Abstract:
Robotics system for rehabilitation of movement disorders and motion assistance are gaining increased intention. In this scenario estimation of ground
contact is an active area of research in robotics and healthcare. This thesis
work addresses the estimation and classification of right and left foot during
the healthy human gait based on the IMU sensor data of chest and lower
back. For this purpose we have collected an IMU data of 48 subjects by
using two smartphones at chest and lower back of the human body and one
smart watch at right ankle of the body. To show the robustness of our approach data was collected at six different surfaces (road tiles carpet grass
concrete and soil). The recorded data of lower back and chest sensor was
segmented into single steps on the basis of right ankle sensor data, then we
computed a total of 408 features from time frequency and wavelet domain
of each segmented step. For classification task we have trained two machine
learning classifiers SVM and RF with 10 fold cross validation method. We
performed classification experiments at individual surfaces, hard surfaces,
soft surfaces and all surfaces, highest accuracy was achieved at individual
surfaces with accuracy index of 98.88%. Further more, classification rate at
hard soft and all surface are 95.60%, 94.38% and 95.05% respectively. The
results shows that estimation of ground contact form normal human walk
at different surfaces can be performed with high accuracy using 6D data of
angular velocities and accelerations from chest and lower back location of the
body.
