

Noncontact Pressure-Based Sleep/Wake Discrimination

“…Other work elsewhere is examining the use of the UMBS for in-bed mobility monitoring during postural transitions [42]. …”

Analysis of Contact Pressure for Monitoring Body Fluid Distribution

“…Unobtrusive and non-invasive measurement technologies aim to supplement and potentially replace some of these invasive methods. Non-invasive remote monitor ing techniques have been developed for the collection of a range of physiological data including: breathing rate [28], [29], blood pressure [30], heart rate (HR) [29]- [31], weight [29], activities of daily living [29], mobility [32]- [34],and sleeping patterns [35]. We have also explored the use of sensor networks to instrument a smart apartment for monitor ing activities of daily living [36].…”

“…Specifically they have been used to measure balance and mobility [54], [35], [55], risk of pressure ulcers [35], [56], sleeping patterns [57], [32], bed occupancy [58] and sit-to-stand movement [59], [60], [55]. The fiber optic based PSMs have been implemented clinically to monitor long term sleeping patterns and health [32], [35], [61]- [66]. Sleep disordered breathing is another area of research in our lab were the PSM are able to monitor breathing patterns during sleep [64], [65], [67], resulting in an effective algorithm for the detection of central sleep apnea.…”

“…These PSM have been used for a variety of investigations prior to this thesis (e.g. [32], [35], [67] ). There were also many fibre-optic PSMs available in the lab, allowing for full coverage of a human body.…”

“…These PSMs have been used in a variety of studies to identify and/or monitor various physiological signals including weight changes (e.g. [10], [12], [14], mobility assessments [62], [83], and sleep analysis [35], [39], [84]). As (from abdomen to ankles) was used for data analysis for both measurement systems.…”