The aim of this study is to accurately distinguish Parkinson's disease (PD) participants from healthy controls using self-administered tests of gait and postural sway. Using consumer-grade smartphones with in-built accelerometers, we objectively measure and quantify key movement severity symptoms of Parkinson's disease. Specifically, we record tri-axial accelerations, and extract a range of different features based on the time and frequency-domain properties of the acceleration time series. The features quantify key characteristics of the acceleration time series, and enhance the underlying differences in the gait and postural sway accelerations between PD participants and controls. Using a random forest classifier, we demonstrate an average sensitivity of 98.5% and average specificity of 97.5% in discriminating PD participants from controls.
|Title of host publication||2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)|
|Number of pages||4|
|Publication status||Published - 2014|
|Event||2014 IEEE International Conference on Acoustics, Speech, and Signal Processing - Florence, Italy|
Duration: 4 May 2014 → 9 May 2014
|Conference||2014 IEEE International Conference on Acoustics, Speech, and Signal Processing|
|Abbreviated title||ICASSP 2014|
|Period||4/05/14 → 9/05/14|
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- Parkinson's disease
- Postural sway
- Random forest
- Tri-axial acceleration