AI Article Synopsis
- Freezing of gait (FOG) is a significant mobility issue in Parkinson's disease patients, leading to increased fall risks and difficulties in daily activities.
- A new smartphone-based system was developed to detect FOG symptoms using sensors in various body positions like the waist and pockets.
- Machine learning, specifically the AdaBoost.M1 classifier, demonstrated high sensitivity in identifying freezing episodes, achieving the best results when the phone was placed at the waist (86% sensitivity).
Article Abstract
Freezing of gait (FOG) is a common motor impairment to suffer an inability to walk, experienced by Parkinson's disease (PD) patients. FOG interferes with daily activities and increases fall risk, which can cause severe health problems. We propose a novel smartphone-based system to detect FOG symptoms in an unconstrained way. The feasibility of single device to sense gait characteristic was tested on the various body positions such as ankle, trouser pocket, waist and chest pocket. Using measured data from accelerometer and gyroscope in the smartphone, machine learning algorithm was applied to classify freezing episodes from normal walking. The performance of AdaBoost.M1 classifier showed the best sensitivity of 86% at the waist, 84% and 81% in the trouser pocket and at the ankle respectively, which is comparable to the results of previous studies.
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| http://dx.doi.org/10.1109/EMBC.2015.7319209 | DOI Listing |
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