Project Overview

Problem statement
As people age, a number of things change in their lives, including mobility and sleep patterns. Typically, with increasing age mobility declines and fall risk increases. Any acute illness can also affect mobility. Similarly, sleep is increasingly interrupted, such as going to the bathroom more frequently. Poor sleep can cause poor mobility the next day. With the development of sensors that can be deployed in older adults’ homes, there is an opportunity to identify mobility and sleep biomarkers that can predict changes in health status for increased fall risk. However, monitoring potentially thousands of aging older adults is a huge challenge and the IBM Data Analytics tools may provide a platform to be able to meet this challenge.

Research purpose
The SAM3 research team has between 9 and 12 months of continuous bed pressure and corresponding health data and fall history for each of over 20 older community-dwelling adults from a previous project. This big data set provides a unique opportunity to analyze typical longitudinal data as expected in a real deployment within IBM software tools to allow existing lab only implementations to potentially have commercial deployment.

Anticipated impacts
If the research team can convert mat big data into sleep and mobility knowledge that can be used to identify changes in health status, SAM3 will be one step closer to bringing a novel solution to market. Then mats with smart algorithms may be able to identify health changes in older Canadians before they get so severe that they require expensive hospitalizations. A goal will be the identification of methods to implement and scale the analysis algorithms, which is necessary for any sensor based solution to get to market. This would be a win for older Canadians and society as a whole.