TY  - JOUR
PY  - 2020//
TI  - Prediction of freezing of gait in Parkinson's disease from foot plantar-pressure arrays using a convolutional neural network
JO  - Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
A1  - Shalin, Gaurav
A1  - Pardoel, Scott
A1  - Nantel, Julie
A1  - Lemaire, Edward D.
A1  - Kofman, Jonathan
SP  - 244
EP  - 247
VL  - 2020
IS  - 
N2  - Freezing of gait (FOG) is a sudden cessation of locomotion in advanced Parkinson's disease (PD). A FOG episode can lead to falls, decreased mobility, and decreased overall quality of life. Prediction of FOG episodes provides an opportunity for intervention and freeze prevention. A novel method of FOG prediction that uses foot plantar pressure data acquired during gait was developed and evaluated, with plantar pressure data treated as 2D images and classified using a convolutional neural network (CNN). Data from five people with PD and a history of FOG were collected during walking trials. FOG instances were identified and data preceding each freeze were labeled as Pre-FOG. Left and right foot FScan pressure frames were concatenated into a single 60x42 pressure array. Each frame was considered as an independent image and classified as Pre-FOG, FOG, or Non-FOG, using the CNN. From prediction models using different Pre-FOG durations, shorter Pre-FOG durations performed best, with Pre-FOG class sensitivity 94.3%, and specificity 95.1%. These results demonstrated that foot pressure distribution alone can be a good FOG predictor when treating each plantar pressure frame as a 2D image, and classifying the images using a CNN. Furthermore, the CNN eliminated the need for feature extraction and selection.Clinical Relevance- This research demonstrated that foot plantar pressure data can be used to predict freezing of gait occurrence, using a convolutional neural network deep learning technique. This had the added advantage of eliminating the need for feature extraction and selection.<p />  <p>Language: en</p>
LA  - en
SN  - 2375-7477
UR  - http://dx.doi.org/10.1109/EMBC44109.2020.9176382
ID  - ref1
ER  -