Value Prediction for Spatiotemporal Gait Data Using Deep Learning
Ryan Cavanagh Jelena Trajkovic Wenlu Zhang I-Hung Khoo Vennila Krishnan
Human gait has been commonly used for the diagnosis and evaluation of medical
conditions and for monitoring the progress during treatment and rehabilitation.
The use of wearable sensors that capture pressure or motion has yielded
techniques that analyze the gait data to aid recovery, identify activity
performed, or identify individuals. Deep learning, usually employing
classification, has been successfully utilized in a variety of applications
such as computer vision, biomedical imaging analysis, and natural language
processing. We expand the application of deep learning to value prediction of
time-series of spatiotemporal gait data. Moreover, we explore several deep
learning architectures (Recurrent Neural Networks (RNN) and RNN combined with
Convolutional Neural Networks (CNN)) to make short- and long-distance
predictions using two different experimental setups. Our results show that
short-distance prediction has an RMSE as low as 0.060675, and long-distance
prediction RMSE as low as 0.106365. Additionally, the results show that the
proposed deep learning models are capable of predicting the entire trial when
trained and validated using the trials from the same participant. The proposed,
customized models, used with value prediction open possibilities for additional
applications, such as fall prediction, in-home progress monitoring, aiding of
exoskeleton movement, and authentication.