Аннотация:
In modern clinics, quantitative analyses
are used to diagnose and treat
gait pathology. Accordingly, foretasting
walking kinematics and kinetics
of people benefits to better understand
gait patterns and construct assisting
devices for rehabilitation. This
capstone research proposes a deep
learning algorithm (LSTM) for forecasting
the walking kinematic of the
knee in the Cartesian coordinate system.
The hyper-parameter optimization
using Gaussian Process method
was used to predict walking kinematics
of knee with an accuracy of 97%.