  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
article 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%. Furthermore, 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.
