Movement analysis and falls management in Parkinson’s disease and stroke
The UK has an aging population with 50% of people over 80 are likely to experience a fall at least once a year [1]. Parkinson’s disease (PD) is a progressive neurological condition that can manifest as movement disorders including, but not limited to, tremor, bradykinesia and freezing of gait. These can lead to an increased risk of falling, which may result in injury and a decline in quality of life.
Technology has now developed to the point where it is feasible to deploy sensors for the long term monitoring of individuals in the home. We are interested in the development and deployment of wearable inertial sensing devices that are low power, user friendly, and comfortable. Our wearable sensors are especially well suited to gather data for movement analysis, enabling an objective insight into the condition. In collaboration with the University of Southampton, several studies have been conducted to inform on the requirements of a home-based wearable movement analysis system, and to develop tools to extract features from the data to provide insight into the condition. This would provide quantitative longitudinal data to aid clinical assessment of the disease in a natural environment, as opposed to periodic evaluation in a clinical setting. The gathered data would provide insight into the circumstances surrounding a fall or moments of instability and saving motions. A continuous assessment of the quality of motion during daily activities allows for changes in the quality of motion over time to be observed. This information can potentially be used to inform long term trends and the shorter term effects of medications. While presenting challenges, this approach lends itself well to personalised healthcare as it captures the individual’s expression of the disease in the home environment.
King, R. C., Villeneuve, E., White, R. J., Sherratt, R. S., Holderbaum, W. and Harwin, W. S. (2017) Application of data fusion techniques and technologies for wearable health monitoring. Medical Engineering & Physics, 42. pp. 1-12. doi: 10.1016/j.medengphy.2016.12.011
Stack, E., King, R., Janko, B., Burnett, M., Hammersley, N., Agarwal, V., Hannuna, S., Burrows, A. and Ashburn, A. (2016) Could in-home sensors surpass human observation of people with Parkinson’s at high risk of falling? An ethnographic study. BioMed research international, 2016. 3703745. doi: 10.1155/2016/3703745