Deep Learning for Ground Reaction Force Data Analysis Application to Wide-Area Floor Sensing

Deep learning methods are proposed to process and
fuse raw spatiotemporal ground reaction forces (GRF) to
accurately categorize gait pattern. These methods are based on
convolutional neural network and long short-term memory
networks architectures to learn spatiotemporal features,
automatically end-to-end from raw GRF sensor signals. In a case
study on Parkinson's disease (PD) data, spatiotemporal signals of
gait for PD patient and healthy subjects are processed and
classified, resulting an effective gait pattern classification with a
precision performance of 96%. Deep learning considerably
achieved better classification results, compared to the shallow
learning methods with the handcrafted features. This implies that
for the purpose of automatic decision-making, it is beneficial to
utilize deep learning methods to analyse GRF. This insight is
portable across a range of industrial tasks that involve complex
spatiotemporal GRF signals classification. The proposed models
are computationally efficient and able to achieve high
classification precision from a large set of GRF signals.