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.

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This paper presents a new approach for analyzing center of pressure (COP) progression using pressure data collected from a pressure-sensitive array placed under the bed mattress. Pressure data were collected from a young female participant who was healthy and an older 78 year old female participant who had a history of falls. Information relevant to movement direction, time, path trajectory, magnitude and frequency was presented in three dimensional plots and color differentiated displays. When tested on data collected from an older participant who experienced a fall, this method of analyzing COP was able to illustrate distinct differences in bed exit patterns used pre and post fall episode. This analysis approach shows the potential to detect changes in bed exit patterns indicative of a critical health event. Future applications include home monitoring to assist with early intervention in the event of bed mobility decline.

The increase in the older adult population and the benefits of independent living are leading to the introduction of new approaches for monitoring the health and well-being of seniors living in their own homes. Technologies that can monitor an older person's daily activities and detect changes in functionality have the potential to act as early warning systems so that help can be provided before a serious health event occurs. Home monitoring can contribute to the creation of more supportive environments for aging at home. This thesis uses a pressure-sensitive mat that is placed between a bed frame and the mattress. The pressure data can reveal important clinical information about the bed occupant. This thesis deals with the analysis of bed exit characteristics in terms of centre of pressure trajectory and its dynamic behaviour. The algorithms were tested on data collected from bed occupants to demonstrate the various clinical features. A graphical user interface (GUI) was designed to assist health care providers in interpreting and comparing these clinical features. This work will be used to monitor frail older adults in their own homes to assist with early detection of mobility decline.ii